TimeCaps: Capturing Time Series Data with Capsule Networks

Electrocardiogram (ECG) signal analysis plays a vital role in medical diagnosis since ECG signal can provide vital information that can help to diagnose various health conditions. For example, ECG beat classification; e.g classifying ECG signal portions in to classes such as normal beats or different arrhythmia types such as atrial fibrillation, premature contraction, or ventricular fibrillation allows to identify different cardiovascular diseases. Similarly, ECG signal compression and reconstruction have a variety of applications such as remote cardiac monitoring in body sensor nodes (Mamaghanian et al., 2011) and achieving low power consumption when sending and processing data through IoT -gateways (Al Disi et al., 2018). ECG signal analysis and classification was predominantly done using signal processing methods such as wavelet transformation or independent component analysis or feature driven classical machine learning methods (Y u and Chou, 2008; Martis et al., 2013; Kim et al., 2009; Li and Zhou, 2016). However such methods have left room for further improvements in terms of accuracy and the manual feature curation is a daunting task. Recently 1D Convolutions have been tried on ECG classification producing some promising results (Li et al., 2017; Acharya et al., 2017), Nonetheless, these methods do not perform well for the classes with less volumes of training data.