Placeholder image for staff profile

Jane Lyle

Postgraduate Research Student
+44 (0)1483 683024
17 AA 04

Academic and research departments

Department of Mathematics.

My publications


J. V. Lyle, P. J. Aston (2021)Symmetric projection attractor reconstruction: Embedding in higher dimensions, In: Chaos: An Interdisciplinary Journal of Nonlinear Science31(11)113135 American Institute of Physics

Symmetric Projection Attractor Reconstruction (SPAR) provides an intuitive visualization and simple quantification of the morphology and variability of approximately periodic signals. The original method takes a three-dimensional delay coordinate embedding of a signal and subsequently projects this phase space reconstruction to a two-dimensional image with threefold symmetry, providing a bounded visualization of the waveform. We present an extension of the original work to apply delay coordinate embedding in any dimension 𝑁≥3 while still deriving a two-dimensional output with some rotational symmetry property that provides a meaningful visualization of the higher dimensional attractor. A generalized result is developed for taking 𝑁≥3 delay coordinates from a continuous periodic signal, where we determine invariant subspaces of the phase space that provide a two-dimensional projection with the required rotational symmetry. The result in each subspace is shown to be equivalent to following each pair of coefficients of the trigonometric interpolating polynomial of 𝑁 evenly spaced points as the signal is translated horizontally. Bounds on the mean and the frequency response of our new coordinates are derived. We demonstrate how this aids our understanding of the attractor properties and its relationship to the underlying waveform. Our generalized result is then extended to real, approximately periodic signals, where we demonstrate that the higher dimensional SPAR method provides information on subtle changes in different parts of the waveform morphology. Data science is challenged to derive meaningful information from large volumes of data. The Symmetric Projection Attractor Reconstruction (SPAR) method1 provides an innovative approach to the analysis of approximately periodic time-series data by generating a unique, bounded, visualization of the signal that encapsulates waveform shape and variability from a three-dimensional time delay embedding of a signal. This paper extends this work to present a general form giving a similar visualization derived from an embedding of the signal in any dimension 𝑁≥3, and the result is shown to be closely related to trigonometric polynomial interpolation. While the theoretical results are stated in the context of periodic signals, significantly, we show that the properties of this theoretical generalization can be translated to the analysis of real, approximately periodic signals where we demonstrate the potential clinical utility of the SPAR approach in the novel visualization and quantification of physiological signals.

JANE VICTORIA LYLE, Manasi Nandi, PHILIP JAMES ASTON (2020)Investigating the Response to Dofetilide with Symmetric Projection Attractor Reconstruction of the Electrocardiogram, In: Proceedings of International Conference in Computing in Cardiology 2019 (CinC 2019)46073pp. 1-4 Institute of Electrical and Electronics Engineers (IEEE)

The aim of this study is a preliminary investigation into the application of our novel Symmetric Projection Attractor Reconstruction (SPAR) method to the electrocardiogram (ECG) signals of individuals treated with the cardioactive drug dofetilide. We show that our SPAR technique correlates with standard assessment, and is also able to discriminate gender from the ECG response to dofetilide more accurately than the standard metrics.

Philip Aston, Jane Lyle, Esther Bonet-Luz, Christopher Huang, Yanmin Zhang, Kamalan Jeevaratnam, Manasi Nandi (2020)Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation, In: Proceedings of International Conference in Computing in Cardiology 2019 (CinC 2019)46097 Institute of Electrical and Electronics Engineers (IEEE)

The aim of this work is to distinguish between wild-type mice and Scn5a +/- mutant mice using short ECG signals. This mutation results in impaired cardiac sodium channel function and is associated with increased ventricular arrhythmogenic risk which can result in sudden cardiac death. Lead I and Lead II ECG signals from wild-type and Scn5a +/- mice are used and the mice are also grouped as female/male and young/old.We use our novel Symmetric Projection Attractor Reconstruction (SPAR) method to generate an attractor from the ECG signal using all of the available waveform data. We have previously manually extracted a variety of quantitative measures from the attractor and used machine learning to classify each animal as either wild-type or mutant. In this work, we take the attractor images and use these as input to a deep learning algorithm in order to perform the same classification. As there is only data available from 42 mice, we use a transfer learning approach in which a network that has been pretrained on millions of images is used as a starting point and the last few layers are changed in order to fine tune the network for the attractor images.The results for the transfer learning approach are not as good as for the manual features, which is not too surprising as the networks have not been trained on attractor images. However, this approach shows the potential for using deep learning for classification of attractor images.

Jane Lyle, Peter H. Charlton, Esther Bonet Luz, Gary Chaffey, Mark Christie, Manasi Nandi, Philip Aston (2017)Beyond HRV: Analysis of ECG Signals Using Attractor Reconstruction, In: Christine Pickett, Cristiana Corsi, Pablo Laguna, Rob MacLeod (eds.), Computing in Cardiology 201744 Computing in Cardiology

Attractor reconstruction analysis has previously been applied to analyse arterial blood pressure and photoplethysmogram signals. This study extends this novel technique to ECG signals. We show that the method gives high accuracy in identifying gender from ECG signals, performing significantly better than the same classification by interval measures.

JANE VICTORIA LYLE, Manasi Nandi, PHILIP JAMES ASTON (2021)Symmetric Projection Attractor Reconstruction: Sex Differences in the ECG, In: Cardiovascular Medicine EMH Schweizerischer Arzteverlag

Background: The electrocardiogram (ECG) is a key tool in patient management. Automated ECG analysis supports clinical decision-making, but traditional fiducial point identification discards much of the time-series data that captures the morphology of the whole waveform. Our Symmetric Projection Attractor Reconstruction (SPAR) method uses all the available data to provide a new visualization and quantification of the morphology and variability of any approximately periodic signal. We therefore applied SPAR to ECG signals to ascertain whether this more detailed investigation of ECG morphology adds clinical value. Methods: Our aim was to demonstrate the accuracy of the SPAR method in discriminating between two biologically distinct groups. As sex has been shown to influence the waveform appearance, we investigated sex differences in normal sinus rhythm ECGs. We applied the SPAR method to 9,007 10 second 12-lead ECG recordings from Physionet, which comprised; Dataset 1: 104 subjects (40% female), Dataset 2: 8,903 subjects (54% female). Results: SPAR showed clear visual differences between female and male ECGs (Dataset 1). A stacked machine learning model achieved a cross-validation sex classification accuracy of 86.3% (Dataset 2) and an unseen test accuracy of 91.3% (Dataset 1). The mid-precordial leads performed best in classification individually, but the highest overall accuracy was achieved with all 12 leads. Classification accuracy was highest for young adults and declined with older age. Conclusions: SPAR allows quantification of the morphology of the ECG without the need to identify conventional fiducial points, whilst utilizing of all the data reduces inadvertent bias. By intuitively re-visualizing signal morphology as two-dimensional images, SPAR accurately discriminated ECG sex differences in a small dataset. We extended the approach to a machine learning classification of sex for a larger dataset, and showed that the SPAR method provided a means of visualizing the similarities of subjects given the same classification. This proof-of-concept study therefore provided an implementation of SPAR using existing data and showed that subtle differences in the ECG can be amplified by the attractor. SPAR's supplementary analysis of ECG morphology may enhance conventional automated analysis in clinically important datasets, and improve patient stratification and risk management.

Esther Bonet-Luz, Jane V. Lyle, Christopher L.-H. Huang, Yanmin Zhang, Manasi Nandi, Kamalan Jeevaratnam, Philip J. Aston (2020)Symmetric Projection Attractor Reconstruction analysis of murine electrocardiograms Retrospective prediction of Scn5a+⁄- genetic mutation attributable to Brugada syndrome, In: Heart Rhythm1(5)pp. 368-375 Elsevier

Background Life-threatening arrhythmias resulting from genetic mutations are often missed in current electrocardiogram (ECG) analysis. We combined a new method for ECG analysis that uses all the waveform data with machine learning to improve detection of such mutations from short ECG signals in a mouse model. Objective We sought to detect consequences of Na+ channel deficiencies known to compromise action potential conduction in comparisons of Scn5a+/- mutant and wild-type mice using short ECG signals, examining novel and standard features derived from lead I and II ECG recordings by machine learning algorithms. Methods Lead I and II ECG signals from anesthetized wild-type and Scn5a+/- mutant mice of length 130 seconds were analyzed by extracting various groups of features, which were used by machine learning to classify the mice as wild-type or mutant. The features used were standard ECG intervals and amplitudes, as well as features derived from attractors generated using the novel Symmetric Projection Attractor Reconstruction method, which reformulates the whole signal as a bounded, symmetric 2-dimensional attractor. All the features were also combined as a single feature group. Results Classification of genotype using the attractor features gave higher accuracy than using either the ECG intervals or the intervals and amplitudes. However, the highest accuracy (96%) was obtained using all the features. Accuracies for different subgroups of the data were obtained and compared. Conclusion Detection of the Scn5a+/- mutation from short mouse ECG signals with high accuracy is possible using our Symmetric Projection Attractor Reconstruction method.