Heartbeat Classification in Wearables Using Multi-layer Perceptron and Time-Frequency Joint Distribution of ECG

Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed representations generates only 3.7% false negatives, reduction of 89% with respect to existing ECG signal classification techniques.Comment: 6 pages, 7 figures, published in IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE

Dualities in persistent (co)homology

We consider sequences of absolute and relative homology and cohomology groups that arise naturally for a filtered cell complex. We establish algebraic relationships between their persistence modules, and show that they contain equivalent information. We explain how one can use the existing algorithm for persistent homology to process any of the four modules, and relate it to a recently introduced persistent cohomology algorithm. We present experimental evidence for the practical efficiency of the latter algorithm.Comment: 16 pages, 3 figures, submitted to the Inverse Problems special issue on Topological Data Analysi

Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention

Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for denoising, augmentation and balancing recording numbers of variant classes. In consideration of efficiency and consistency of data length, the recordings are padded or truncated into a medium length, where the padding/truncating time windows are selected randomly to sup-press overfitting. Then, the ECGs are used to train deep neural network (DNN) models with a novel structure that combines a deep residual network with an attention mechanism. Finally, an ensemble model is built based on these trained models to make predictions on the test data set. Our method is evaluated based on the test set of the First China ECG Intelligent Competition dataset by using the F1 metric that is regarded as the harmonic mean between the precision and recall. The resultant overall F1 score of the algorithm is 0.875, showing a promising performance and potential for practical use.Comment: 8 pages, 2 figures, conferenc

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms

Event categories in the EDELWEISS WIMP search experiment

Four categories of events have been identified in the EDELWEISS-I dark matter experiment using germanium cryogenic detectors measuring simultaneously charge and heat signals. These categories of events are interpreted as electron and nuclear interactions occurring in the volume of the detector, and electron and nuclear interactions occurring close to the surface of the detectors(10-20 mu-m of the surface). We discuss the hypothesis that low energy surface nuclear recoils,which seem to have been unnoticed by previous WIMP searches, may provide an interpretation of the anomalous events recorded by the UKDMC and Saclay NaI experiments. The present analysis points to the necessity of taking into account surface nuclear and electron recoil interactions for a reliable estimate of background rejection factors.Comment: 11 pages, submitted to Phys. Lett.

Background discrimination capabilities of a heat and ionization germanium cryogenic detector

The discrimination capabilities of a 70 g heat and ionization Ge bolometer are studied. This first prototype has been used by the EDELWEISS Dark Matter experiment, installed in the Laboratoire Souterrain de Modane, for direct detection of WIMPs. Gamma and neutron calibrations demonstrate that this type of detector is able to reject more than 99.6% of the background while retaining 95% of the signal, provided that the background events distribution is not biased towards the surface of the Ge crystal. However, the 1.17 kg.day of data taken in a relatively important radioactive environment show an extra population slightly overlapping the signal. This background is likely due to interactions of low energy photons or electrons near the surface of the crystal, and is somewhat reduced by applying a higher charge-collecting inverse bias voltage (-6 V instead of -2 V) to the Ge diode. Despite this contamination, more than 98% of the background can be rejected while retaining 50% of the signal. This yields a conservative upper limit of 0.7 event.day^{-1}.kg^{-1}.keV^{-1}_{recoil} at 90% confidence level in the 15-45 keV recoil energy interval; the present sensitivity appears to be limited by the fast ambient neutrons. Upgrades in progress on the installation are summarized.Comment: Submitted to Astroparticle Physics, 14 page

First Results of the EDELWEISS WIMP Search using a 320 g Heat-and-Ionization Ge Detector

The EDELWEISS collaboration has performed a direct search for WIMP dark matter using a 320 g heat-and-ionization cryogenic Ge detector operated in a low-background environment in the Laboratoire Souterrain de Modane. No nuclear recoils are observed in the fiducial volume in the 30-200 keV energy range during an effective exposure of 4.53 kg.days. Limits for the cross-section for the spin-independent interaction of WIMPs and nucleons are set in the framework of the Minimal Supersymmetric Standard Model (MSSM). The central value of the signal reported by the experiment DAMA is excluded at 90% CL.Comment: 14 pages, Latex, 4 figures. Submitted to Phys. Lett.

Identification of backgrounds in the EDELWEISS-I dark matter search experiment

This paper presents our interpretation and understanding of the different backgrounds in the EDELWEISS-I data sets. We analyze in detail the several populations observed, which include gammas, alphas, neutrons, thermal sensor events and surface events, and try to combine all data sets to provide a coherent picture of the nature and localisation of the background sources. In light of this interpretation, we draw conclusions regarding the background suppression scheme for the EDELWEISS-II phase