Curvature flow learning: algorithm and analysis

Li, Yangyang; Lu, Ruqian

doi:10.1007/s11432-020-3068-7

Cited by 1 publication

(1 citation statement)

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“…Continuously monitoring and recognition for life threating arrhythmia based on wearable and smartphone devices using deep-learning method become a hot topic in recent years [21][22][23]. As PVC often exhibits no obvious clinical symptoms during the attack and is a primary contributor to sudden cardiac death, there have been many research results on the real-time recognition of PVCs [24][25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Real-Time PVC Recognition System Design Based on Multi-Parameter SE-ResNet

Li,

Liu,

Sun

et al. 2024

IEEE Access

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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.

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