Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems

• URL: http://arxiv.org/abs/2008.08060v1
• Date: Tue, 18 Aug 2020 17:41:58 GMT
• Title: Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems
• Authors: Zhenge Jia, Zhepeng Wang, Feng Hong, Lichuan Ping, Yiyu Shi, Jingtong Hu
• Abstract summary: Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD) We propose the personalized computing framework for deep learning based VA detection on medical IoT systems. We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
• Score: 17.966382901357118
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD), which is the most significant cause of natural death in the US. The implantable cardioverter defibrillator (ICD) is a small device implanted to patients under high risk of SCD as a preventive treatment. The ICD continuously monitors the intracardiac rhythm and delivers shock when detecting the life-threatening VA. Traditional methods detect VA by setting criteria on the detected rhythm. However, those methods suffer from a high inappropriate shock rate and require a regular follow-up to optimize criteria parameters for each ICD recipient. To ameliorate the challenges, we propose the personalized computing framework for deep learning based VA detection on medical IoT systems. The system consists of intracardiac and surface rhythm monitors, and the cloud platform for data uploading, diagnosis, and CNN model personalization. We equip the system with real-time inference on both intracardiac and surface rhythm monitors. To improve the detection accuracy, we enable the monitors to detect VA collaboratively by proposing the cooperative inference. We also introduce the CNN personalization for each patient based on the computing framework to tackle the unlabeled and limited rhythm data problem. When compared with the traditional detection algorithm, the proposed method achieves comparable accuracy on VA rhythm detection and 6.6% reduction in inappropriate shock rate, while the average inference latency is kept at 71ms.

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