SIViP (2011) 5:1–10 DOI 10.1007/s11760-009-0136-1 ORIGINAL PAPER A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram Muhammad Abdullah Arafat · Abdul Wadud Chowdhury · Md. Kamrul Hasan Received: 16 May 2009 / Revised: 2 September 2009 / Accepted: 9 September 2009 / Published online: 30 September 2009 © Springer-Verlag London Limited 2009 Abstract Ventricular fibrillation (VF) is the most serious variety of arrhythmia which requires quick and accurate detection to save lives. In this paper, we propose a new time domain algorithm, called threshold crossing sample count (TCSC), which is an improved version of the threshold cross- ing interval (TCI) algorithm for VF detection. The algorithm is based on an important feature of the VF signal which relies on the random behavior of the electrical heart vector. By two simple operations: comparison and count, the technique cal- culates an effective measure which is used to separate life- threatening VF from other heart rhythms. For assessment of the performance of the algorithm, the method is applied on the complete MIT-BIH arrhythmia and CU databases, and a promising good performance is observed. Seven other classical and new VF detection algorithms, including TCI, have been simulated and comparative performance results in terms of different quality parameters are presented. The TCSC algorithm yields the highest value of the area under the receiver operating characteristic curve (AUC). The new algorithm shows strong potential to be applied in clinical applications for faster and accurate detection of VF. Keywords Ventricular fibrillation · Normal sinus rhythm · Isoelectric level · Threshold crossing M. A. Arafat · Md. K. Hasan (B ) Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh e-mail: khasan@eee.buet.ac.bd M. A. Arafat e-mail: abdullah_arafat_eee@yahoo.com A. W. Chowdhury Department of Cardiology, Dhaka Medical College, Dhaka, Bangladesh 1 Introduction Ventricular fibrillation (VF) is a life-threatening cardiac arrhythmia. Patients experiencing VF require a high-ampli- tude current impulse to the heart in order to restore normal rhythm. If left untreated, VF can lead to death within min- utes. Moreover, if a normal sinus rhythm (NSR) is misinter- preted as VF, leading to delivering of an unnecessary shock, it can damage the heart, causing fatal consequences to the patient. Therefore, correct and prompt detection of VF is of great importance. A wide variety of methods have been developed for this purpose, such as complexity measure [1], probability density function method [2], rate and irregularity analysis [3, 4], analysis of peaks in the short-term autocor- relation function [5], sequential hypothesis testing algorithm [6]–[8], correlation waveform analysis [9], four fast template matching algorithms [10], VF-filter method [11]–[13], spec- tral analysis [14], time–frequency analysis [15] and nonlinear descriptor using Hurst index [16]. When tested on a wide vari- ety of data, most of these methods show poor performance. Some are too complex to be implemented in real time appli- cations. An automatic external defibrillator (AED) should be able to differentiate, automatically, quickly and reliably, between shockable VF and other nonshockable arrhythmias. In [17], the performance of several classical old and new VF detection algorithms were compared in a standardized way. Most of the algorithms did not reveal the required performance. Two methods, namely Hilbert transform (HILB) [18] and phase space reconstruction (PSR) [19] proposed recently, demon- strated a better performance than the other previous methods. Current trend of the research is to develop new methods that are fast, simple and more accurate than others. In this paper, we propose a new ventricular fibrillation detection algorithm, called threshold crossing sample count 123