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
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.
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.
Background: Life threatening arrhythmias resulting from genetic mutations are often missed in current 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+D- 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 anaesthetised wild type and Scn5a+D- mutant mice of length 130s were analysed 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 two-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+D- mutation from short mouse ECG signals with high accuracy is possible using our Symmetric Projection Attractor Reconstruction method.