Convolution Neural Network Bidirectional Long Short-Term Memory for Heartbeat Arrhythmia Classification

• Published in: International journal of computational intelligence systems Vol. 16; no. 1; pp. 1 - 13
• Main Authors: Alkhawaldeh, Rami S., Al-Ahmad, Bilal, Ksibi, Amel, Ghatasheh, Nazeeh, Abu-Taieh, Evon M., Aldehim, Ghadah, Ayadi, Manel, Alkhawaldeh, Samar M.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 19.12.2023; Springer
• Subjects: Arrhythmia; Artificial Intelligence; Bidirectional long short-term memory; Computational Intelligence; Control; Deep residual convolution neural network; Electrocardiogram; Engineering; Mathematical Logic and Foundations; Mechatronics; Research Article; Robotics; Transfer learning; Deep residual convolution neural network; Arrhythmia; Bidirectional long short-term memory; Transfer learning; Electrocardiogram
• ISSN: 1875-6883; 1875-6883
• DOI: 10.1007/s44196-023-00374-8

Arrhythmia is a heart condition that poses a severe threat to life and requires prompt medical attention. One of the challenges in detecting arrhythmias accurately is that incorrect diagnoses can have severe consequences. In light of this, it is critical to develop a solution that is both effective and reliable. In this study, we propose a residual Convolution Neural Network Bidirectional Long Short-Term Memory (DeepResidualBiLSTM) model for classifying Arrhythmia types, which addresses the vanishing gradient problem and captures the relevant features in the signals’ long dependencies. The model is characterized by its simplicity, stability, and ability to extract meaningful features effectively. Using two well-known datasets, the experimental results demonstrate exceptional accuracy, precision, and recall values of approximately 99.4% at the early stage of 20 epoch training. Furthermore, the model demonstrates a remarkable ability to discriminate between Arrhythmia classes under varying thresholds using the ROC curve metric, with a high value, in most cases, of 100% for accurately detecting positive cases.