A new method for QRS detection in ECG signals using QRS-preserving filtering techniques

Abstract

Detection of QRS complexes in ECG signals is required for various purposes such as determination of heart rate, feature extraction and classification. The problem of automatic QRS detection in ECG signals is complicated by the presence of noise spectrally overlapping with the QRS frequency range. As a solution to this problem, we propose the use of least-squares-optimisation-based smoothing techniques that suppress the noise peaks in the ECG while preserving the QRS complexes. We also propose a novel nonlinear transformation technique that is applied after the smoothing operations, which equalises the QRS amplitudes without boosting the supressed noise peaks. After these preprocessing operations, the R-peaks can finally be detected with high accuracy. The proposed technique has a low computational load and, therefore, it can be used for real-time QRS detection in a wearable device such as a Holter monitor or for fast offline QRS detection. The offline and real-time versions of the proposed technique have been evaluated on the standard MIT-BIH database. The offline implementation is found to perform better than state-of-the-art techniques based on wavelet transforms, empirical mode decomposition, etc. and the real-time implementation also shows improved performance over existing real-time QRS detection techniques.

Less computational approach to detect QRS complexes in ECG rhythms

“…This filtering also discards fallacious signals caused by T waves and noise. A filter design constrains when using a constant threshold function is the procedure accuracy, especially with signals containing a large admixture of EMG noise or artifacts of motion [19]. In the second stage, the threshold function is used.…”

Less computational approach to detect QRS complexes in ECG rhythms

“…This filtering also discards fallacious signals caused by T waves and noise. A filter design constrain when using a constant threshold function is the procedure accuracy, especially with signals containing a large admixture of EMG noise or artifacts of motion [19]. In the second stage, the threshold function is used.…”