Design of an Integrated Arrhythmia Detection Model using Connectivity Features and Multivariate Time Series Classification

Cardiac arrhythmia, characterized by irregular heart rhythms, represents a widespread concern within the realm of cardiology. It encompasses a range of rhythm irregularities, with some being benign and others carrying substantial health risks. Therefore, the timely detection of arrhythmia holds considerable importance. Existing methods to detect arrhythmia mainly utilize either the traditional machine learning classifiers like SVM, and random forest or the recent deep learning-based models like CNN, LSTM, and RNN for the classification while few other methods use the classical signal processing-based transforms to extract the discriminating features. This paper proposes a novel integrated approach to classify the ECG signals for arrhythmia detection. Unlike existing methods, it considers the multivariate time series nature of the input along with the interrelationships among different ECG leads. The approach utilizes multivariate time series features extracted using ROCKET (RandOM Convolutional KErnal Transform) and introduces new connectivity-based features such as correlation and coherence for improved ECG signal classification. The state-of-the-art classification performance of the proposed integrated model on the PTB-XL PhysioNet dataset attested to the efficacy of the same.