A novel adaptive multilevel thresholding based algorithm for QRS detection

Abstract

The heart is the single most important organ of the human body. By studying the ECG signal any abnormalities in the heart’s tempo can be identified. The QRS complex is the most prominent feature of an ECG signal. However, the detection of this feature is hampered by the presence of high and low-frequency noises in the ECG signal and abrupt changes in the signal’s morphology. This paper presents a new method of QRS detection using advanced adaptive multilevel thresholding (AAMT) with selective statistical false peak elimination (SSFPE). Firstly, a band-pass filter is used to filter out most of the unwanted noise and interference to aid the detection operation. Then, AAMT is applied to the entire ECG record to find the location and amplitudes of the pseudo peaks. Next, SSFPE is employed to eliminate false peaks resulting from electromyogram (EMG) and any other high-frequency noise that has not been eliminated in the filtering stage. Finally, after most of the peaks are correctly identified a search back stage is included to find any low amplitude true peaks that might have been missed in the peak detection stage. The proposed method is tested on the MIT-BIH arrhythmia and Fantasia databases and shows high accuracy in detection compared to many state-of-the-art QRS detection methods. The method yields high sensitivity, positive predictivity, and a low detection error rate of 99.85%, 99.91%, and 0.25%, respectively, for the MIT-BIH arrhythmia and 99.98%, 99.90%, and 0.12%, respectively for the Fantasia database.

Keywords

• QRS detection
• Electrocardiogram
• Adaptive multilevel thresholding
• Statistical false peak elimination
• Segmentation