A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram

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 crossing 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 calculates an effective measure which is used to separate lifethreatening 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.

Development of an automated vehicle stop system for cardiac emergencies

“…The threshold crossing sample count (TCSC) [24] was implemented for the VF detection. Considering the safeness of the demonstration, the steer-sensor and the driver-monitoring camera were settled on the passenger seat as shown on the Figure 5.…”

Detection of Ventricular Fibrillation Using Random Forest Classifier

“…Decision is made on every L e -second ECG episode (L e > 3) by averaging L e 2 consecutive values of N obtained from L e 2 consecutive 3-s data segments with 1-s step. As explained in [21], the value of V o is suitably selected to be 0.2 after a number of empirical studies. It must be noted that TCSC is different from Count1, Count2 and Count3 as they represent the number of samples in certain amplitude ranges in a signal filtered though a band pass filter with bandwidth 13 Hz to 16.5 Hz.…”

Structured prediction for differentiating between normal rhythms, ventricular tachycardia, and ventricular fibrillation in the ECG

“…3) Threshold crossing sample count [16] is an improvement to the threshold crossing interval [6] transformation, obtained by counting number of samples above the absolute value of an adaptive threshold. The segment x is multiplied with a Tukey window where the centre half of the window is constant, and the remainder tapers to 0.…”