With aim of reducing the incidence of false critical arrhythmia alarms in intensive care units, a novel data fusion and machine learning algorithm is presented in this article. The 2015 PhysioNet/Computing in Cardiology Challenge database was used in this present algorithm, with each grouped as an asystole (AS), extreme bradycardia (EB), extreme tachycardia (ET), ventricular tachycardia (VT) or ventricular flutter/fibrillation (VF) arrhythmia alarm. A 10-second segment before the onset of the alarm was truncated from available signals, namely electrocardiogram (ECG), arterial blood pressure (ABP), and/or photoplethysmogram (PPG). By first assessing signal quality of available signals, a robust estimation of beat-to-beat intervals could then be derived. Features in heart rate variability (HRV) analysis and ECG parameters such as temporal statistical parameters, spectral analysis results, wavelet transformation coefficients, and complexity measurement etc were extracted and formed a vector. After feature selection through genetic algorithm (GA), a support vector machine (SVM) model was applied to conduct the classification of alarms for the specific arrhythmia type. The overall true positive rate (TPR) of classification algorithm is 93%, with the true negative rate (TNR) 94%. According to the method of performance evaluation in the 2015 Challenge, this algorithm achieved a gross score of 84.4.
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机译：为了减少重症监护室中错误的严重心律失常警报的发生，本文提出了一种新颖的数据融合和机器学习算法。在本算法中使用了2015 PhysioNet / Computing in Cardiology Challenge数据库，每个分组为：心搏停止（AS），极端心动过缓（EB），极端心动过速（ET），室性心动过速（VT）或室扑/颤动（VF） ）心律不齐报警。从可用信号（即心电图（ECG），动脉血压（ABP）和/或光电容积描记图（PPG））中将警报开始之前的10秒钟截断。通过首先评估可用信号的信号质量，然后可以得出拍频间隔的可靠估计。提取心率变异性（HRV）分析和ECG参数的特征，例如时间统计参数，频谱分析结果，小波变换系数和复杂性测量等，并形成一个向量。通过遗传算法（GA）选择特征后，使用支持向量机（SVM）模型对特定心律失常类型的警报进行分类。分类算法的总真实阳性率（TPR）为93％，真实阴性率（TNR）为94％。根据2015年挑战赛中的绩效评估方法，该算法获得了84.4的总得分。
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