Diagnosis of shockable rhythms for automated external defibrillators using a reliable support vector machine classifier

Publication date: July 2018 Source:Biomedical Signal Processing and Control, Volume 44 Author(s): Minh Tuan Nguyen, Ahsan Shahzad, Binh Van Nguyen, Kiseon Kim Sudden cardiac arrest is mainly caused by ventricular fibrillation and ventricular tachycardia, which are known as shockable rhythms. In this paper, a detection algorithm of shockable rhythms including support vector machine (SVM) model uses the public electrocardiogram (ECG) databases for training and testing. The databases are the Creighton University Ventricular Tachyarrhythmia Database (CUDB) and the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). At first, to compose a set of good features, we extend a well-known set of 2 good features such as Count2 and VF-filter Leakage Measure (Lk). We supplemented 5 more good features, selected based on a binary genetic algorithm-based feature selection, among 11 new input candidate features. All the combinations of 7 good features are estimated for their performance on the training and the testing data using the SVM models to identify 6 combinations of the final feature pool. 5-Folds cross validation is then implemented carefully to validate the performance of the SVM classifier using final feature pool on separated and entire 5s-segment databases. The final combination of 4 features, which includes Count2, Lk, Threshold Crossing Interval (TCI), and Centroid Frequency (CF), is addressed by the highest validation performance of the corresponding SVM model. The C...
Source: Biomedical Signal Processing and Control - Category: Biomedical Science Source Type: research