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Dr Daniel Abásolo

Senior Lecturer in Biomedical Engineering

Email:
Phone: Work: 01483 68 2971
Room no: 17 DK 05

Further information

Research Interests

  • Biomedical Signal Processing.
  • Non-linear Analysis.
  • Electroencephalogram.
  • Magnetoencephalogram.
  • Alzheimer’s Disease.
  • Epilepsy.
  • Sleep apnoea.
  • Intracranial Pressure Signals.

Publications

Highlights

  • Gómez C, Olde Dubbelink KT, Stam CJ, Abásolo D, Berendse HW, Hornero R. (2011) 'Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson's Disease Patients.'. Springer Ann Biomed Eng, United States: 39 (12), pp. 2935-2944.
    doi: 10.1007/s10439-011-0416-0
    Full text is available at: http://epubs.surrey.ac.uk/55451/

    Abstract

    The aim of the present study was to analyze resting-state brain activity in patients with Parkinson's disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel-Ziv complexity (LZC), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects' recordings. We found significant group differences (p-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of LZC results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed. LZC results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of LZC to detect an abnormal type of dynamics associated with PD.

  • Escudero J, Hornero R, Abásolo D, Fernández A. (2011) 'Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation.'. Springer Ann Biomed Eng, United States: 39 (8), pp. 2274-2286.
    doi: 10.1007/s10439-011-0312-7
    Full text is available at: http://epubs.surrey.ac.uk/39612/

    Abstract

    The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

  • Escudero J, Hornero R, Abásolo D. (2010) 'Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity'. ELSEVIER SCI LTD MED ENG PHYS, 32 (10), pp. 1137-1144.
    doi: 10.1016/j.medengphy.2010.08.005
    Full text is available at: http://epubs.surrey.ac.uk/39610/
  • Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. (2009) 'Analysis of MEG Background Activity in Alzheimer's Disease Using Nonlinear Methods and ANFIS'. SPRINGER ANN BIOMED ENG, 37 (3), pp. 586-594.
    doi: 10.1007/s10439-008-9633-6
    Full text is available at: http://epubs.surrey.ac.uk/55453/
  • Abásolo D, Hornero R, Escudero J, Espino P. (2008) 'A study on the possible usefulness of detrended fluctuation analysis of the electroencephalogram background activity in Alzheimer's disease'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE T BIO-MED ENG, 55 (9), pp. 2171-2179.
    doi: 10.1109/TBME.2008.923145
    Full text is available at: http://epubs.surrey.ac.uk/39598/

Journal articles

  • Santamarta D, Abásolo D, Martínez-Madrigal M, Hornero R. (2012) 'Characterisation of the intracranial pressure waveform during infusion studies by means of central tendency measure.'. Acta Neurochir (Wien), Austria: 154 (9), pp. 1595-1602.
    doi: 10.1007/s00701-012-1441-y
  • Fernández A, Serra A, Fernández A, Zuluaga P, Abásolo D, Gómez C, Hornero R, Méndez MA. (2012) 'Brain oscillatory complexity across the life span'. Clinical Neurophysiology,
    doi: 10.1016/j.clinph.2012.04.025
    Full text is available at: http://epubs.surrey.ac.uk/713640/

    Abstract

    Objective: Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. Method: This study examines changes in resting-state magnetoencephalogram (MEG) complexity - quantified with the Lempel-Ziv complexity (LZC) algorithm - due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years. Results: A significant quadratic (curvilinear) relationship (p < 0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life. Once that peak was crossed, complexity values slowly decreased until late senescence. Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p < 0.05). Conclusions: These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm. Significance: Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age. © 2012 International Federation of Clinical Neurophysiology.

  • Santamarta D, Fernández J, Abásolo D, Hornero R. (2012) 'Pulse amplitude and lempel-ziv complexity of the cerebrospinal fluid pressure signal'. Acta Neurochirurgica, Supplementum, (114), pp. 23-27.
    doi: 10.1007/978-3-7091-0956-4-5
    Full text is available at: http://epubs.surrey.ac.uk/713710/

    Abstract

    Background: The complexity of the intracranial pressure (ICP) signal decreases with intracranial hypertension in children with acute brain injury as well as during infusion studies in adults with hydrocephalus. In this study we have analysed the pressure signal obtained in the lumbar subarachnoid space during infusion testing. The pulse amplitude rises when the ICP is increased by additional external volume. Our objective was to determine the relative influence of the pressure range and pulse amplitude on the loss of complexity observed during infusion-related intracranial hypertension. Materials and Methods: The Lempel-Ziv (LZ) complexity of the cerebrospinal fluid pressure (CSFP) signal was analysed in 52 infusion studies performed in patients with normal pressure hydrocephalus (median age 71 years, IQR: 60-78). Four sequences during the baseline, infusion, steady plateau and recovery periods of each infusion study were selected. The mean values of the CSFP (mCSFP), pulse amplitude and LZ complexity in every sequence were measured. Correlations between LZ complexity and CSFP parameters were explored. Results: Significant inverse correlations were found among LZ complexity, pulse amplitude and mCSFP during all periods of infusion testing, except at baseline. Partial correlation analysis controlling the effect of mCSFP emphasised the relationship between pulse amplitude and LZ complexity. When pulse amplitude is held constant the partial correlation between LZ complexity and mCSFP is not significant. Conclusions: The pulse amplitude of the CSFP signal seems to be a major determinant of the waveform complexity. © 2012 Springer-Verlag/Wien.

  • Gómez C, Olde Dubbelink KT, Stam CJ, Abásolo D, Berendse HW, Hornero R. (2011) 'Complexity Analysis of Resting-State MEG Activity in Early-Stage Parkinson's Disease Patients.'. Springer Ann Biomed Eng, United States: 39 (12), pp. 2935-2944.
    doi: 10.1007/s10439-011-0416-0
    Full text is available at: http://epubs.surrey.ac.uk/55451/

    Abstract

    The aim of the present study was to analyze resting-state brain activity in patients with Parkinson's disease (PD), a degenerative disorder of the nervous system. Magnetoencephalography (MEG) signals were recorded with a 151-channel whole-head radial gradiometer MEG system in 18 early-stage untreated PD patients and 20 age-matched control subjects. Artifact-free epochs of 4 s (1250 samples) were analyzed with Lempel-Ziv complexity (LZC), applying two- and three-symbol sequence conversion methods. The results showed that MEG signals from PD patients are less complex than control subjects' recordings. We found significant group differences (p-values <0.01) for the 10 major cortical areas analyzed (e.g., bilateral frontal, central, temporal, parietal, and occipital regions). In addition, using receiver-operating characteristic curves with a leave-one-out cross-validation procedure, a classification accuracy of 81.58% was obtained. In order to investigate the best combination of LZC results for classification purposes, a forward stepwise linear discriminant analysis with leave-one out cross-validation was employed. LZC results (three-symbol sequence conversion) from right parietal and temporal brain regions were automatically selected by the model. With this procedure, an accuracy of 84.21% (77.78% sensitivity, 90.0% specificity) was achieved. Our findings demonstrate the usefulness of LZC to detect an abnormal type of dynamics associated with PD.

  • Fernández A, Ríos-Lago M, Abásolo D, Hornero R, Alvarez-Linera J, Paul N, Maestú F, Ortiz T. (2011) 'The correlation between white-matter microstructure and the complexity of spontaneous brain activity: a difussion tensor imaging-MEG study.'. Elsevier Neuroimage, United States: 57 (4), pp. 1300-1307.
    doi: 10.1016/j.neuroimage.2011.05.079
    Full text is available at: http://epubs.surrey.ac.uk/125672/

    Abstract

    The advent of new signal processing methods, such as non-linear analysis techniques, represents a new perspective which adds further value to brain signals' analysis. Particularly, Lempel-Ziv's Complexity (LZC) has proven to be useful in exploring the complexity of the brain electromagnetic activity. However, an important problem is the lack of knowledge about the physiological determinants of these measures. Although a correlation between complexity and connectivity has been proposed, this hypothesis was never tested in vivo. Thus, the correlation between the microstructure of the anatomic connectivity and the functional complexity of the brain needs to be inspected. In this study we analyzed the correlation between LZC and fractional anisotropy (FA), a scalar quantity derived from diffusion tensors that is particularly useful as an estimate of the functional integrity of myelinated axonal fibers, in a group of sixteen healthy adults (all female, mean age 65.56±6.06 years, intervals 58-82). Our results showed a positive correlation between FA and LZC scores in regions including clusters in the splenium of the corpus callosum, cingulum, parahipocampal regions and the sagittal stratum. This study supports the notion of a positive correlation between the functional complexity of the brain and the microstructure of its anatomical connectivity. Our investigation proved that a combination of neuroanatomical and neurophysiological techniques may shed some light on the underlying physiological determinants of brain's oscillations.

  • Escudero J, Hornero R, Abásolo D, Fernández A. (2011) 'Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation.'. Springer Ann Biomed Eng, United States: 39 (8), pp. 2274-2286.
    doi: 10.1007/s10439-011-0312-7
    Full text is available at: http://epubs.surrey.ac.uk/39612/

    Abstract

    The magnetoencephalogram (MEG) is contaminated with undesired signals, which are called artifacts. Some of the most important ones are the cardiac and the ocular artifacts (CA and OA, respectively), and the power line noise (PLN). Blind source separation (BSS) has been used to reduce the influence of the artifacts in the data. There is a plethora of BSS-based artifact removal approaches, but few comparative analyses. In this study, MEG background activity from 26 subjects was processed with five widespread BSS (AMUSE, SOBI, JADE, extended Infomax, and FastICA) and one constrained BSS (cBSS) techniques. Then, the ability of several combinations of BSS algorithm, epoch length, and artifact detection metric to automatically reduce the CA, OA, and PLN were quantified with objective criteria. The results pinpointed to cBSS as a very suitable approach to remove the CA. Additionally, a combination of AMUSE or SOBI and artifact detection metrics based on entropy or power criteria decreased the OA. Finally, the PLN was reduced by means of a spectral metric. These findings confirm the utility of BSS to help in the artifact removal for MEG background activity.

  • Escudero J, Sanei S, Jarchi D, Abásolo D, Hornero R. (2011) 'Regional Coherence Evaluation in Mild Cognitive Impairment and Alzheimer’s Disease Based on Adaptively Extracted Magnetoencephalogram Rhythms'. Institute of Physics Physiological Measurement, 32 (8) Article number 1163
    doi: 10.1088/0967-3334/32/8/011
    Full text is available at: http://epubs.surrey.ac.uk/591160/

    Abstract

    This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology.

  • Santamarta D, Abásolo D, Hornero R. (2011) 'Reply to the comment by Carmelo Anile on the paper "Complexity analysis of the cerebrospinal fluid pulse waveform during infusion studies"'. Child's Nervous System, , pp. 1-2.
    doi: 10.1007/s00381-011-1629-0
    Full text is available at: http://epubs.surrey.ac.uk/713712/
  • Escudero J, Hornero R, Abásolo D. (2010) 'Consistency of the blind source separation computed with five common algorithms for magnetoencephalogram background activity'. ELSEVIER SCI LTD MED ENG PHYS, 32 (10), pp. 1137-1144.
    doi: 10.1016/j.medengphy.2010.08.005
    Full text is available at: http://epubs.surrey.ac.uk/39610/
  • Santamarta D, Hornero R, Abásolo D, Martínez-Madrigal M, Fernández J, García-Cosamalón J. (2010) 'Complexity analysis of the cerebrospinal fluid pulse waveform during infusion studies.'. Childs Nerv Syst, Germany: 26 (12), pp. 1683-1689.
    doi: 10.1007/s00381-010-1244-5
    Full text is available at: http://epubs.surrey.ac.uk/713711/

    Abstract

    PURPOSE: Nonlinear dynamics has enhanced the diagnostic abilities of some physiological signals. Recent studies have shown that the complexity of the intracranial pressure waveform decreases during periods of intracranial hypertension in paediatric patients with acute brain injury. We wanted to assess changes in the complexity of the cerebrospinal fluid (CSF) pressure signal over the large range covered during the study of CSF circulation with infusion studies. METHODS: We performed 37 infusion studies in patients with hydrocephalus of various types and origin (median age 71 years; interquartile range 60-77 years). After 5 min of baseline measurement, infusion was started at a rate of 1.5 ml/min until a plateau was reached. Once the infusion finished, CSF pressure was recorded until it returned to baseline. We analysed CSF pressure signals using the Lempel-Ziv (LZ) complexity measure. To characterise more accurately the behaviour of LZ complexity, the study was segmented into four periods: basal, early infusion, plateau and recovery. RESULTS: The LZ complexity of the CSF pressure decreased in the plateau of the infusion study compared to the basal complexity (p=0.0018). This indicates loss of complexity of the CSF pulse waveform with intracranial hypertension. We also noted that the level of complexity begins to increase when the infusion is interrupted and CSF pressure drops towards the initial values. CONCLUSIONS: The LZ complexity decreases when CSF pressure reaches the range of intracranial hypertension during infusion studies. This finding provides further evidence of a phenomenon of decomplexification in the pulsatile component of the pressure signal during intracranial hypertension.

  • Alcaraz R, Abásolo D, Hornero R, Rieta JJ. (2010) 'Optimal parameters study for sample entropy-based atrial fibrillation organization analysis'. ELSEVIER IRELAND LTD COMPUT METH PROG BIO, 99 (1), pp. 124-132.
    doi: 10.1016/j.cmpb.2010.02.009
    Full text is available at: http://epubs.surrey.ac.uk/124492/

    Abstract

    Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series, such as heart rate, hormonal data, etc. However, no guidelines exist for the selection of that values in other cases. Therefore, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In the present work, a thorough analysis on the optimal values for m, r and N is presented within the context of atrial fibrillation (AF) organization estimation, computed from surface electrocardiogram recordings. Recently, the evaluation of AF organization through SampEn, has revealed clinically useful information that could be used for a better treatment of this arrhythmia. The present study analyzed optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF. As a result, interesting recommendations about the selection of m, r and N, together with the relationship between N and the sampling rate (f(s)) were obtained. More precisely, (i) the proportion between N and f(s) should be higher than 1s and f(s) >= 256 Hz, (ii) overlapping between adjacent N-length windows does not improve AF organization estimation with respect to the analysis of non-overlapping windows, and (iii) values of m and r maximizing successful classification for the analyzed AF databases should be considered within a range wider than the proposed in the literature for heart rate analysis, i.e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

  • Escudero J, Hornero R, Abásolo D, Fernández A. (2009) 'Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease'. ELSEVIER SCI LTD MED ENG PHYS, 31 (7), pp. 872-879.
    doi: 10.1016/j.medengphy.2009.04.003
    Full text is available at: http://epubs.surrey.ac.uk/39627/
  • Gómez C, Mediavilla A, Hornero R, Abásolo D, Fernández A. (2009) 'Use of the Higuchi's fractal dimension for the analysis of MEG recordings from Alzheimer's disease patients'. ELSEVIER SCI LTD MED ENG PHYS, 31 (3), pp. 306-313.
    doi: 10.1016/j.medengphy.2008.06.010
    Full text is available at: http://epubs.surrey.ac.uk/55452/
  • Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. (2009) 'Analysis of MEG Background Activity in Alzheimer's Disease Using Nonlinear Methods and ANFIS'. SPRINGER ANN BIOMED ENG, 37 (3), pp. 586-594.
    doi: 10.1007/s10439-008-9633-6
    Full text is available at: http://epubs.surrey.ac.uk/55453/
  • Escudero J, Hornero R, Abásolo D. (2009) 'Interpretation of the auto-mutual information rate of decrease in the context of biomedical signal analysis. Application to electroencephalogram recordings'. IOP PUBLISHING LTD PHYSIOL MEAS, 30 (2), pp. 187-199.
    doi: 10.1088/0967-3334/30/2/006
    Full text is available at: http://epubs.surrey.ac.uk/39611/
  • Hornero R, Abásolo D, Escudero J, Gómez C. (2009) 'Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease'. ROYAL SOC PHILOS T R SOC A, 367 (1887), pp. 317-336.
    doi: 10.1098/rsta.2008.0197
    Full text is available at: http://epubs.surrey.ac.uk/713540/
  • García M, Sánchez CI, López MI, Abásolo D, Hornero R. (2009) 'Neural network based detection of hard exudates in retinal images'. ELSEVIER IRELAND LTD COMPUT METH PROG BIO, 93 (1), pp. 9-19.
    doi: 10.1016/j.cmpb.2008.07.006
    Full text is available at: http://epubs.surrey.ac.uk/124488/
  • Álvarez D, Hornero R, Abásolo D, Del Campo F, Zamarrón C, López M. (2009) 'Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome'. Physiological Measurement, 30 (9), pp. 967-982.
    doi: 10.1088/0967-3334/30/9/008
    Full text is available at: http://epubs.surrey.ac.uk/58444/
  • Abásolo D, Hornero R, Espino P. (2009) 'APPROXIMATE ENTROPY OF EEG BACKGROUND ACTIVITY IN ALZHEIMER'S DISEASE PATIENTS'. AUTOSOFT PRESS INTELL AUTOM SOFT CO, 15 (4), pp. 591-603.
    Full text is available at: http://epubs.surrey.ac.uk/39602/

    Abstract

    Non-linear analysis of the electroencephalogram (EEG) background activity can help to obtain a better understanding of abnormal dynamics in the brain. The aim of this study was to analyze the regularity of the EEG time series of Alzheimer's disease (AD) Patients to test the hypothesis that the irregularity of the AD patients' EEG is lower than that of age-matched controls. We recorded the EEG from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the Approximate Entropy (ApEn). ApEn is a non-linear method that can be used to quantify the irregularity of a time series. Larger values correspond to more irregularity. We evaluated different values for input parameters m and r to estimate ApEn and concluded that m=1 and r=0.25 times the SD of the time series were the optimum choices. With these parameters, ApEn was significantly lower in the AD patients at the P3, P41 O1 and O2 (p < 0.01) electrodes. The decreased irregularity found in the EEG of AD patients in the parietal and occipital regions leads us to think that regularity analysis of the EEG with ApEn could be a useful tool to increase our insight into brain dysfunction in Alzheimer's disease.

  • Abásolo D, Escudero J, Hornero R, Gómez C, Espino P. (2008) 'Approximate entropy and auto mutual information analysis of the electroencephalogram in Alzheimer's disease patients'. SPRINGER HEIDELBERG MED BIOL ENG COMPUT, 46 (10), pp. 1019-1028.
    doi: 10.1007/s11517-008-0392-1
    Full text is available at: http://epubs.surrey.ac.uk/39601/
  • Abásolo D, Hornero R, Escudero J, Espino P. (2008) 'A study on the possible usefulness of detrended fluctuation analysis of the electroencephalogram background activity in Alzheimer's disease'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE T BIO-MED ENG, 55 (9), pp. 2171-2179.
    doi: 10.1109/TBME.2008.923145
    Full text is available at: http://epubs.surrey.ac.uk/39598/
  • Escudero J, Hornero R, Poza J, Abásolo D, Fernández A. (2008) 'Assessment of classification improvement in patients with Alzheimer's disease based on magnetoencephalogram blind source separation'. ELSEVIER SCIENCE BV ARTIF INTELL MED, 43 (1), pp. 75-85.
    doi: 10.1016/j.artmed.2008.01.001
    Full text is available at: http://epubs.surrey.ac.uk/39626/
  • Poza J, Hornero R, Abásolo D, Fernández A, Mayo A. (2008) 'Evaluation of spectral ratio measures from spontaneous MEG recordings in patients with Alzheimer's disease'. ELSEVIER IRELAND LTD COMPUT METH PROG BIO, 90 (2), pp. 137-147.
    doi: 10.1016/j.cmpb.2007.12.004
    Full text is available at: http://epubs.surrey.ac.uk/118060/
  • Sánchez CI, Hornero R, López MI, Aboy M, Poza J, Abásolo D. (2008) 'A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis'. ELSEVIER SCI LTD MED ENG PHYS, 30 (3), pp. 350-357.
    doi: 10.1016/j.medengphy.2007.04.010
    Full text is available at: http://epubs.surrey.ac.uk/713578/
  • Poza J, Hornero R, Abásolo D, Fernández A, García M. (2007) 'Extraction of spectral based measures from MEG background oscillations in Alzheimer's disease.'. Med Eng Phys, England: 29 (10), pp. 1073-1083.
    doi: 10.1016/j.medengphy.2006.11.006
    Full text is available at: http://epubs.surrey.ac.uk/118061/

    Abstract

    In this study, we explored the ability of several spectral based measures to summarize the information of the power spectral density (PSD) function from spontaneous magnetoencephalographic (MEG) activity in Alzheimer's disease (AD). The MEGs of 20 AD patients and 21 elderly controls were recorded with eyes closed at rest during 5 min from 148 channels. Five spectral parameters were estimated from PSD: mean frequency (MF), individual alpha frequency (IAF), transition frequency (TF), 95% spectral edge frequency (SEF95) and spectral entropy (SE). To reduce the dimensionality of the problem, we applied a principal component analysis. According to our results, MF was the best discriminating index between both groups (85.00% sensitivity, 85.71% specificity) indicating a shift to the left of the power spectrum in AD. A significant MEG slowing was also observed using both IAF (p < 0.001) and TF (p < 0.01). The lowest classification statistics (65% sensitivity, 66.67% specificity) were obtained with SEF95. However, these results were also significant (p < 0.05). This fact points out that there is a variation in the spectral content at high frequencies of AD patients and controls. Finally, a significant decrease of irregularity in the AD group was observed with SE, with results close to those obtained with MF (90.00% sensitivity, 76.19% specificity). In conclusion, a complete description of PSD can help to increase our insight into brain dysfunction in AD and to extract spectral patterns specific to the disease.

  • Escudero J, Abásolo D, Hornero R, Espino P, López M. (2007) 'Reply to "Comment on 'Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy'"'. IOP PUBLISHING LTD PHYSIOL MEAS, 28 (12), pp. L3-L7.
    doi: 10.1088/0967-3334/28/12/L02
    Full text is available at: http://epubs.surrey.ac.uk/714847/

    Abstract

    We appreciate the interest of Dr Tang in our article. Certainly, our previous results should be taken with caution due to the small database size. Nevertheless, it must be noted that this limitation was clearly recognized in our article. Furthermore, our hypothesis is completely justified from the current state of the art in the analysis of electroencephalogram (EEG) signals from Alzheimer's disease (AD) patients. We evaluated whether the multiscale entropy (MSE) analysis of the EEG background activity was useful to distinguish AD patients and controls. We do believe that further discussions about risk factors or related clinicophysiological protein aspects are clearly beyond the scope of our article. For the sake of completeness, we now detail some results that complement our previous analysis. They suggest that the MSE analysis can provide relevant information about the dynamics of AD patients' EEG data. Thus, we must reaffirm our conclusions, although we again acknowledge that further studies are needed.

  • Escudero J, Hornero R, Abásolo D, Fernández A, López-Coronado M. (2007) 'Artifact removal in magnetoencephalogram background activity with independent component analysis.'. IEEE IEEE Trans Biomed Eng, United States: 54 (11), pp. 1965-1973.
    doi: 10.1109/TBME.2007.894968
    Full text is available at: http://epubs.surrey.ac.uk/39614/

    Abstract

    The aim of this study was to assess whether independent component analysis (ICA) could be valuable to remove power line noise, cardiac, and ocular artifacts from magnetoencephalogram (MEG) background activity. The MEGs were recorded from 11 subjects with a 148-channel whole-head magnetometer. We used a statistical criterion to estimate the number of independent components. Then, a robust ICA algorithm decomposed the MEG epochs and several methods were applied to detect those artifacts. The whole process had been previously tested on synthetic data. We found that the line noise components could be easily detected by their frequency spectrum. In addition, the ocular artifacts could be identified by their frequency characteristics and scalp topography. Moreover, the cardiac artifact was better recognized by its skewness value than by its kurtosis one. Finally, the MEG signals were compared before and after artifact rejection to evaluate our method.

  • Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. (2007) 'Analysis of the magnetoencephalogram background activity in Alzheimer's disease patients with auto-mutual information.'. Comput Methods Programs Biomed, Ireland: 87 (3), pp. 239-247.
    doi: 10.1016/j.cmpb.2007.07.001
    Full text is available at: http://epubs.surrey.ac.uk/56943/

    Abstract

    The aim of the present study was to analyse the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD), one of the most frequent disorders among elderly population. For this pilot study, we recorded the MEGs with a 148-channel whole-head magnetometer in 20 patients with probable AD and 21 age-matched control subjects. Artefact-free epochs of 3392 samples were analysed with auto-mutual information (AMI). Average AMI decline rates were lower for the AD patients' recordings than for control subjects' ones. Statistically significant differences were found using a Student's t-test (p<0.01) in 144 channels. Mean AMI values were analysed with a receiver operating characteristic curve. Sensitivity, specificity and accuracy values of 75%, 90.5% and 82.9% were obtained. Our results show that AMI estimations of the magnetic brain activity are different in both groups, hence indicating an abnormal type of dynamics associated with AD. This study suggests that AMI might help medical doctors in the diagnosis of the disease.

  • Cuesta D, Varela M, Miró P, Galdós P, Abásolo D, Hornero R, Aboy M. (2007) 'Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy.'. Med Biol Eng Comput, United States: 45 (7), pp. 671-678.
    doi: 10.1007/s11517-007-0200-3
    Full text is available at: http://epubs.surrey.ac.uk/726365/

    Abstract

    Body temperature is a classical diagnostic tool for a number of diseases. However, it is usually employed as a plain binary classification function (febrile or not febrile), and therefore its diagnostic power has not been fully developed. In this paper, we describe how body temperature regularity can be used for diagnosis. Our proposed methodology is based on obtaining accurate long-term temperature recordings at high sampling frequencies and analyzing the temperature signal using a regularity metric (approximate entropy). In this study, we assessed our methodology using temperature registers acquired from patients with multiple organ failure admitted to an intensive care unit. Our results indicate there is a correlation between the patient's condition and the regularity of the body temperature. This finding enabled us to design a classifier for two outcomes (survival or death) and test it on a dataset including 36 subjects. The classifier achieved an accuracy of 72%.

  • Hornero R, Aboy M, Abásolo D. (2007) 'Analysis of intracranial pressure during acute intracranial hypertension using Lempel-Ziv complexity: further evidence.'. Med Biol Eng Comput, United States: 45 (6), pp. 617-620.
    doi: 10.1007/s11517-007-0194-x
  • Hornero R, Alvarez D, Abásolo D, del Campo F, Zamarrón C. (2007) 'Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome.'. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC IEEE Trans Biomed Eng, United States: 54 (1), pp. 107-113.
    doi: 10.1109/TBME.2006.883821
    Full text is available at: http://epubs.surrey.ac.uk/58445/

    Abstract

    Approximate entropy (ApEn) is a family of statistics introduced as a quantification of regularity in time series without any a priori knowledge about the system generating them. The aim of this preliminary study was to assess whether a time series analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry by means of ApEn could yield essential information on the diagnosis of obstructive sleep apnea (OSA) syndrome. We analyzed SaO2 signals from 187 subjects: 111 with a positive diagnosis of OSA and 76 with a negative diagnosis of OSA. We divided our data in a training set (44 patients with OSA Positive and 30 patients with OSA Negative) and a test set (67 patients with OSA Positive and 46 patients with OSA Negative). The training set was used for algorithm development and optimum threshold selection. Results showed that recurrence of apnea events in patients with OSA determined a significant increase in ApEn values. This method was assessed prospectively using the test dataset, where we obtained 82.09% sensitivity and 86.96% specificity. We conclude that ApEn analysis of SaO2 from pulse oximetric recording could be useful in the study of OSA.

  • Escudero J, Abásolo D, Hornero R, Espino P, López M. (2006) 'Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy.'. Physiol Meas, England: 27 (11), pp. 1091-1106.
    doi: 10.1088/0967-3334/27/11/004
    Full text is available at: http://epubs.surrey.ac.uk/39613/

    Abstract

    The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer's disease (AD) patients using multiscale entropy (MSE). MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant differences between both subject groups at electrodes F3, F7, Fp1, Fp2, T5, T6, P3, P4, O1 and O2 (p-value < 0.01, Student's t-test). These findings indicate that the EEG complexity analysis performed on deeper time scales by MSE may be a useful tool in order to increase our knowledge of AD.

  • Gómez C, Hornero R, Abásolo D, Fernández A, López M. (2006) 'Complexity analysis of the magnetoencephalogram background activity in Alzheimer's disease patients.'. Elsevier Med Eng Phys, England: 28 (9), pp. 851-859.
    doi: 10.1016/j.medengphy.2006.01.003
    Full text is available at: http://epubs.surrey.ac.uk/55454/

    Abstract

    The aim of the present study was to analyse the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using the Lempel-Ziv (LZ) complexity. This non-linear method measures the complexity of finite sequences and is related to the number of distinct substrings and the rate of their occurrence along the sequence. The MEGs were recorded with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging) in 21 patients with AD and in 21 age-matched control subjects. Artefact-free epochs were selected for complexity analysis. Results showed that MEG signals from AD patients had lower complexity than control subjects' MEGs and the differences were statistically significant (p<0.01). In order to reduce the dimension of the LZ complexity results, a principal components analysis (PCA) was applied, and only the first principal component was retained. The first component score from PCA was graphically analysed using a box plot and a receiver-operating characteristic (ROC) curve. A specificity of 85.71%, a sensitivity of 80.95% and an area under the ROC curve of 0.9002 were obtained. These preliminary results suggest that cognitive dysfunction in AD is associated with a decreased LZ complexity in the MEG signals.

  • Aboy M, Hornero R, Abásolo D, Alvarez D. (2006) 'Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis.'. IEEE Trans Biomed Eng, United States: 53 (11), pp. 2282-2288.
    doi: 10.1109/TBME.2006.883696
  • Zamarrón C, Hornero R, del Campo F, Abásolo D, Alvarez D. (2006) 'Heart rate regularity analysis obtained from pulse oximetric recordings in the diagnosis of obstructive sleep apnea.'. Sleep Breath, Germany: 10 (2), pp. 83-89.
    doi: 10.1007/s11325-005-0049-3
  • Abásolo D, Hornero R, Gómez C, García M, López M. (2006) 'Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.'. Med Eng Phys, England: 28 (4), pp. 315-322.
    doi: 10.1016/j.medengphy.2005.07.004
    Full text is available at: http://epubs.surrey.ac.uk/39599/

    Abstract

    In this study we have investigated the electroencephalogram (EEG) background activity in patients with Alzheimer's disease (AD) using non-linear analysis methods. We calculated the Lempel-Ziv (LZ) complexity - applying two different sequence conversion methods - and the central tendency measure (CTM) of the EEG in 11 AD patients and 11 age-matched control subjects. CTM quantifies the degree of variability, while LZ complexity reflects the arising rate of new patterns along with the EEG time series. We did not find significant differences between AD patients and control subjects' EEGs with CTM. On the other hand, AD patients had significantly lower LZ complexity values (p<0.01) at electrodes P3 and O1 with a two-symbol sequence conversion, and P3, P4, O1 and T5 using three symbols. Our results show a decreased complexity of EEG patterns in AD patients. In addition, we obtained 90.9% sensitivity and 72.7% specificity at O1, and 72.7% sensitivity and 90.9% specificity at P3 and P4. These findings suggest that LZ complexity may contribute to increase the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.

  • Álvarez D, Hornero R, Abásolo D, del Campo F, Zamarrón C. (2006) 'Nonlinear characteristics of blood oxygen saturation from nocturnal oximetry for obstructive sleep apnoea detection.'. Institute of Physics Physiol Meas, England: 27 (4), pp. 399-412.
    doi: 10.1088/0967-3334/27/4/006
    Full text is available at: http://epubs.surrey.ac.uk/58443/

    Abstract

    Nocturnal oximetry is an attractive option for the diagnosis of obstructive sleep apnoea (OSA) syndrome because of its simplicity and low cost compared to polysomnography (PSG). The present study assesses nonlinear analysis of blood oxygen saturation (SaO(2)) from nocturnal oximetry as a diagnostic test to discriminate between OSA positive and OSA negative patients. A sample of 187 referred outpatients, clinically suspected of having OSA, was studied using nocturnal oximetry performed simultaneously with complete PSG. A positive OSA diagnosis was found for 111 cases, while the remaining 76 cases were classified as OSA negative. The following oximetric indices were obtained: cumulative time spent below a saturation of 90% (CT90), oxygen desaturation indices of 4% (ODI4), 3% (ODI3) and 2% (ODI2) and the delta index (Delta index). SaO(2) records were subsequently processed applying two nonlinear methods: central tendency measure (CTM) and Lempel-Ziv (LZ) complexity. Significant differences (p < 0.01) were found between OSA positive and OSA negative patients. Using CTM we obtained a sensitivity of 90.1% and a specificity of 82.9%, while with LZ the sensitivity was 86.5% and the specificity was 77.6%. CTM and LZ accuracies were higher than those provided by ODI4, ODI3, ODI2 and CT90. The results suggest that nonlinear analysis of SaO(2) signals from nocturnal oximetry could yield useful information in OSA diagnosis.

  • Abásolo D, Hornero R, Espino P, Alvarez D, Poza J. (2006) 'Entropy analysis of the EEG background activity in Alzheimer's disease patients.'. Physiol Meas, England: 27 (3), pp. 241-253.
    doi: 10.1088/0967-3334/27/3/003
    Full text is available at: http://epubs.surrey.ac.uk/39603/

    Abstract

    Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although a definite diagnosis is only possible by necropsy, a differential diagnosis with other types of dementia and with major depression should be attempted. The aim of this study was to analyse the electroencephalogram (EEG) background activity of AD patients to test the hypothesis that the regularity of the AD patients' EEG is higher than that of age-matched controls. We recorded the EEG from 19 scalp electrodes in 11 AD patients and 11 age-matched controls. Two different methods were used to estimate the regularity of the EEG background activity: spectral entropy (SpecEn) and sample entropy (SampEn). We did not find significant differences between AD patients and control subjects' EEGs with SpecEn. On the other hand, AD patients had significantly lower SampEn values than control subjects (p < 0.01) at electrodes P3, P4, O1 and O2. Our results show an increase of EEG regularity in AD patients. These findings suggest that nonlinear analysis of the EEG with SampEn could yield essential information and may contribute to increasing the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.

  • Hornero R, Abásolo D, Jimeno N, Sánchez CI, Poza J, Aboy M. (2006) 'Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects.'. IEEE Trans Biomed Eng, United States: 53 (2), pp. 210-218.
    doi: 10.1109/TBME.2005.862547
  • del Campo F, Hornero R, Zamarrón C, Abásolo D, Álvarez D. (2006) 'Oxygen saturation regularity analysis in the diagnosis of obstructive sleep apnea'. Artificial Intelligence in Medicine, 37 (2), pp. 111-118.
    Full text is available at: http://epubs.surrey.ac.uk/713641/
  • Fernández A, Gómez C, Hornero R, Abásolo D, López M. (2006) 'Complexity analysis of the magnetoencephalogram background activity in Alzheimer disease patients'. HOGREFE & HUBER PUBLISHERS J PSYCHOPHYSIOL, 20 (3), pp. 229-229.
  • Hornero R, Aboy M, Abásolo D, McNames J, Wakeland W, Goldstein B. (2006) 'Complex analysis of intracranial hypertension using approximate entropy.'. Crit Care Med, United States: 34 (1), pp. 87-95.
  • Hornero R, Aboy M, Abásolo D, McNames J, Goldstein B. (2005) 'Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension.'. IEEE Trans Biomed Eng, United States: 52 (10), pp. 1671-1680.
    doi: 10.1109/TBME.2005.855722
  • Abásolo D, Hornero R, Espino P, Poza J, Sánchez CI, de la Rosa R. (2005) 'Analysis of regularity in the EEG background activity of Alzheimer's disease patients with Approximate Entropy.'. Clin Neurophysiol, Netherlands: 116 (8), pp. 1826-1834.
    doi: 10.1016/j.clinph.2005.04.001
    Full text is available at: http://epubs.surrey.ac.uk/39600/

    Abstract

    The aim of this study was to analyse the regularity of the EEG background activity of Alzheimer's disease (AD) patients to test the hypothesis that the irregularity of the AD patients' EEG is lower than that of age-matched controls.

Conference papers

  • Al-Amri M, Abasolo D, Ghoussayni S, Ewins D. (2012) 'Development of an augmented treadmill for the rehabilitation of children with cerebral palsy: pilot perspectives from young healthy adult users'. Proceedings 9th International Conference on Disability, Virtual Reality & Associated Technologies, Laval, France: International Conference on Disability, Virtual Reality & Associated Technologies, pp. 77-85.
    Full text is available at: http://epubs.surrey.ac.uk/744109/

    Abstract

    A Real-time Treadmill Speed Control Algorithm (RTSCA) has been developed for gait rehabilitation of children with cerebral palsy (CP). The objective of the work described in this paper was to investigate the feasibility of the RTSCA prior to use by children with CP. Thirteen healthy subjects aged between 19 and 25 were recruited to walk on the treadmill using conventional speed buttons without the virtual reality (VR) environment, and the RTSCA with and without VR. The participants were asked to undertake three treadmill tests and to complete a questionnaire to provide feedback on the control of the treadmill. The descriptive results show that for 10 participants changing walking speed from stationary when using the RTSCA was similar or more comfortable to using conventional treadmill speed control buttons. For those who found it less comfortable the core issue was insufficient time to practise with the system. All the participants were satisfied with the safety and the performance of the RTSCA when incorporated into the VR scenario. A Wilcoxon test was conducted to examine whether there was a significant difference between walking speeds on the treadmill when using the conventional speed buttons and the RTSCA. The results showed that participants walked at significantly higher speeds when using the RTSCA. This may suggest that they walked more naturally or confidently on the treadmill when using the RTSCA as compared to the use of conventional treadmill speed control buttons.

  • Abásolo D, Muñoz D, Espino P. (2012) 'Kullback-leibler entropy analysis of the electroencephalogram background activity in alzheimer's disease patients'. Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, , pp. 43-46.
    doi: 10.2316/P.2012.764-072
    Full text is available at: http://epubs.surrey.ac.uk/719258/

    Abstract

    Alzheimer's disease (AD) is the most frequent form of dementia in western countries. An early detection would be beneficial, but currently diagnostic accuracy is relatively poor. In this study, differences in information content between cortical areas in 12 AD patients and 11 control subjects were assessed with Kullback-Leibler (KL) entropy. KL entropy measures the degree of similarity between two probability distributions. EEGs were recorded from 19 scalp electrodes and KL entropy values of the EEGs in both groups were estimated for the local, distant and interhemispheric electrodes. KL entropy values were lower in AD patients than in age-matched control subjects, with significant effects for diagnosis and brain region (p < 0.05, two-way ANOVA). No significant interaction for diagnosis X region was found (p = 0.7671). Additionally a one-way ANOVA showed that KL entropy values were significantly lower in AD patients (p < 0.05) for the distant electrodes on the right hemisphere. These results suggest that KL entropy highlights information content changes in the EEG due to AD. However, further studies are needed to address the possible usefulness of KL entropy in the characterisation and early detection of AD.

  • Abásolo D, Jelinek HF. (2012) 'Lempel-Ziv complexity dynamics in early detection of cardiac autonomic neuropathy in diabetes'. Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, , pp. 16-20.
    doi: 10.2316/P.2012.764-036
  • Abásolo D, Alcaraz R, Rieta JJ, Hornero R. (2011) 'Lempel-Ziv complexity analysis for the evaluation of atrial fibrillation organization'. Proceedings of the 8th IASTED International Conference on Biomedical Engineering, Biomed 2011, Innsbruck, Austria: 8th IASTED International Conference on Biomedical Engineering, pp. 30-35.
    doi: 10.2316/P.2011.723-074
    Full text is available at: http://epubs.surrey.ac.uk/713546/

    Abstract

    The Lempel-Ziv (LZ) complexity is a non-linear time series analysis metric that reflects the arising rate of new patterns along with the sequence. Thus, it captures its temporal sequence and, quite conveniently, it can be computed with short data segments. In the present work, a detailed analysis on LZ complexity is presented within the context of atrial fibrillation (AF) organization estimation. As the analysed time series depend on the original sampling rate (fs), we evaluated the relationship between LZ complexity and fs. Furthermore, different implementations of LZ complexity were tested. Our results show the usefulness of LZ complexity to estimate AF organization and suggest that the signals from a terminating paroxysmal AF group are more organized (i.e. less complex) than those from the non-terminating paroxysmal AF group. However, the diagnostic accuracy was not as high as that obtained with sample entropy (SampEn), another non-linear metric, with the same database in a previous study (92% vs. 96%). Nevertheless, the LZ complexity analysis of AF organization with sampling frequencies higher than 2048 Hz, or even its combination with SampEn or other non-linear metrics, might improve the prediction of spontaneous AF termination.

  • Alcaraz R, Abásolo D, Hornero R, Rieta JJ. (2010) 'Optimized assessment of atrial fibrillation organization through suitable parameters of Sample Entropy.'. Conf Proc IEEE Eng Med Biol Soc, United States: 1, pp. 118-121.
    doi: 10.1109/IEMBS.2010.5627169
    Full text is available at: http://epubs.surrey.ac.uk/713576/

    Abstract

    Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒ(s)) should be higher than one second and ƒ(s) ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data.

  • Alcaraz R, Abásolo D, Hornero R, Rieta JJ, Alcaraz R. (2010) 'Study of Sample Entropy ideal computational parameters in the estimation of atrial fibrillation organization from the ECG'. Computers in Cardiology, 37, pp. 1027-1030.
    Full text is available at: http://epubs.surrey.ac.uk/713577/

    Abstract

    Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (f ) should be higher than one second and f ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis.

  • Gómez C, Abásolo D, Poza J, Fernández A, Hornero R. (2010) 'MEG analysis in Alzheimer's disease computing approximate entropy for different frequency bands.'. Conf Proc IEEE Eng Med Biol Soc, United States: 1, pp. 2379-2382.
    doi: 10.1109/IEMBS.2010.5627236
    Full text is available at: http://epubs.surrey.ac.uk/713804/

    Abstract

    The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a regularity measure: approximate entropy (ApEn). This measure was computed for a broad band (0.5-40 Hz) as well as typical frequency bands (delta, theta, alpha, beta and gamma). Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 elderly control subjects. Our results showed that AD patients' MEGs were more regular than controls' recordings at all frequency bands, with the exception of beta. Additionally, there were statistically significant differences (p 〈 0.01, Student's t-test) at the broad and delta bands. Using receiver operating characteristic curves, the highest accuracy (83.33%) was reached at delta band. These results suggest the usefulness of ApEn to gain a better understanding of dynamical processes underlying the MEG recording.

  • Gómez C, Hornero R, Abásolo D, Fernández A, Poza J. (2009) 'Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods.'. IEEE Conf Proc IEEE Eng Med Biol Soc, United States: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE 2009, pp. 3485-3488.
    doi: 10.1109/IEMBS.2009.5334569
    Full text is available at: http://epubs.surrey.ac.uk/713549/

    Abstract

    Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this research work is to study the magnetoencephalogram (MEG) background activity in AD patients using two scaling analysis methods: detrended fluctuation analysis (DFA) and backward detrended moving average (BDMA). Both measures have been designed to quantify correlations in noisy and non-stationary signals. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 control subjects. Both DFA and BDMA exhibited two scaling regions with different slopes. Significant differences between both groups were found in the second region of DFA and in the first region of BDMA (p < 0.01, Student's t-test). Using receiver operating characteristic curves, accuracies of 83.33% with DFA and of 80% with BDMA were reached. Our findings show the usefulness of these scaling analysis methods to increase our insight into AD.

  • Abásolo D, Hornero R, Gómez C, Escudero J, Espino P. (2009) 'Electroencephalogram Background Activity Characterization with Detrended Moving Average in Alzheimer's Disease Patients'. IEEE WISP 2009: 6TH IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING, PROCEEDINGS, Budapest, HUNGARY: 6th IEEE International Symposium on Intelligent Signal Processing, pp. 211-215.
    doi: 10.1109/WISP.2009.5286531
    Full text is available at: http://epubs.surrey.ac.uk/713544/

    Abstract

    The aim of this study was to analyse the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with the Detrended Moving Average (DMA) method, a new approach to quantify correlation properties in non-stationary signals with underlying trends. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels, with a clear bend when their corresponding slopes (alpha(1) and alpha(2)) were distinctly different. With the exception of electrode T4, the alpha(1) values were lower in control subjects than in AD patients, with significant differences at TS, P3, P4 and O1 (p < 0.01, Student's t-test). On the other hand, alpha(2) values were higher in control subjects than in AD patients, with significant differences only at F4. Furthermore, we evaluated the ability of alpha(2) to discriminate AD patients from control subjects at these electrodes using ROC plots. We obtained a maximum accuracy of 81.82% at O1 with alpha(1) and at F4 with alpha(2). These findings suggest that the scaling behaviour of the EEG is sensitive to AD and that the DMA method could help to increase our insight into brain dysfunction in AD.

  • Escudero J, Hornero R, Abásolo D, López M. (2008) 'On the application of the auto mutual information rate of decrease to biomedical signals.'. Conf Proc IEEE Eng Med Biol Soc, United States: 2008, pp. 2137-2140.
    doi: 10.1109/IEMBS.2008.4649616
    Full text is available at: http://epubs.surrey.ac.uk/714845/

    Abstract

    The auto mutual information function (AMIF) evaluates the signal predictability by assessing linear and non-linear dependencies between two measurements taken from a single time series. Furthermore, the AMIF rate of decrease (AMIFRD) is correlated with signal entropy. This metric has been used to analyze biomedical data, including cardiac and brain activity recordings. Hence, the AMIFRD can be a relevant parameter in the context of biomedical signal analysis. Thus, in this pilot study, we have analyzed a synthetic sequence (a Lorenz system) and real biosignals (electroencephalograms recorded with eyes open and closed) with the AMIFRD. We aimed at illustrating the application of this parameter to biomedical time series. Our results show that the AMIFRD can detect changes in the non-linear dynamics of a sequence and that it can distinguish different physiological conditions.

  • Gómez C, Hornero R, Mediavilla A, Fernández A, Abásolo D. (2008) 'Nonlinear forecasting measurement of magnetoencephalogram recordings from Alzheimer's disease patients.'. Conf Proc IEEE Eng Med Biol Soc, United States: 2008, pp. 2153-2156.
    doi: 10.1109/IEMBS.2008.4649620
    Full text is available at: http://epubs.surrey.ac.uk/713805/

    Abstract

    The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a nonlinear forecasting measure. It is a nonparametric method to quantify the predictability of time series. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 elderly control subjects. Stationary epochs of 5 seconds (848 points, sample frequency of 169.55 Hz) were selected. Our results showed that AD patients' MEGs were more predictable than controls' recordings. Additionally, an accuracy of 76.7% (80.0% sensitivity; 73.3% specificity) was reached using a receiver operating characteristic curve. These preliminary results suggest the usefulness of nonlinear forecasting to gain a better understanding of dynamical processes underlying the MEG recording.

  • de la Rosa R, Alonso A, de la Rosa S, Abásolo D. (2008) 'Myo-Pong: a neuromuscular game for the UVa-Neuromuscular Training System Platform'. IEEE 2008 VIRTUAL REHABILITATION, Vancouver, CANADA: Virtual Rehabilitation 2008 Conference, pp. 61-61.
  • Abásolo D, Hornero R, Espino P, Escudero J, Gómez C. (2007) 'Electroencephalogram background activity characterization with approximate entropy and auto mutual information in Alzheimer's disease patients.'. Conf Proc IEEE Eng Med Biol Soc, United States: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 2007, pp. 6192-6195.
    doi: 10.1109/IEMBS.2007.4353769
    Full text is available at: http://epubs.surrey.ac.uk/713543/

    Abstract

    The aim of this study was to analyze the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with two non-linear methods: Approximate Entropy (ApEn) and Auto Mutual Information (AMI). ApEn quantifies the regularity in data, while AMI detects linear and non-linear dependencies in time series. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. ApEn was significantly lower in AD patients at electrodes O1, O2, P3 and P4 (p<0.01). The AMI of the AD patients decreased significantly more slowly with time delays than the AMI of normal controls at electrodes T5, T6, O1, O2, P3 and P4 (p<0.01). Furthermore, we observed a strong correlation between the results obtained with both non-linear methods, suggesting that the AMI rate of decrease can be used to estimate the regularity in time series. The decreased irregularity found in AD patients suggests that EEG analysis with ApEn and AMI could help to increase our insight into brain dysfunction in AD.

  • Poza J, Hornero R, Abásolo D, Fernández A, Escudero J. (2007) 'Analysis of spontaneous MEG activity in patients with Alzheimer's disease using spectral entropies.'. Conf Proc IEEE Eng Med Biol Soc, United States: 2007, pp. 6180-6183.
    doi: 10.1109/IEMBS.2007.4353766
    Full text is available at: http://epubs.surrey.ac.uk/713575/

    Abstract

    The aim of this study was to explore the ability of several spectral entropies to discriminate between spontaneous magnetoencephalographic (MEG) oscillations from 20 Alzheimer's disease (AD) patients and 21 controls. Hence, the relative spectral power (RSP) in classical frequency bands was calculated from the averaged power spectral density. Given the fact that the RSP can be viewed as a probability distribution function, the Shannon spectral entropy, Tsallis spectral entropy, generalized escort-Tsallis spectral entropy and Rényi spectral entropy were calculated from the RSP. Significant differences for each parameter were assessed with Mann-Whitney U test, whereas classification performance was studied using binary logistic regression. Results revealed an increase in the RSP of control subjects at beta and gamma bands, while AD patients showed an increase in the RSP values at delta and theta bands. Entropies obtained statistically significant lower values for AD patients than for controls. This issue suggests a significant decrease in irregularity of AD patients' MEG activity.

  • Abásolo D, James CJ, Hornero R. (2007) 'Non-linear analysis of intracranial electroencephalogram recordings with approximate entropy and Lempel-Ziv complexity for epileptic seizure detection.'. IEEE Conf Proc IEEE Eng Med Biol Soc, United States: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 2007, pp. 1953-1956.
    doi: 10.1109/IEMBS.2007.4352700
    Full text is available at: http://epubs.surrey.ac.uk/713547/

    Abstract

    Epileptic seizures are generated by an abnormal synchronization of neurons unforeseeable for the patients. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable focal epilepsy with two non-linear methods, Approximate Entropy (ApEn) and Lempel-Ziv (LZ) complexity. ApEn and LZ complexity quantify the regularity and complexity of a time series, respectively, and are well suited to the analysis of non-stationary biomedical signals of short length. Our results show an increase in ApEn and LZ complexity values during seizures at the focal electrodes. These changes could also be seen at some extra focal electrodes. After the seizure ends, the values of both non-linear metrics return to values lower than those before the seizure. Moreover, we quantified the changes in LZ complexity, showing the complexity increase during the seizure and its notable decrease after its end. Our results suggest that these techniques are useful to detect changes due to epileptic seizures in the EEG.

  • Escudero J, Hornero R, Abásolo D, Fernández A, Poza J. (2007) 'Magnetoencephalogram blind source separation and component selection procedure to improve the diagnosis of Alzheimer's disease patients.'. Conf Proc IEEE Eng Med Biol Soc, United States: 2007, pp. 5437-5440.
    doi: 10.1109/IEMBS.2007.4353575
    Full text is available at: http://epubs.surrey.ac.uk/714844/

    Abstract

    The aim of this study was to improve the diagnosis of Alzheimer's disease (AD) patients applying a blind source separation (BSS) and component selection procedure to their magnetoencephalogram (MEG) recordings. MEGs from 18 AD patients and 18 control subjects were decomposed with the algorithm for multiple unknown signals extraction. MEG channels and components were characterized by their mean frequency, spectral entropy, approximate entropy, and Lempel-Ziv complexity. Using Student's t-test, the components which accounted for the most significant differences between groups were selected. Then, these relevant components were used to partially reconstruct the MEG channels. By means of a linear discriminant analysis, we found that the BSS-preprocessed MEGs classified the subjects with an accuracy of 80.6%, whereas 72.2% accuracy was obtained without the BSS and component selection procedure.

  • James CJ, Abásolo D, Gupta D. (2007) 'Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.'. IEEE Conf Proc IEEE Eng Med Biol Soc, United States: Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 2007, pp. 5473-5476.
    doi: 10.1109/IEMBS.2007.4353584
    Full text is available at: http://epubs.surrey.ac.uk/713548/

    Abstract

    In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.

  • Gómez C, Hornero R, Abásolo D, Fernández A, Escudero J. (2007) 'Analysis of MEG recordings from Alzheimer's disease patients with sample and multiscale entropies.'. Conf Proc IEEE Eng Med Biol Soc, United States: 2007, pp. 6184-6187.
    doi: 10.1109/IEMBS.2007.4353767
    Full text is available at: http://epubs.surrey.ac.uk/713801/

    Abstract

    Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this study is to analyze the magnetoencephalogram (MEG) background activity in AD patients using sample entropy (SampEn) and multiscale entropy (MSE). The former quantifies the signal regularity, while the latter is a complexity measure. These concepts, irregularity and complexity, are linked although the relationship is not straightforward. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 20 patients with probable AD and 21 control subjects. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in some MEG channels with both methods (p<0.01, Student's t-test with Bonferroni's correction). Using receiver operating characteristic curves, accuracies of 75.6% with SampEn and of 87.8% with MSE were reached. Our findings show the usefulness of these entropy measures to increase our insight into AD.

  • Gómez C, Hornero R, Fernández A, Abasolo D, Escudero J, López M. (2006) 'Magnetoencephalogram background activity analysis in Alzheimer's disease patients using auto mutual information.'. Conf Proc IEEE Eng Med Biol Soc, United States: 1, pp. 6181-6184.
    doi: 10.1109/IEMBS.2006.260317
    Full text is available at: http://epubs.surrey.ac.uk/713803/

    Abstract

    The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using the auto mutual information (AMI). Applied to time series, AMI provides a measure of future points predictability from past points. Five minutes of recording were acquired with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D neuroimaging) in 12 patients with probable AD and 12 elderly control subjects. Artifact-free epochs of 20 seconds (3392 points, sample frequency of 169.6 Hz) were selected for our study. Our results showed that the absolute values of the averaged decline rate of AMI were lower in AD patients than in control subjects for all channels. In addition, there were statistically significant differences (p<0.01, Student's t-test) in most channels. These preliminary results suggest that neuronal dysfunction in AD is associated with differences in the dynamical processes underlying the MEG recording.

  • Escudero J, Abásolo D, Hornero R, Espino P, López M. (2006) 'Electroencephalograms multiscale entropy analysis of Alzheimer's disease patients'. IET Conference Publications, Glasgow, UK: IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing (520), pp. 3-3.
    doi: 10.1049/cp:20060348
    Full text is available at: http://epubs.surrey.ac.uk/713545/
  • Abásolo D, Hornero R, Escudero J, Gómez C, García M, López M. (2006) 'Approximate entropy and mutual information analysis of the electroencephalogram in Alzheimer's disease patients'. IET IET Conference Publications, Glasgow: IET 3rd International Conference MEDSIP 2006. Advances in Medical, Signal and Information Processing (520), pp. 2-2.
    doi: 10.1049/cp:20060347
    Full text is available at: http://epubs.surrey.ac.uk/713541/
  • Escudero J, Hornero R, Abásolo D, Poza J, Fernández A, López M. (2006) 'Rejection of artifact sources in magnetoencephalogram background activity using independent component analysis'. IEEE 2006 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-15, New York, NY: 28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, pp. 273-276.
    Full text is available at: http://epubs.surrey.ac.uk/714846/

    Abstract

    The aim of this pilot study was to assess the usefulness of independent component analysis (ICA) to detect cardiac artifacts and power line interferences in magnetoencephalogram (MEG) recordings. We recorded MEG signals from six subjects with, a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging). Epochs of 50 s with power line noise, cardiac, and ocular artifacts were selected for analysis. We applied a statistical criterion to determine the number of sources, and a robust ICA algorithm to decompose the MEG epochs. Skewness, kurtosis, and a spectral metric were used to mark the studied artifacts. We found that the power fine interference could be easily detected by its frequency characteristics. Moreover, skewness outperformed kurtosis when identifying the cardiac artifact.

  • Hornero R, Alvarez D, Abásolo D, Gómez C, del Campo F, Zamarrón C. (2005) 'Approximate entropy from overnight pulse oximetry for the obstructive sleep apnea syndrome'. IEEE 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-7, Shanghai, PEOPLES R CHINA: 27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, pp. 6157-6160.
  • Gómez C, Hornero R, Fernández A, Abásolo D, López M. (2005) 'Decreased Lempel-Ziv complexity in Alzheimer's disease patients' magnetoencephalograms'. IEEE 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-7, Shanghai, PEOPLES R CHINA: 27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, pp. 4514-4517.
    Full text is available at: http://epubs.surrey.ac.uk/713802/

    Abstract

    The aim of the present research is to study the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using the Lempel-Ziv (LZ) complexity. We recorded the MEG with a 148-channel whole-head magnetometer (MAGNES 2500 WH, 4D Neuroimaging) in 10 patients with probable AD and 10 age-matched control subjects, during five minutes. Artefact-free epochs were selected for the non-linear analysis. In all MEG channels, the AD patients had lower complexity than control subjects. In 77 of them the differences were statistically significant (p < 0.01). These preliminary results suggest that cognitive dysfunction in AD is associated with a decreased complexity in certain regions of the brain.

  • Abásolo D, Hornero R, Espino P, Alonso A, de la Rosa R. (2003) 'Electroencephalogram analysis with approximate entropy to help in the diagnosis of Alzheimer's disease'. IEEE ITAB 2003: 4TH INTERNATIONAL IEEE EMBS SPECIAL TOPIC CONFERENCE ON INFORMATION TECHNOLOGY APPLICATIONS IN BIOMEDICINE, CONFERENCE PROCEEDINGS, BIRMINGHAM, ENGLAND: 4th International Conference on Information Technology Applications in Biomedicine (ITAB 2003), pp. 222-225.
    doi: 10.1109/ITAB.2003.1222516
    Full text is available at: http://epubs.surrey.ac.uk/713542/

    Abstract

    Alzheimer's disease (AD) is the main cause of dementia in western countries. Although a definite diagnosis of this illness is only possible by necropsy, the analysis of nonlinear dynamics in electroencephalogram (EEG) signals could help physicians in this difficult task In this study we have applied Approximate Entropy (ApEn) to analyze the EEG background activity of patients with a clinical diagnosis of Alzheimer's disease and control subjects. ApEn is a newly introduced statistic that can be used to quantify the complexity (or irregularity) of a time series. We have divided the EEG data into frames to calculate their ApEn. Our results show that the degree of complexity of EEGs from control subjects is higher. Applying the ANOVA test, we have verified that there was a significant difference (p < 0.05) between the EEGs of these groups.

  • Hornero R, Abásolo D, Jimeno N, Espino P. (2003) 'Applying Approximate Entropy and Central Tendency Measure to Analyze Time Series Generated by Schizophrenic Patients'. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 3, pp. 2447-2450.

Affiliations

- Member of the IEEE and the IEEE Engineering in Medicine and Biology Society since 2004.

- Member of the Spanish Biomedical Engineering Society (Sociedad Española de Ingeniería Biomédica) since 2004.

- Member of the European Alliance for Medical and Biological Engineering and Science since 2004.

- Member of the International Federation for Medical and Biological Engineering since 2004.

- Member of the Institution of Engineering and Technology since 2010.

- Member of the executive committee of the Institution of Engineering and Technology Healthcare Technologies Network since January 2011.