Why the study?
Does a machine learning approach using robust heart rate estimation and cost-sensitive support vector machines improve the classification of true versus false critical arrhythmia alarms?
Does a machine learning approach using robust heart rate estimation and cost-sensitive support vector machines improve the classification of true versus false critical arrhythmia alarms?
A machine learning approach using cost-sensitive support vector machines and robust heart rate estimation achieved high accuracy in distinguishing true from false critical arrhythmia alarms.
Answers are grounded in this paper and cite their sources.
May reduce false arrhythmia alarms in critical care; leaves open prospective clinical validation before adoption.
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%.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2017) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: