QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Sharma, Tanushree; Sharma, Kamalesh Kumar

doi:10.1080/03772063.2016.1221744

Cited by 23 publications

(14 citation statements)

References 36 publications

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“…With this assumption, there are 109,494 annotated beats in this database. This number is consistent with the results of other studies [17,18,19,20,21,22,23,24,25]. To be consistent with other studies of R-peaks detection, quantitative comparisons in terms of sensitivity (S), positive predictivity (P), and detection error (DER), as defined in Equations (1)–(3), are studied.…”

Section: Methodssupporting

confidence: 89%

Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Nguyen

Dinh

Bui

2019

Sensors

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A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

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Section: Methodssupporting

confidence: 89%

Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Nguyen

Dinh

Bui

2019

Sensors

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“…ECG is a method commonly used to detect the characteristics of respiratory and heartbeat. The study of ECG detection signals is mainly the study of QRS characteristics [10]. Despite the high accuracy of ECG detection results, there are still some problems that need to be solved.…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Detection of Vital Signs Using a UWB Radar Sensor

Duan

Liang

2019

IEEE Access

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The ultra-wide band (UWB) radar sensor is useful in the field of mission critical sensors and sensor networks due to its short detection time, high penetration, and low energy consumption. To support critical missions such as search and rescue, this paper designs a non-contact detection system based on a UWB radar sensor module to obtain vital signs of human beings. There are noise and clutter due to non-contact detection, and therefore the coherent background noise removal and moving target display filter are applied. Then, the variational mode decomposition (VMD) algorithm is used to extract heartbeat signals and respiratory signals. In addition, the Hilbert transform is applied to heartbeat signals and respiratory signals to obtain the time-frequency information. The electrocardiogram is also employed to compare the results of the UWB radar sensor. We also detect the human target through-the-wall. It turns out that the system can obtain respiratory and heartbeat characteristics simultaneously in one measurement, saving cost with high accuracy. INDEX TERMS Mission critical sensors, non-contact detection, variational mode decomposition, electrocardiogram, through-the-wall detection. ZHENZHEN DUAN received the B.S. degree from the University of Electronic Science and Technology of China, in 2016, where she is currently pursuing the M.S. degree with the Department of Information and Communication Engineering. Her research interests include ultra-wideband radar for human detection and clutter suppression. JING LIANG received the B.S. and M.S. degrees in electrical engineering from the Beijing University of Posts and Telecommunications, Beijing, China, in 2003 and 2006, respectively, and the Ph.D. degree in electrical engineering from the University of Texas at Arlington, in 2009. She is currently an Associate Professor with the Department of Electronic Engineering, University of Electronic Science and Technology of China. Her current research interests include radar sensor networks, collaborative and distributed signal processing, wireless communications, compressive sensing, wireless networks, and fuzzy logic systems.

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“…Sharma et al, in their paper (see: [ 79 ]), presented a development of a novel technique for QRS (the most distinctive feature in the ECG signal) detection. The QRS can serve as a starting point for various applications, such as, among others, the detection of other waves and segments, heart-rate calculation, derivation or respiration features.…”

Section: Electrocardiographymentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

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Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

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QRS Complex Detection in ECG Signals Using the Synchrosqueezed Wavelet Transform

Cited by 23 publications

References 36 publications

Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

Non-Contact Detection of Vital Signs Using a UWB Radar Sensor

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part I: Cardiac Signals

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