Research Area: Machine Learning
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.
Keywords:
Automated Diagnosis
Arrhythmia
Cnn
Lstm Techniques
Variable Length Heart Beats
Machine Learning
Deep Learning
Author(s) Name: Oh, Shu Lih; Ng, Eddie Yin Kwee; Tan, Ru San; Acharya, U. Rajendra
Journal name: Computers in Biology and Medicine
Conferrence name:
Publisher name: ELSEVIER
DOI: 10.1016/j.compbiomed.2018.06.002
Volume Information: Volume 102, 1 November 2018, Pages 278-287
