MEDEEP CNN: MOMENTS EMBEDDING — DEEP CONVOLUTIONAL NEURAL NETWORK FOR ARRHYTHMIA CLASSIFICATION

An abnormal or irregular heartbeat is known as an arrhythmia, and it is a common condition. Uneven heartbeats or cardiac arrhythmias are caused when the electrical impulses that regulate heartbeats are disrupted, causing the heart to beat slowly, irregularly, or too quickly. Using a computer-assisted system can enhance detection accuracy in medical diagnoses. Traditional classification methods are often time-consuming and tend to offer limited accuracy. To classify arrhythmias, this study develops a hybrid model that integrates the Moments Embedding Network (MoNet) and Deep Convolutional Neural Network (Deep CNN). In the preprocessing phase, the Daubechies wavelet filter is applied along with an eight-level decomposition of the ECG signal to effectively identify its wave components. Then, auto-regressive, variational mode decomposition (VMD), empirical mode decomposition (EMD) and Discrete Wavelet Transform (DWT) features are extracted. After the feature extraction, the Chameleon-Sparrow Search Algorithm (CsSA) is used to select features efficiently. Moments Embedding-Deep Convolutional Neural Network (MEDeep CNN) is a network that is proposed to perform arrhythmia classification. Results obtained from experiments with established models reveal that the proposed CsSA-based MEDeep CNN methodology surpasses these benchmarks with an accuracy of 96.7%, a sensitivity of 97.2%, and a specificity of 96.5%.