QRS detection based on wavelet coefficients

Electrocardiogram (ECG) signal processing and analysis provide crucial information about functional status of the heart. The QRS complex represents the most important component within the ECG signal. Its detection is the first step of all kinds of automatic feature extraction. QRS detector must be able to detect a large number of different QRS morphologies. This paper examines the use of wavelet detail coefficients for the accurate detection of different QRS morphologies in ECG. Our method is based on the power spectrum of QRS complexes in different energy levels since it differs from normal beats to abnormal ones. This property is used to discriminate between true beats (normal and abnormal) and false beats. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database (MITDB). The obtained results show a sensitivity of 99.64% and a positive predictivity of 99.82%. ⺠We make QRS detection using a single threshold obtained from wavelet coefficients. ⺠Power spectrum in different energy levels is used to calculate detector threshold. ⺠QRS energy is highest in the range (11.25–22.5 Hz) for normal beats. ⺠QRS energy is highest in the range (6.62–11.25 Hz) for ventricular extra-systoles. ⺠Effect of artifacts, T and P waves are attenuated using wavelet detail coefficients.