Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks
arXiv:1906.05795
Abstract
This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that turns out to be effective for new unseen patient. The novelty of our approach relies on the use of topological data analysis as basis of our multichannel architecture, to diminish the bias due to individual differences. We show that our structure reaches the performances of the state-of-the-art methods regarding arrhythmia detection and classification.
7 pages, 4 figures
References in corpus (4)
- Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
- ECG arrhythmia classification using a 2-D convolutional neural network
- Detection of Premature Ventricular Contractions Using Densely Connected Deep Convolutional Neural Network with Spatial Pyramid Pooling Layer
- Combining Support Vector Machine and Elephant Herding Optimization for Cardiac Arrhythmias
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- ANAPT: Additive Noise Analysis for Persistence Thresholding
- PLLay: Efficient Topological Layer based on Persistence Landscapes
- ECG Classification based on Wasserstein Scalar Curvature
- Geometric feature performance under downsampling for EEG classification tasks