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. 2020 Oct 29;20(21):6144.
doi: 10.3390/s20216144.

An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis

Tae Wuk Bae  1 , Sang Hag Lee  2 , Kee Koo Kwon  1
Affiliations

An Adaptive Median Filter Based on Sampling Rate for R-Peak Detection and Major-Arrhythmia Analysis

Tae Wuk Bae et al. Sensors (Basel). .

Abstract

With the advancement of the Internet of Medical Things technology, many vital sign-sensing devices are being developed. Among the diverse healthcare devices, portable electrocardiogram (ECG) measuring devices are being developed most actively with the recent development of sensor technology. These ECG measuring devices use different sampling rates according to the hardware conditions, which is the first variable to consider in the development of ECG analysis technology. Herein, we propose an R-point detection method using an adaptive median filter based on the sampling rate and analyze major arrhythmias using the signal characteristics. First, the sliding window and median filter size are determined according to the set sampling rate, and a wider median filter is applied to the QRS section with high variance within the sliding window. Then, the R point is detected by subtracting the filtered signal from the original signal. Methods for detecting major arrhythmias using the detected R point are proposed. Different types of ECG signals were used for a simulation, including ECG signals from the MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database, signals generated by a simulator, and actual measured signals with different sampling rates. The experimental results indicated the effectiveness of the proposed R-point detection method and arrhythmia analysis technique.

Keywords: R peak; adaptive median; arrhythmia; heartrate variability; sampling rate.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Simulation environment.
Figure 2
Figure 2
Relationship of variance and filter size: an example of 250 samples/s.
Figure 3
Figure 3
Difference comparison of the original signal and the filter result using (a) the normal median filter and (b) the adaptive median filter.
Figure 4
Figure 4
Sampling rate-based sliding-window structure; (a) variable configuration of the sliding window based on the sampling rate and (b) detection structure for various arrhythmias.
Figure 5
Figure 5
Pseudocode for the detection of various arrhythmias.
Figure 6
Figure 6
Features of (a) bigeminy and (b) trigeminy.
Figure 7
Figure 7
Features of (a) PVC, (b) VT, and (c) SVT.
Figure 8
Figure 8
Five features for AF detection.
Figure 9
Figure 9
Detected R peaks for MITDB Records (a) 100, (b) 104, (c) 105, and (d) 108.
Figure 10
Figure 10
Computation time per 1000 iterations for 11 s of Records (a) 100N112, (b) 104/328, (c) 105N364, and (d) 108N270.
Figure 11
Figure 11
R-peak detection results for (a) bigeminy, (b) trigeminy, (c) MITDB 114 including PVC, (d) VT, and (e) SVT.
Figure 12
Figure 12
Comparison of BPM, RR interval, RR histogram, PSD, and poincaré plot for MITDB Records (100, 104, 105, and 108), bigeminy, trigeminy, mitdb 114 including PVC, VT, and SVT.
Figure 13
Figure 13
Test signals of AFDB used for AF detection.
Figure 13
Figure 13
Test signals of AFDB used for AF detection.
Figure 14
Figure 14
Visualization of test AF signals in in AFDB.
See this image and copyright information in PMC

References

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