An improved deep residual convolutional neural network achieved 88.35% sensitivity and 94.92% specificity for ventricular ectopic segments, comparable to state-of-the-art methods.
Does an improved deep residual convolutional neural network improve automated arrhythmia classification on ECG signals?
An improved deep residual convolutional neural network using overlapping segmentation and focal loss achieves high sensitivity and specificity for automated arrhythmia classification on ECG segments.
BACKGROUND AND OBJECTIVE: Early detection of arrhythmias has become critical due to the increased mortality from cardiovascular disease, and ECG is an effective tool for diagnosing cardiovascular disease and detecting arrhythmias. Classification based on ECG signal segments is more suitable for clinical application. METHODS: An improved deep residual convolutional neural network is proposed to classify arrhythmias automatically. Firstly, the overlapping segmentation method is used to segment the ECG signals in the MIT-BIH database into segments of 5 seconds in length to overcome the imbalance between classes, and these segments of the ECG signals are re-labeled. Then the discrete wavelet transform (DWT) is used to denoise these segments and the improved deep residual convolutional neural network is used for arrhythmia classification. In addition, the focal loss function is used to overcome the imbalanced classification difficulty between classes. RESULTS: The proposed method gives 94.54% sensitivity, 93.33% positive predictivity, and 80.80% specificity for normal segments. For the supraventricular ectopic segment, the proposed method gives 35.22% sensitivity, 65.88% positive predictivity, and 98.83% specificity. For the ventricular ectopic segment, the proposed method gives 88.35% sensitivity, 79.86% positive predictivity, and 94.92% specificity. CONCLUSION: The results of this study indicate that the proposed improved deep residual convolutional neural network model trained by the training set obtained by using the overlapping segmentation method is comparable to a classical method and three state-of-art methods. In addition, the classification performance of the network model trained by focal loss as the loss function is further improved.
Li et al. (Sun,) conducted a other in Arrhythmias. Improved deep residual convolutional neural network vs. Classical method and three state-of-art methods was evaluated on Classification performance (sensitivity, positive predictivity, specificity). An improved deep residual convolutional neural network achieved 88.35% sensitivity and 94.92% specificity for ventricular ectopic segments, comparable to state-of-the-art methods.
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