title: Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices.
abstract: In this Letter, the authors propose an efficient and robust method for automatically determining the VT and VF events in the electrocardiogram (ECG) signal. The proposed method consists of: (i) discrete cosine transform (DCT)-based noise suppression; (ii) addition of bipolar sequence of amplitudes with alternating polarity; (iii) zero-crossing rate (ZCR) estimation-based VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination. The proposed method is evaluated using 18,000 episodes of different ECG arrhythmias taken from 6 PhysioNet databases. The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature. Results show that the method achieves a Se of 100%, Sp of 99.70% and OA of 99.85% for discriminating VT from VF episodes using PPI features extracted from the processed signal. The robustness of the method is tested using different kinds of ECG beats and various types of noises including the baseline wanders, powerline interference and muscle artefacts. Results demonstrate that the proposed method with the ZCR, PPI features can achieve significantly better detection rates as compared with the existing methods.
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: Eedara
authors.initials: E
authors.lastname: Prabhakararao
authors.affiliation: School of Electrical Sciences , Indian Institute of Technology Bhubaneswar , Bhubaneswar, Odisha 751013 , India.
authors.forename: M Sabarimalai
authors.initials: MS
authors.lastname: Manikandan
keywords: ECG; ECG arrhythmias; PhysioNet databases; VTVF detection; automatic external defibrillator; baseline wanders; discrete cosine transform; discrete cosine transforms; diseases; electrocardiogram; electrocardiography; feature extraction; fibrillation detection method; medical signal processing; muscle; muscle artefacts; noise suppression; patient monitoring; peak-to-peak interval; powerline interference; rapid ventricular tachycardia; signal denoising; ventricular tachycardia; wearable cardiac health monitoring devices; zero-crossing rate estimation