Efficient Arrhythmia Detection Using Progressive Resolution Shrinking

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term, at-home cardiac care is the ability to provide continuous monitoring, and detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Various big data and deep learning based approaches have been developed to analyze electrocardiogram data to identify arrhythmia conditions. However, most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow, which is critical for real-time detection. In this paper, we propose progressive resolution shrinking, a new method for supporting efficient execution of deep learning models for arrhythmia detection, without compromising the detection accuracy. Specifically, we explored multidimensional methods in reducing the amount of information needed for the learning task, and developed a new training method to leverage the advantage of reduced resolution. We have evaluated this approach using real electrocardiogram data, and the experimental results show that it effectively improves the efficiency of arrhythmia detection while preserving high accuracy. We expect this approach will pave the way for real-time arrhythmia detection on resource-constrained wearable devices.