Verification and comparison of MIT-BIH arrhythmia database based on number of beats

<span lang="EN-US">The ECG signal processing methods are tested and evaluated based on many databases. The most ECG database used for many researchers is the MIT-BIH arrhythmia database. The QRS-detection algorithms are essential for ECG analyses to detect the beats for the ECG signal. There is no standard number of beats for this database that are used from numerous researches. Different beat numbers are calculated for the researchers depending on the difference in understanding the annotation file. In this paper, the beat numbers for existing methods are studied and compared to find the correct beat number that should be used. We propose a simple function to standardize the beats number for any ECG PhysioNet database to improve the waveform database toolbox (WFDB) for the MATLAB program. This function is based on the annotation's description from the databases and can be added to the Toolbox. The function is removed the non-beats annotation without any errors. The results show a high percentage of 71% from the reviewed methods used an incorrect number of beats for this database.</span>

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Comparative analysis of ECG signal processing methods in the time-frequency domain

2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) ◽

10.1109/eiconrus.2018.8317272 ◽

2018 ◽

Cited By ~ 3

Author(s):

Yeldos A. Altay ◽

Artem S. Kremlev

Keyword(s):

Signal Processing ◽

Comparative Analysis ◽

Frequency Domain ◽

Ecg Signal ◽

Processing Methods ◽

Time Frequency ◽

Ecg Signal Processing ◽

Signal Processing Methods

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Comparison of malfunction diagnosis sensibility for direct and inverse ECG signal processing methods

Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology ◽

10.1109/iembs.2002.1134474 ◽

2003 ◽

Author(s):

S.M. Szilagyi ◽

Z. Benyo ◽

L. Szilagyi

Keyword(s):

Signal Processing ◽

Ecg Signal ◽

Processing Methods ◽

Ecg Signal Processing ◽

Malfunction Diagnosis ◽

Signal Processing Methods

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Efficient Real-Time R and QRS Detection Method Using a Pair of Derivative Filters and Max Filter for Portable ECG Device

Applied Sciences ◽

10.3390/app9194128 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4128

Author(s):

Tae Wuk Bae ◽

Kee Koo Kwon

Keyword(s):

Signal Processing ◽

Real Time ◽

Detection Method ◽

Sliding Window ◽

Ecg Signal ◽

Qrs Detection ◽

Real Time Processing ◽

Ecg Signals ◽

Ecg Signal Processing ◽

Processing Techniques

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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