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• W2576164115 abstract "To lessen the rate of false critical arrhythmia alarms, we used robust heart rate estimation and cost-sensitive support vector machines. The PhysioNet MIMIC II database and the 2015 PhysioNet/CinC Challenge public database were used as the training dataset; the 2015 Challenge hidden dataset was for testing. Each record had an alarm labeled with asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia or ventricular flutter/fibrillation. Before alarm onsets, 300 s multimodal data was provided, including electrocardiogram, arterial blood pressure and/or photoplethysmogram. A signal quality modified Kalman filter achieved robust heart rate estimation. Based on this, we extracted heart rate variability features and statistical ECG features. Next, we applied a genetic algorithm (GA) to select the optimal feature combination. Finally, considering the high cost of classifying a true arrhythmia as false, we selected cost-sensitive support vector machines (CSSVMs) to classify alarms. Evaluation on the test dataset showed the overall true positive rate was 95%, and the true negative rate was 85%." @default.
• W2576164115 created "2017-01-26" @default.
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• W2576164115 date "2017-01-18" @default.
• W2576164115 modified "2023-09-23" @default.
• W2576164115 title "Reducing false arrhythmia alarm rates using robust heart rate estimation and cost-sensitive support vector machines" @default.
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• W2576164115 doi "https://doi.org/10.1088/1361-6579/38/2/259" @default.
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