Automatic Detection of the R Peaks in Single-Lead ECG Signal

Sabherwal, Pooja; Agrawal, Monika; Singh, Latika

doi:10.1007/s00034-017-0537-2

Cited by 45 publications

(26 citation statements)

References 15 publications

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“…Similar QRS detection techniques based on matched filters were studied in [34,35,36,37,38,39,40,41,42]. A QRS complex is created when the ventricles depolarize prior to their contraction [3]. In addition, the QRS complex has the largest amplitude and a sharp upward slope of any ECG signal.…”

Section: The Proposed R-peak Detection Algorithmmentioning

confidence: 99%

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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: The Proposed R-peak Detection Algorithmmentioning

confidence: 99%

“…The automatic detection of R-peaks is considered a classic ECG signal processing problem and has been extensively investigated. Recently, there were several notable studies [3,4,5,6,7,8,9,10]. In the work by the authors of [3], the combination of wavelet transform, derivatives, Hilbert transform, and adaptive thresholding was proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

Dinh

Bui

2019

Sensors

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“…This transform is complementary with wavelet transform for the task of wave detection. Sabherwal et al [70] have used the DWT to denoise the ECG signals, and then adopted the derivatives of the reconstructed signals to enhance the QRS complexes, and finally employed the HT with the peak-finding logic for R peak detection. Rakshit and Das [71] have utilized the DWT and the ShE Transform (ShET) to attenuate the noises and accentuate the QRS complexes in the ECG signals, and then leveraged the peak-finding logic based on the HT to detect the R peaks.…”

Section: E: Hilbert Transform Can Cooperate With Wavelet Transform Tomentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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“…Several QRS complex detection techniques have been reported in the recent literature. These include quadratic filter, level crossing sampling‐based analog to digital conversion logic, integrate and fire sampling, least mean square algorithm‐based adaptive linear predictor, empirical mode decomposition (EMD), multiscale mathematical morphology (MM), sigmoidal radial basis function artificial neural network (ANN), max‐min difference (MMD) algorithm, filter banks (FBs), quadratic spline wavelet transform (WT), daubechies (db10) WT, Harr WT, wavelet filter bank, digital filtering with dynamic threshold, combination of WT, derivative, and Hilbert transform (HT), adaptive MM, phase space reconstruction and box‐scoring calculation, ECG structural analysis (SA), relative energy (RE), parallel delta modulator (PDM), modified S‐transform (ST), and deterministic finite automata (DFA) …”

Section: Introductionmentioning

confidence: 99%

“…The Shannon energy and Hilbert transform‐based methods detect several false peaks for long pause ECG signals . The QRS detection accuracy of the methods reported in the literature is very poor, which reduces the diagnostic correctness and operational reliability, whereas, the high accuracy methods employ costly signal processing operations.…”

Section: Introductionmentioning

confidence: 99%

Optimal SSA‐based wideband digital differentiator design for cardiac QRS complex detection application

Nayak

Saha

Kar

et al. 2018

Int J Numerical Modelling

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In this paper, a computationally efficient, highly accurate, wideband, stable, and minimum phase infinite impulse response type first-order digital differentiator (DD) is designed by employing a swarm intelligence-based search method called Salp Swarm Algorithm (SSA) for the QRS complex detection application. The optimal coefficients of the DD are computed by minimizing a suitable fitness function to meet the ideal differentiator magnitude response characteristics.The simulation results and the root mean square magnitude error metric justify the superiority of the proposed SSA-based DD design as compared with all other differentiators employed in the QRS complex detection application, and the reported first-order DDs based on the numerical methods and the other evolutionary algorithms. The electrocardiogram signal is preprocessed by the proposed DD to generate the feature signal corresponding to each QRS complex.The generated feature signal is used as a marker to identify the exact occurrence of the QRS complex by using an adaptive threshold-based detection logic. The proposed DD-based QRS detection approach achieves a sensitivity (Se), positive prediction (PP), detection error rate (DER), and accuracy (Acc) of 99.94%, 99.93%, 0.1279%, and 99.87%, respectively, when validated against MIT/BIH arrhythmia database. Also, against the QT database, the proposed QRS detector produces a Se of 99.93%, PP of 99.97%, DER of 0.09%, and Acc of 99.90%. The performance of the proposed QRS detection technique is compared with the methods already reported in the recent literature, and the superiority of the proposed approach is established with respect to different standard performance metrics. The noise tolerance capability of the proposed QRS detector is demonstrated against MIT/BIH noise stress test database. KEYWORDS digital differentiator (DD), electrocardiogram (ECG), QRS complex detection, Salp swarm algorithm (SSA) /journal/jnm 1 of 25sinus rhythm. Any deviation from the standard sinus rhythm, called arrhythmia, can be detected by close observation of the shape and the interval duration of the waves. Hence, from a diagnosis point of view, the calculation of the wave interval is essential and can be determined by accurately identifying the time of occurrence of the R-wave/R-peak (peak of the QRS wave). From the appearance of the R-wave, the R-R interval duration can be estimated. Also, by knowing the occurrence of the R-waves, several post-processing applications like heartbeat classification, foetal heart rate monitoring, ECG coder, and heart rate variability analysis can be implemented. The time of occurrences of the R-waves is considered as markers to identify the time of the presence of the P/T-waves, QRS interval, PR interval, QT interval, ST segment, and PQ segment. 1 This information plays a pivotal role to diagnose various cardiac diseases. With the accurate estimation of the QT interval and the heart rate, a potentially dangerous disease called hypoglycaemia (low blood glucose level) can be monitored noninvasively...

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Automatic Detection of the R Peaks in Single-Lead ECG Signal

Cited by 45 publications

References 15 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

Wavelets for Electrocardiogram: Overview and Taxonomy

Optimal SSA‐based wideband digital differentiator design for cardiac QRS complex detection application

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