An Efficient Teager Energy Operator-Based Automated QRS Complex Detection

An innovative method based on Shannon energy envelope and summit navigation for detecting R peaks of noise stress test signals

In recent decades, there has been an increased demand for the processing of electrocardiogram (ECG) signals because of its significant role in diagnosing cardiac diseases. The QRS complex is the dominant feature of the ECG signal. The detection of QRS complexes is thus an essential part of almost any ECG signal processing systems. This paper presents a developed QRS complex detection method using dominant peak extraction and Shannon energy envelope for useful ECG signal analysis. The algorithm is divided into three main stages: pre-processing, searching for dominant peaks, and removing false R peaks. The proposed algorithm is validated in static ECG recordings from the MIT-BIH Arrhythmia Database (MITDB) and noise-contaminated ECG stress tests from the Glasgow University Database (GUDB), separately. The method compares favorably with conventional and recently published results of many QRS detection algorithms on the same MITDB. Subsequently, valuable performance coefficients are also found on the GUDB. The average detection accuracy of finding R peaks exceed 99% on both the databases, especially for cardiac stress test records with high interference levels. The method enables a highly effective ECG signal processing tool under various noises, artifacts, abnormalities, and morphologies.

Exploratory data analysis based efficient QRS-complex detection technique with minimal computational load

Detection of QRS-complex in the electrocardiogram (ECG) plays a decisive role in cardiac disorder detection. We face many challenges in terms of powerline interference, baseline drift, and abnormal varying peaks. In this work, we propose an exploratory data analysis (EDA) based efficient QRS-complex detection technique with minimal computational load. This paper includes median and moving average filter for pre-processing of the ECG. The peak of filtered ECG is enhanced to third power of the signal. The root mean square (rms) of the signal is estimated for the decision making rule. This technique adapted the new concept for isoelectric line identification and EDA based QRS-complex detection. In this paper, total 10,70,981 beats were used for validation from MIT BIH-Arrhythmia Database (MIT-BIH), Fantasia Database (FDB), European ST-T database (ESTD), a self recorded dataset (SDB), and fetal ECG database (FTDB). Overall sensitivity of 99.65 % and positive predictivity rate of 99.84 % have been achieved. The proposed technique doesn't require selection, setting, and training for QRS-complex detection. Thus, this paper presents a QRS-complex detection technique based on simple decision rules.

Real-Time Recording and Processing of Spike Electrical Activity of the Small Intestine in Experiments on Rats

Real-time recording technique and mathematical processing of the spike electrical activity in the small intestine were developed for chronic experiments on rats. Open-source software was employed to digitize electromyograms and to process them in a real-time mode with a fourth-order nonlinear differential energy operator. This method improved identification of spike electrical activity in the small intestine in experiments.