A Fast and Accurate Myocardial Infarction Detector
A Fast and Accurate Myocardial Infarction Detector
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DOI：
10.1109/csci51800.2020.00147
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发表时间：
2020-12
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通讯作者：
Harold Martin;Walter Izquierdo;Ulyana Morar;M. Cabrerizo;A. Cabrera;M. Adjouadi
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作者：
Harold Martin;Walter Izquierdo;Ulyana Morar;M. Cabrerizo;A. Cabrera;M. Adjouadi
我们提出了一种新的管道，用于从12导联心电图的单次心跳实时检测心肌梗死。我们通过将实时R尖峰检测算法与基于深度学习长短期记忆（LSTM）网络的分类器合并来实现。的分类性能的比较评估所得到的系统进行和提供。所提出的算法实现了患者间分类准确率为95.76%（95%置信区间（CI）为±2.4%），召回率为96.67%（±2.4% 95% CI），特异性为93.64%（±5.7% 95% CI），平均J-Score为90.31%（±6.2% 95% CI）。这些最先进的心肌梗死检测指标非常有前途，可以为心肌梗死的早期检测铺平道路。这种高精度是在40毫秒的处理时间内实现的，这最适合在线分类，因为快速心跳之间的时间约为300毫秒。
We propose a novel pipeline for the real-time detection of myocardial infarction from a single heartbeat of a 12-lead electrocardiograms. We do so by merging a real-time R-spike detection algorithm with a deep learning Long-Short Term Memory (LSTM) network-based classifier. A comparative assessment of the classification performance of the resulting system is performed and provided. The proposed algorithm achieves an inter-patient classification accuracy of 95.76% (with a 95% Confidence Interval (CI) of ±2.4%), a recall of 96.67% (±2.4% 95% CI), specificity of 93.64% (±5.7% 95% CI), and the average J-Score is 90.31% (±6.2% 95% CI). These state-of-the-art myocardial infarction detection metrics are extremely promising and could pave the wave for the early detection of myocardial infarctions. This high accuracy is achieved with a processing time of 40 milliseconds, which is most appropriate for online classification as the time between fast heartbeats is around 300 milliseconds.