QRS Wave Detection Research Articles

A Stochastic Resonance Electrocardiogram Enhancement Algorithm for Robust QRS Detection.

This study presents a new QRS detection algorithm making use of the background noise that is inevitably present in electrocardiogram (ECG) recordings. The algorithm suppresses noise, enhances the QRS-waves, and applies a threshold for QRS detection. Noise suppression and QRS enhancement are performed by a band-pass filter stage followed by a nonlinear stage based on the interaction of a particle inside an underdamped monostable potential well. The nonlinear stage maximizes the output when there is a QRS-wave and minimizes the output otherwise. One of the instruments that the nonlinear stage uses to enhance the QRS-waves is stochastic resonance, where the output is maximized for a non-zero intensity background noise. In terms of QRS-wave detection F1 score, which ranges from 98.87% to 99.99% on four major benchmarking databases (MIT-BIH Arrhythmia, QT, European ST-T, and MIT-BIH Noise Stress Test), the algorithm outperforms all existing ECG processing algorithms. The study, for the first time, demonstrates QRS-enhancement by facilitating stochastic resonance while suppressing in-band noise of ECG signals. Detecting QRS-waves as the ECG data streams, having a complexity of O(n), and not requiring any training data make the algorithm convenient for real-time ECG monitoring applications with limited computational resources.

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

T-Wave Alternans Detection Analysis and Software Design

ECG machine, currently on the market are generally not a function of the T-wave alternans detection analysis. Studies have shown that T-wave alternans can accurately predict sudden cardiac death, higher predictive accuracy in the case of combination of left ventricular ejection fraction (Left Ventricular Ejection Fraction, LVEF). This paper presents the design of function modules of T-wave alternans software based on the QT environment. Focus on the data processing module of the software, such as the QRS wave detection, T wave detection, the matrix of the T-wave extraction, T-wave alternans detection.

Design and Realization of Signal Processing Platform of Multi-Parameter Wearable Medical Devices

This paper presents a design of new type of multi-parameter wearable medical devices signal processing platform. The signal processing algorithm has a QRS-wave detection algorithm based on LADT, wavelet transformation and threshold detection with TMS320VC5509 DSP system. The DSP can greatly increase the speed of QRS-wave detection, and the results can be practical used for multi-parameter wearable device detection of abnormal ECG.

The Study and Design Of a Wireless ECG Monitoring System

This paper describes a research project on wireless electrocardiogram (ECG) monitoring systems. A detection and measurement processor designed by a MSP430 microcontroller accomplishes the analog-to-digital conversion, digital filtering, QRS wave detection, and heart rate calculation. The data of detection can be sent to the central controller and personal computer (PC) by wireless on-chip MG2455 through a ZigBee network. This design can be used widely in home healthcare, community healthcare, and sports training, as well as in healthcare facilities, due to its characteristics of low power consumption, small size, and reliability.