A Machine Learning-based Lightweight and Real-time Cardiac Arrhythmia Detection using Optimum Samples and Features

Multi-class cardiac arrhythmia detection (MCAD) from electrocardiogram (ECG) using various machine learning (ML) techniques has been active research area for some time. In the context of real-time wearable applications towards MCAD, the real challenge is lightweight architecture development with high accuracy. This research proposes an optimal raw sample selection from cardiac cycles, optimal features selection and corresponding real-time low-resource hardware implementation for CAD. It describes a two-stage sample reduction and selection, through beat skeletonization and segmentation into pre-QRS, QRS and post-QRS region from a cardiac cycle. A joint feature set consisting of optimally selected 14 morphological features and 10 deep autoencoder (DAE) extracted features from selected samples fed to a random forest classifier provides 95.34% detection accuracy while evaluated with most common 8 classes from MIT BIH arrhythmia database. The proposed CAD was implemented in an ARM v6 single core controller with a detection latency of 182 ms and 200 kbytes of execution memory, with an improved performance over few published works on real-time MCAD. The results are encouraging for real-time wearable MCAD detection development research.