Automatic Classification of Atrial and Ventricular Arrhythmias Using Scaled Bidirectional LSTM

Cardiac arrhythmia (CA) is a condition of the heart that raises the risk of heart failure, stroke, and mortality. A CA can be quickly detected by an electrocardiogram (ECG), which analyses the heart’s electrical activity. The primary diagnostic procedure, which is typically strenuous and ineffective, is the visual examination of ECG. In this article, a scaled bidirectional long short-term memory (SB-LSTM) prototype is designed for the automatic categorization of CAs. The conventional bidirectional long short-term memory (BILSTM) is modified by embedding a scaling layer into it and hence it can able to categorize the CAs automatically and efficiently. After the various preprocessing steps, namely denoising, and normalization, the results show that there is an improvement in accuracy (ACCY). The newly developed prototype is endorsed by popular databases like MIT-BIH atrial fibrillation database (MITBIHAFDB), Creighton University Ventricular Tachyarrhythmia Database (CUVTDB), and MIT-BIH Malignant Ventricular Ectopy database (MITBIHVFDB) from the physioNet warehouse. Using these resources, we succeeded in achieving an ACCY of 98.60%. Thus, the technique proposed presents a reliable and accurate procedure for the automatic categorization of CAs.