An Evolutionary-Neural Mechanism for Arrhythmia Classification With Optimum Features Using Single-Lead Electrocardiogram

Potentially lethal heart abnormalities can be detected/spotted with recent evolution in continuous, long-term cardiac health monitoring using wearable sensors. However, the huge data accumulated presents a challenge in terms of storage, knowledge extraction and computing time. Moreover, manual examination of long-term ECG recordings presents various problems like huge time and work demand, inter-observer variations and difficulty classifying complex non-linear single-lead ECG signal. To address these problems, we propose an automatic heartbeat classification system that uses the optimized minimum number of features using ECG time-series amplitude directly as input, without feature extraction and provides a primary classification and diagnosis for 1 normal and 14 types of arrhythmic heartbeats. Multi-objective particle swarm optimization (MOPSO) is used to achieve the best feature fitness. A novel fitness function is designed to be the sum of macro F1 loss and normalized dimension, with the optimization objective calculated as the minimum of the fitness function. Multi-layer perceptron (MLP), k-nearest neighbor, support vector machine, random forest and extra decision tree classifiers are trained using the selected features. For the targeted 15-class classification problem, MOPSO-optimized features with MLP consistently performed best with significantly reduced number of features. The proposed method proves to be an efficient and effective arrhythmia identification system for continuous, long-term cardiac health monitoring using single-lead ECG signal.