Abstract
Heartbeats are important aspects for the study of heart diseases in medical sciences as they provide vital information on heart disorders and abnormalities in heart rhythms. Each heartbeat provides a QRS complex in the electrocardiogram (ECG) which is centered at the R-peak. The analysis of ECG is hindered by low-frequency noise, high-frequency noise, interference from P and T waves, and changes in QRS morphology. This paper presents a new peak detection algorithm that can suppress the noise and adapt to changes in ECG signal morphology for better detection performance. The proposed algorithm is based on median and moving average (MA) filtering, segmentation, time and amplitude thresholds, and statistical false peak elimination (SFPE). The filters are first used in preprocessing to reduce unwanted noise and interference. The data is then divided into smaller segments and each segment is then analyzed using two distinct thresholds, a time axis (x-axis) threshold and an amplitude (y-axis) threshold. Next, the false peaks are eliminated resulting from any residue of noise using an average value of peak-to-peak interval. A post-processing stage is added to eliminate any peak that is detected twice and to search for missed low-amplitude peaks. The proposed method is tested on MIT-BIH arrhythmia and Fantasia databases and provides better results in comparison to several state-of-the-art methods in the field. The mean sensitivity, positive predictivity, and detection error rates for the proposed method are 99.82%, 99.88%, and 0.31%, respectively, for the MIT-BIH arrhythmia database and 99.92%, 99.90%, and 0.18%, respectively, for the Fantasia database.
Highlights
The electrocardiogram (ECG) is a non-invasive test [1] for the heart which is conducted by placing electrodes on the chest to record the electrical activity of the heart [2]
MATERIALS AND DATA The proposed method is evaluated on two distinct databases, namely, the MIT-BIH arrhythmia database [40], and the Fantasia database [41] from PhysioNet [42] which is an open source for physiological signals
As mentioned earlier the databases used were MIT-BIH arrhythmia and Fantasia databases with 48 records each with a duration of 30 minutes and 40 records each with a duration of 2 hours, respectively
Summary
The electrocardiogram (ECG) is a non-invasive test [1] for the heart which is conducted by placing electrodes on the chest to record the electrical activity of the heart [2]. The sources of noise are: Power-line interference, electrode contact noise, muscle contraction noise, motion artifacts, the noise produced from electronic devices, external electrical interference such as recording systems, baseline wander, and bodily sounds such as breathing, stomach, or bowels sounds [3] All these unwanted artifacts make the detection of R-peaks quite challenging to date and though algorithms have reached quite a high accuracy in recent times. Using wavelet transform requires several filters and that will make the process computationally challenging Another method that is quite effective in envelope detection is Hilbert transform (HT). Moving median filters were used in [27] and mean-median filtering along with DWT was used in [28] to remove the baseline wander This yields better results for noise cancellation than other methods.
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