Key result
The proposed real-time R and QRS detection method achieved a sensitivity of 99.80%, positive predictivity of 99.80%, and detection error rate of 0.36% against the MIT-BIH database.
Why the study?
Wearable ECG devices face various noise conditions that reduce the reliability of detected R points or QRS intervals, creating a need for efficient real-time ECG signal processing.
A novel real-time R and QRS detection algorithm using derivative and max filters demonstrates high accuracy (99.80% sensitivity) for processing noisy ECG signals in wearable devices.
May support real-time QRS detection in wearable ECGs; leaves open prospective clinical validation.
Recently, with the active development of wearable electrocardiogram (ECG) devices such as smart-bands or portable ECG devices, efficient ECG signal processing technology that can be applied in real-time has been actively studied. However, a wearable ECG device is exposed to various noise situations, thereby reducing the reliability of the detected R point or QRS interval. In addition, as early warning techniques in healthcare systems have been studied, real-time ECG signal processing techniques have become very important in wearable ECG devices. In this paper, we propose an efficient real-time R and QRS detection method using two kinds of first-order derivative filters and a max filter to analyze ECG signals measured from wearable ECG devices in real-time. The proposed method detects the R point and QRS interval in units of a sliding window for real-time processing and combines the detected R points in each sliding window. Also, the reliability of the detected R points and RR intervals is examined through noise region analysis using the histogram characteristic of a sample point. The performance of the proposed method was verified by the MIT-BIH database (DB), CYBHi DB and real ECG data measured from the developed wearable ECG patch. The proposed method achieves Se = 99.80%, +P = 99.80%, and DER = 0.36% against MIT-BIH DB. In addition, the proposed method enables accurate R point detection and heart rate variability (HRV) analysis even with noisy ECG signals.
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Bae et al. (2019) studied Electrocardiogram signal processing. Real-time R and QRS detection method using derivative filters and max filter was evaluated on Sensitivity (Se), Positive Predictivity (+P), and Detection Error Rate (DER) against MIT-BIH DB. The proposed real-time R and QRS detection method achieved a sensitivity of 99.80%, positive predictivity of 99.80%, and detection error rate of 0.36% against the MIT-BIH database.
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