Dr Daniel Abásolo MEng PhD is Senior Lecturer in Biomedical Engineering at the Centre for Biomedical Engineering, Department of Mechanical Engineering Sciences within the Faculty of Engineering and Physical Sciences at the University of Surrey since 2010.
He is an engineer by background, holding an Engineering degree (MEng Telecommunication Engineering, 2001) from the University of Valladolid, where he also carried doctoral research and was conferred the PhD degree - Summa Cum Laude - with a thesis on advanced biomedical signal processing of the electroencephalogram (EEG) in Alzheimer's disease in 2006.
Dr Abásolo started his research career working on the development of non-linear techniques for the analysis of brain activity recorded in EEGs for the diagnosis of Alzheimer's disease. He has subsequently applied this work to the characterisation of other biomedical signals (such as intracranial pressure, oxygen saturation and electrocardiogram recordings, among others) with many different advanced signal processing algorithms. His work has led to the publication of several highly cited papers in indexed journals (h-index of 24 based on Thomson Reuters, February 2018, or 26 based on Google Scholar, February 2018) and he has also been instrumental in securing research grants throughout his academic career.
Currently, Dr Abásolo's research interests lie mainly in non-linear biomedical signal processing, in particular the development of algorithms that can be applied to Alzheimer's disease diagnosis, the characterisation of healthy and pathological ageing using brain signals, EEG analysis in sleep studies, and electrocardiogram analysis in atrial fibrillation. He is also interested in deep learning techniques for EEG analysis and the identification of circadian and ultradian rhythms with advanced signal processing algorithms.
Dr Daniel Abásolo is a passionate advocate for biomedical engineering and has been championing the discipline both at the University of Surrey, for example as a member of the Engineering for Health project, and nationally, as an active member of the IET Healthcare Technologies Network Executive Committee. He is currently the Chairman of this committee and also represents the IET at the IMechE Biomedical Engineering Division.
He is also particularly enthusiastic about nurturing students' talent with research-intensive final year projects. This has led to significant success for the University of Surrey nationally, with medical/biomedical engineering students working under his supervision winning the IET Dennis Hill Award for best MEng/MSc project for four consecutive years and other IMechE Medical Engineering student awards also going to students from the University of Surrey.
University roles and responsibilities
- Programme Leader for BEng/MEng Biomedical Engineering
Affiliations and memberships
- Biomedical Signal Processing
- Non-linear Analysis
- Deep Learning
- Alzheimer's Disease
- Healthy and Pathological Ageing
- Intracranial Pressure
- Normal Pressure Hydrocephalus
- Atrial Fibrillation
- Circadian and Ultradian Rhythms
Dr Raphaelle Winsky-Sommerer, University of Surrey, UK
Dr Jonathan Johnston, University of Surrey, UK
Dr Daan van der Veen, University of Surrey, UK
Prof Christopher James, University of Warwick, UK
Prof Chris Fry, University of Bristol, UK
Dr Javier Escudero, University of Edinburgh, UK
Dr Vladyslav Vyazovskiy University of Oxford, UK
Dr Herbert Jelinek, Charles Sturt University, Australia
Dr Alberto Fernández, Universidad Complutense Madrid, Spain
- FHEQ Level 6 (Year 3 BEng/MEng Medical Engineering): Biomedical Signal Processing (ENG3186)
- FHEQ Level 7 (MSc Biomedical Engineering): Instrumentation (ENGM186)
- FHEQ Level 7 (MSc Biomedical Engineering): Computer Methods in Biomedical Engineering Research (ENGM259)
- Supervision of final year projects (BEng/MEng Medical Engineering, MSc Biomedical Engineering)
specificity (defined as correctly classified controls) were evaluated using the receiver operating curves method. Accuracy of the methods was calculated according to sensitivity and specificity measures of electrodes showing statistically significant differences between the control group and Alzheimer's disease patients and ranged between 72.73-77.27%. These accuracy values were in agreement with previously published entropy studies on this data set. Although combining these methods did not provide any greater accuracy over previous findings, using a symbolic sequence decomposition method enhanced the data processing.
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.
We propose a novel complexity measure to overcome the deficiencies of the widespread and powerful multiscale entropy (MSE), including, MSE values may be undefined for short signals, and MSE is slow for real-time applications.
We introduce multiscale dispersion entropy (DisEn - MDE) as a very fast and powerful method to quantify the complexity of signals. MDE is based on our recently developed DisEn, which has a computation cost of O(N), compared with O(N2) for sample entropy used in MSE. We also propose the refined composite MDE (RCMDE) to improve the stability of MDE.
We evaluate MDE, RCMDE, and refined composite MSE (RCMSE) on synthetic signals and three biomedical datasets. The MDE, RCMDE, and RCMSE methods show similar results, although the MDE and RCMDE are faster, lead to more stable results, and discriminate different types of physiological signals better than MSE and RCMSE.
For noisy short and long time series, MDE and RCMDE are noticeably more stable than MSE and RCMSE, respectively. For short signals, MDE and RCMDE, unlike MSE and RCMSE, do not lead to undefined values. The proposed MDE and RCMDE are significantly faster than MSE and RCMSE, especially for long signals, and lead to larger differences between physiological conditions known to alter the complexity of the physiological recordings.
caused by the progressive death of brain cells over time. One
non-invasive approach to investigate AD is to use electroencephalogram
(EEG) signals. The data are usually non-stationary
with a strong background activity and noise which makes the
analysis difficult leading to low performance in many real
world applications including the detection of AD. In this study,
we present a method based on local texture changes of EEG
signals to differentiate AD patients from the healthy ones, using
one-dimensional local binary patterns (1D-LBPs) coupled with
support vector machines (SVM). Our proposed method maps
the EEG data into a less detailed representation which is less
sensitive to noise. A 10 fold cross validation performed at both
the epoch and subject level show the discriminancy power of
1D-LBP feature vectors with application to AD data.
Electroencephalogram: An Application to Detecting
Alzheimer?s Disease, IEEE CIBCB 2017 Proceedings IEEE
disease caused by the progressive death of brain cells over
time. It represents the most frequent cause of dementia in the
western world, and affects an individual?s cognitive ability and
psychological capacity. While clinical diagnoses of AD are made
primarily on the basis of clinical evaluation and mental health
tests, diagnostic certainty is only possible through necropsy. One
non-invasive approach to investigating AD is to use electroencephalograms
(EEGs), which reflect brain electrical activity and
so can be used to detect electrical abnormalities in brain signals
with non-invasive cranial surface electrodes. Generally EEGs
in AD patients show a shift to lower frequencies in spectral
analysis and display less complexity and contain more regular
patterns compared to those of control subjects. Here we present
a method for differentiating AD patients from healthy ones based
on their EEG signals using Benford?s law and support vector
machines (SVMs) with a radial basis function (RBF) kernel.
EEG signals from eleven AD and eleven age-matched controls
were divided into artefact-free 5-sec epochs and used to train an
SVM. 10 fold cross validation was performed at both the epochand
subject-level to evaluate the importance of each electrode
in discriminating between AD and healthy subjects. Substantive
variability was seen across the different electrodes, with electrodes
O1, O2 and C4 particularly being important. Performance across
the electrodes was reduced when subject-level cross validation
was performed, but relative performance across the electrodes
was consistent with that found using epoch-level cross validation.
in selecting an appropriate threshold to binarise edge weights. For EEG phase-based
functional connectivity, we test the hypothesis that such binarisation should take into
account the complex hierarchical structure found in functional connectivity. We explore
the density range suitable for such structure and provide a comparison of
state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold
(CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path
graphs, with arbitrary proportional thresholds and weighted networks. We test these
techniques on weighted complex hierarchy models by contrasting model realisations
with small parametric differences. We also test the robustness of these techniques to
random and targeted topological attacks.We find that the CST performs consistenty
well in state-of-the-art modelling of EEG network topology, robustness to topological
network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical
complexity. This provides interesting new evidence into the relevance of considering a
large number of edges in EEG functional connectivity research to provide informational
density in the topology.
in Patients with Alzheimer?s Disease: Is the Method
Superior to Sample Entropy?, Entropy 20 (1) MDPI
characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive
symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy,
differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG)
is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear
signal processing methods have shown changes in the EEG due to AD, which is characterised
reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control
subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an
improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn)
algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using
Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn,
reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the
insight into brain dysfunction in AD, providing potentially useful diagnostic information. However,
results depend heavily on the input parameters that are used to compute FuzzyEn.
The motion characterising submarining with CRS is as of yet poorly defined, and although CRS are assessed for protection level, there is currently no established identification criteria for submarining.
As part of the Enabling Protection for Older Children project (EPOCh), standard frontal impact sled tests of 10 CRS (6 high back booster seats; 4 booster cushions) with 10 year old anthropomorphic testing devices (ATDs) were analysed qualitatively, transversally and longitudinally for submarining detection. The methods used included video analysis, descriptive and inferential statistics, principal component analysis, time series analysis, as well as multiple linear regression and logistic regression, applied on both ATD trajectories and ATD instrumentation recordings (dynamic data).
From the videos, trajectories and dynamic data, submarining motion is shown to embody an exaggerated slouching movement regardless of the CRS type. The observations and quantitative results confirm that exaggerated forward knee displacement is characteristic of submarining, as well as a very strong criterion for the latter's detection. No other individual trajectory or dynamic variable distinguish clear submarining behaviour, however the regression analyses on dynamic variables establish the association of the pelvis, lumbar and chest as representative of the knee displacement, and demonstrate the combination's capacity to distinguish submarining cases.
These findings establish the complexity of the movement involved in submarining and the potential of using current ATD instrumentation for its assessment with CRS. This opens a path for an integral approach to ATD movement in CRS appraisal and suggests considering pelvis, lumbar and chest motion control for submarining prevention.
In this PhD, rodent and human sleep EEG recordings were analysed using SDA methods: Lempel-Ziv complexity (LZC), Permutation Entropy (PE) and Permutation Lempel-Ziv complexity (PLZC). All the methods were able characterise different VS with wakefulness and REM sleep resulting in higher measures of complexity compared to NREM sleep suggesting an active state of the brain in these VS. This was measured in all datasets assisting the hypothesis on the usefulness of these techniques in sleep research by providing the minimum requirement for sleep analysis. In addition to this, SD significantly reduced complexity in the following sleep period supporting the compensation process for the lost sleep by the increased in slow wave activity which was reflected as reduced complexity in this study. Furthermore, a low dose tiagabine administration?s sleep compensation promoting effect was found in mice.
Moreover, ageing was identified as a main effect on changes in brain activity. These changes were more pronounced in the old age where complexity was significantly lower compared to young age. On the one hand, this was found with all three methods and contributing to the hypothesis that these techniques reveal structural dynamic changes due to physiological alterations. On the other hand, no significant differences in complexity across genders were found suggesting the underlying mechanisms to maintain sleep-wake cycles are similar for men and women. This finding with further investigation might corroborate to question the need to use both genders in drug trials. Furthermore, significant changes in brain activity were found at different times of the sleep period highlighting the changes occurring within VS as sleep progresses. This also has an impact on the way sleep stages are scored and investigated which are influenced by different brain activity levels within each VS throughout the entire sleep.
All in all, this study achieved to support its hypothesis of determining the changes in brain activity as a complexity measure by characterising sleep under physiological and pharmacologically induced EEG datasets in mice and in humans. The study was a novel application to analyse sleep in these conditions. However, with further analysis performed on larger datasets, its findings together with surrogate data analysis proved SDA techniques? robust usability which can complement the gold standard FT analysis in sleep research.
The brain is a very delicate, but sophisticated and complex organ of the body. During life, the brain grows and develops, matures, and then ages like all other organs of the body. This maturation and ageing comes with various physiological and anatomical changes which have an impact on the background activity of the brain. Brain activity can be recorded using various techniques including magnetoencephalography and magnetic resonance imaging, and these recordings combined with signal processing can be useful to characterise the changes in the brain that can be a result of the ageing process. By analysing various aspects of the brain, such as functional and effective connectivity, entropy and complexity, the state of the resting brain at different ages can be understood with greater detail. Furthermore, these analyses can be used to investigate the resting state brain networks that are present in the brain, as well as their topology. This information can be combined with network analysis techniques such as graph theory and used to understand the manner in which the brain both matures and ages.
This research made use of two magnetoencephalogram (MEG) databases recorded from both males and females. The first, containing resting state MEGs (rMEGs) from 220 healthy volunteers (aged 7-84), was used as the main database in this thesis to investigate the effects of age on rMEG signals throughout life. The aim of this research was to make use of rMEG signals and signal processing techniques to determine the effects of healthy ageing on the brain throughout life. It was hypothesised that the effects of age are identifiable using advanced signal processing techniques. Thus, the effects of age on linear interactions, causality, synchronisation, information flow, entropy and complexity, were investigated in the 148 MEG channels lying over 5 brain regions (anterior, central, left lateral, posterior, and, right lateral) using multiple linear and non-linear analysis techniques (namely: Pearson?s correlation, coherence, Granger causality, phase slope index, rho index, transfer entropy, synchronisation likelihood, Lempel-Ziv complexity, permutation Lempel-Ziv complexity, permutation entropy, and, modified permutation entropy). Additionally, graph theory principles were used to evaluate different network components such as integration (global efficiency), segregation (clustering coefficient and modularity), centrality (betweenness), and resilience (strength and assortativity) so as to obtain an understanding of the construct of the resting brain network. Moreover, complex network analysis was also used to determine the overall network topology of the brain network and how this changed at different stages in life. Gender effects were also studied so as to identify if there were any significant differences between males and females at different stages of life. Results from these analyses showed that the healthy resting brain has low effective and functional connectivity, relatively low complexity and entropy, as well as no significant detectable direction of information flow. Therefore this showed that there is very little synchronous or simultaneously occurring information in the rMEG time series. Thus, during rest, the brain resembles a system in limbo/phase transition, with low effective and functional connectivity, relatively low complexity and entropy, and no significant detectable direction of information flow.
The second database used in this research project was obtained during a collaboration visit to the Cognitive and Computational Neuroscience laboratory at the Centre for Biomedical Technology- Universidad Politécnica de Madrid (CTB-UPM). This database was made up of rMEGs recorded from 199 healthy volunteers (aged 60-80), and the focus of this additional set of analyses was to identify differences between the rMEG signals recorded from healthy individuals, those with subjective cognitive decline as well as those with