Abstract
Early warning and detection of ventricular fibrillation is crucial to the successful treatment of this life-threatening condition. In this paper, a ventricular fibrillation classification algorithm using a machine learning method, random forest, is proposed. A total of 17 previously defined ECG feature metrics were extracted from fixed length segments of the echocardiogram (ECG). Three annotated public domain ECG databases (Creighton University Ventricular Tachycardia database, MIT-BIH Arrhythmia Database and MIT-BIH Malignant Ventricular Arrhythmia Database) were used for evaluation of the proposed method. Window sizes 3 s, 5 s and 8 s for overlapping and non-overlapping segmentation methodologies were tested. An accuracy (Acc) of 97.17%, sensitivity (Se) of 95.17% and specificity (Sp) of 97.32% were obtained with 8 s window size for overlapping segments. The results were benchmarked against recent reported results and were found to outper-form them with lower complexity.
Highlights
Wearable health-monitoring systems have attracted much attention due to their high potential in healthcare as cost-effective solutions for real-time healthcare monitoring, early detection of diseases, and improving treatment of various medical conditions [1]
The study in this paper aims to build a high-performance Ventricular Fibrillation (VF) detector by combining 17 previously defined ECG parameters using random forest classifier
The number of decision trees (T), number of attributes selected in random selection (m) and depth of decision trees (d) were varied to optimize the classifier
Summary
Wearable health-monitoring systems have attracted much attention due to their high potential in healthcare as cost-effective solutions for real-time healthcare monitoring, early detection of diseases, and improving treatment of various medical conditions [1].
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