The proposed multi-parameter SE-ResNet model for a wearable ECG system achieved a premature ventricular contraction recognition accuracy of 99.34% on the MIT-BIH database and 94.07% on recorded data.
Does an MP-SE-ResNet based wearable system accurately detect premature ventricular contractions in ECG recordings?
A novel wearable PVC real-time auxiliary diagnosis system based on MP-SE-ResNet demonstrated high accuracy, sensitivity, and specificity for PVC recognition in both standard databases and real-world device recordings.
The real-time and accurate detection of premature ventricular contractions (PVC) in patients is of great significance for preventing the occurrence of high-risk events such as sudden cardiac death and guiding cardiac surgical procedures such as radiofrequency ablation. To improve the diagnostic accuracy and real-time performance, and expand application scenarios, an economical wearable PVC real-time auxiliary diagnosis system based on the multi-parameter squeeze excitation residual network (MP-SE-ResNet) is proposed. We have realized the real-time acquisition, processing, and wireless transmission of dynamic ECGs based on ESP32, furthermore, realized the PVCs recognition based on MP-SE-ResNet. Using the lead-II ECGs in the MIT-BIH arrhythmia databases as training samples, and the network was evaluated using the remainder of this dataset and data recorded by our device, respectively. The accuracy of the MIT-BIH dataset reached 99.34%, and the sensitivity and specificity of PVC recognition reached 98.26% and 99.64%, respectively. Using the ECGs recorded by our system, we achieved the following results: the accuracy was 94.07%, the sensitivity and specificity of PVC were 92.76% and 97.63%, respectively. The experimental results show that the system meets the requirements of remote monitoring and auxiliary diagnosis. Therefore, it provides a new method and design idea for wearable remote arrhythmia monitoring and auxiliary diagnosis.
Li et al. (Mon,) conducted a other in Premature ventricular contractions (PVC). Multi-parameter SE-ResNet (MP-SE-ResNet) wearable ECG system vs. Other machine learning models (ResNet, LSTM, CNN, AlexNet) was evaluated on Overall PVC recognition accuracy. The proposed multi-parameter SE-ResNet model for a wearable ECG system achieved a premature ventricular contraction recognition accuracy of 99.34% on the MIT-BIH database and 94.07% on recorded data.
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