BackgroundAccurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach.Methods and ResultsThis study involves a total of 953 independent life‐threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid‐ convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5‐fold cross‐validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability.ConclusionsOur method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.

中文翻译：

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使用混合卷积神经网络的实时心律失常检测

背景在重症监护病房 (ICU) 中准确检测心律失常事件对于提供及时护理至关重要。然而，传统的 ICU 监护仪会产生很高的误报率，从而导致警报疲劳。在这项工作中，我们使用深度学习方法开发了一种算法来改善 ICU 中危及生命的心律失常检测。方法和结果本研究涉及 410 名患者的 ICU 床边监测器产生的总共 953 个独立的危及生命的心律失常警报。具体来说，我们使用心电图（4 通道）、动脉血压和光电容积描记器信号来准确检测各种心律失常的开始和消失，而无需事先了解警报类型。我们使用基于混合卷积神经网络的分类器，将传统手工特征与使用卷积神经网络自动学习的特征融合在一起。此外，所提出的架构仍然可以灵活地适应各种心律失常状况以及多种生理信号。与仅使用卷积神经网络的方法相比，我们的混合卷积神经网络方法实现了卓越的性能。我们使用 5 倍交叉验证对我们的算法进行了 5 次评估，获得了 87.5%±0.5% 的准确率和 81%±0.9% 的分数。在公开的 PhysioNet 2015 Challenge 数据库上对我们的算法进行的独立评估导致总体分类准确率和得分分别为 93.9% 和 84.3%，表明其有效性和普遍性。结论我们的方法准确地检测出多种心律失常情况。我们算法的适当翻译可以通过减少误报的负担来显着提高 ICU 的护理质量。