Abstract
Cardiovascular diseases are the main cause of global non-communicable deaths, accounting for about one-third of the total deaths in the world. Wearable devices are used for early monitoring and early prevention of cardiovascular diseases. Aimed at the low configuration, low power consumption and personalized characteristics of the wearable ECG equipment, a transfer learned with deep LSTM model is proposed in this work. The model is computational efficient and serves as the patient-specific ECG anomaly detector that can be used on the wearable devices. Based on the MIT-BIH open dataset, the experiment results show that the transfer learning model produces superior weighted F2 scores compared to the general model and the naive model for detecting ECG anomalies, such as the premature ventricular contractions and atrial premature beats. The transfer learning model proposed here provides a sensitive, personalized ECG anomaly detection mechanism that has the added benefit of light, quick and robust learning process to be used on wearable devices, which can meet the requirement of ECG monitoring for patients by wearable devices.
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Acknowledgments
This work was financially supported by the National Nature Science Foundation of China (No. 61806033, 61703065), the National Natural Science Foundation of Chongqing(cstc2020jcyj-msxm1555), the Scientific and Technological Research Program of Chongqing Municipal Education Commission (No. KJ202000646472197), the Key Industry Core Technology Innovation Project of CQ (cstc2017zdcy-zdyfX0012).
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Deng, X., Zhou, J., Xiao, B., Xiang, X., Yuan, Y. (2021). Deep LSTM Transfer Learning for Personalized ECG Anomaly Detection on Wearable Devices. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_32
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DOI: https://doi.org/10.1007/978-981-16-2336-3_32
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