Key result
CNN using 2D ECG data achieves ~99% accuracy in classifying normal and dysrhythmia beats.
Why the study?
Heartbeat classification of electrocardiography signals is valuable for early warning of dysrhythmia, motivating a model to automatically detect main types of dysrhythmia.
Does a convolutional neural network model accurately classify dysrhythmia from electrocardiography signals?
Does a convolutional neural network model accurately classify dysrhythmia from electrocardiography signals?
Answers are grounded in this paper and cite their sources.
A convolutional neural network can accurately classify normal and arrhythmic ECG beats without prior feature extraction, potentially aiding automated dysrhythmia detection.
Electrocardiography is the most useful method for diagnosing cardiovascular disease such as arrhythmia. Heartbeat classification of electrocardiography signals is a valuable and hopeful technology for early warning of dysrhythmia because it contains the cardiac electrical activities and reflects the abnormal cardiac activity. Therefore, a novel electrocardiogram-based arrhythmic beats classification was proposed to automatically detect the main types of dysrhythmia using electrocardiography signal in this work. A convolutional neural network model was used to automatically detect the normal and the types of dysrhythmia electrocardiography beats. The beat was transformed into a matrix as two-dimensional input to the model. The classification system was assessed to detect the normal, left bundle branch block, premature ventricular contraction and right bundle branch block beats using the MIT-BIH arrhythmia database. The results showed that an average accuracy of 99.30% and 98.85% was achieved by using ML2 lead of electrocardiography data with one-dimensional and 2-dimensional input, respectively. An average accuracy of 97.00% and 97.20% was achieved by using V1 lead of electrocardiography data with one-dimensional and 2-dimensional input, respectively. Moreover, no feature extraction of signals was carried out in this study. Consequently, the proposed model can accurately test the unknown electrocardiography signal and aid the clinician in the diagnosis of dysrhythmia.
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Wenzong Lu (2022) studied Dysrhythmia (n=41). Convolutional Neural Network (CNN) with 2-dimensional input vs. 1-dimensional input was evaluated on Classification accuracy of dysrhythmia types. A 7-layer convolutional neural network using 2-dimensional input of ML2 lead electrocardiography data achieved an average accuracy of 99.30% in classifying normal and dysrhythmia beats.
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