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Evaluating Deep Learning Algorithms for Real-Time Arrhythmia Detection

机译：评估实时心律失常检测的深层学习算法

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摘要

Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both 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 monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. In this paper, we present our work in designing real-time sensing, and evaluating machine learning algorithms for real-time arrhythmia detection. Most of the existing work applies machine learning algorithms to electrocardiogram (ECG) images to detect abnormal patterns. These approaches are not suitable for real-time processing due to high processing overhead. In our work, we treat data as time series, and evaluate various machine learning algorithms in terms of both learning and computational performance. Our experimental results show that the long short-term memory network (LSTM) has both high accuracy and efficiency, demonstrating great potential for online detection of arrhythmia.
机译：心血管疾病，如心脏病发作和充血性心力衰竭，是美国和全球的主要死因。目前用于诊断心血管疾病的医疗实践不适合长期休息室使用。长期监测的关键是实时检测心脏节律，即心律失常的能力。在本文中，我们在设计实时感测和评估机器学习算法方面的工作，以实现实时心律失常检测。大多数现有工作将机器学习算法应用于心电图（ECG）图像以检测异常模式。由于高处理开销，这些方法不适合实时处理。在我们的工作中，我们将数据视为时间序列，并根据学习和计算性能评估各种机器学习算法。我们的实验结果表明，长期短期内存网络（LSTM）具有高精度和效率，展示了在线检测心律失常的巨大潜力。

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