Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats

• Published in: Computers in biology and medicine Vol. 102; pp. 278 - 287
• Main Authors: Oh, Shu Lih, Ng, Eddie Y.K., Tan, Ru San, Acharya, U. Rajendra
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-11-2018; Elsevier Limited
• Subjects: Abnormalities; Accuracy; Algorithms; Ambulatory electrocardiogram; Arrhythmia; Artificial neural networks; Automation; Cardiac arrhythmia; Cardiology; Cardiovascular disease; Cardiovascular system; Computer aided detection; Conduction; Contraction; Convolutional neural network; Deep learning; Diagnosis; EKG; Heart; Long short-term memory; Medical diagnosis; Morphology; Neural networks; Physiology; Ventricle; Wavelet transforms; Computer aided detection; Deep learning; Automation; Arrhythmia; Ambulatory electrocardiogram; Convolutional neural network; Long short-term memory; Diagnosis
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2018.06.002

Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats. Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) signal. However, it can be challenging and time-consuming to visually assess the ECG signals due to the very low amplitudes. Implementing an automated system in the clinical setting can potentially help expedite diagnosis of arrhythmia, and improve the accuracies. In this paper, we propose an automated system using a combination of convolutional neural network (CNN) and long short-term memory (LSTM) for diagnosis of normal sinus rhythm, left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature beats (APB) and premature ventricular contraction (PVC) on ECG signals. The novelty of this work is that we used ECG segments of variable length from the MIT-BIT arrhythmia physio bank database. The proposed system demonstrated high classification performance in the handling of variable-length data, achieving an accuracy of 98.10%, sensitivity of 97.50% and specificity of 98.70% using ten-fold cross validation strategy. Our proposed model can aid clinicians to detect common arrhythmias accurately on routine screening ECG.
[Display omitted]
•Classification of 5-classes arrhythmias using variable length heart beats.•Five classes are Normal, PVC, LBBB, APB, RBBB.•Employed combination of CNN and LSTM techniques.•Noise filtering, feature extraction and selection are not required.•Obtained 98.10% accuracy, 97.50% sensitivity and 98.70% specificity.