An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal

Nayak, Chandan; Saha, Suman Kumar; Kar, Rajib; Mandal, Durbadal

doi:10.1016/j.bspc.2018.09.005

Cited by 38 publications

(12 citation statements)

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“…Step 5. If (t c /T max ) < rand, update the location of manta rays using ( 12) and ( 13), else, amend the position of manta rays using (14). It is noteworthy that ( 13) is used to perform an extensive local search, whereas, ( 14) promotes an exhaustive global search mechanism of the MRFO algorithm.…”

Section: A Manta-ray Foraging Optimization (Mrfo)mentioning

confidence: 99%

“…Out of various R-peak detection techniques reported in [2]- [13], the differentiator based R-peak detection approach is more prevalently employed in real-time QRS complex detection methods, due to its benefits like (i) computational efficiency, and (ii) independent of algorithm's training, patient, and manual segmentation of ECG signal [1]. In the reported differentiator based QRS detection approaches, both integer order differentiator (IOD) [14]- [16] and fractional order differentiator (FOD) [17]- [19] have been employed to generate QRS related feature signal. The produced QRS feature signal is the input to the peak detection block.…”

Section: Introductionmentioning

confidence: 99%

“…The IOD is regarded as a particular case of FOD for p = 1. Thus, compared to IOD based QRS complex detectors [14]- [16], superior QRS complex detection performance is obtained for the fractional-order digital differentiator (FODD) based QRS detector [19].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Design of Zero-Phase Riesz Fractional Order Digital Differentiator Using Manta-Ray Foraging Optimisation for Precise Electrocardiogram QRS Detection

Nayak

Saha

Kar

et al. 2020

IEEE Open J. Circuits Syst.

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This article introduces a new design of an optimised zero-phase response fractional order digital differentiator (FODD) called Riesz FODD of half order by employing a recently developed Manta-Ray Foraging Optimisation (MRFO) for improved and precise electrocardiogram QRS detection. A new weighted cost function is developed that precisely considers the characteristics of an ideal Riesz fractionalorder differentiator. Simulation tests of the rational approximations of the Riesz FODD based on the MRFO algorithm ensure close correspondence with the theoretical zero-phase and near-ideal magnitude responses and sensibly supersede the Least-squares method, Convex optimisation, Flower Pollination Algorithm, and Artificial Bee Colony based Riesz FODD designs. The Zero-phase response of the proposed Riesz FODD assists in preserving the features of the differentiated ECG precisely at the same time instant where they occur in the unfiltered signal. Additionally, the optimal magnitude characteristics of the proposed Riesz FODD help to obtain highly accurate differentiated ECG. These two indigenous properties of the proposed Riesz FODD accommodate the QRS detector to precisely identify the R-peak location and thus sensibly escalate the QRS detection accuracy when tested against several real electrocardiogram (ECG) signals of the PhysioNet ECG repository.

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Section: A Manta-ray Foraging Optimization (Mrfo)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Design of Zero-Phase Riesz Fractional Order Digital Differentiator Using Manta-Ray Foraging Optimisation for Precise Electrocardiogram QRS Detection

Nayak

Saha

Kar

et al. 2020

IEEE Open J. Circuits Syst.

Self Cite

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“…In Refs. [ 6 , 7 ], the Hilbert transform with an adaptive thresholding technique was utilized to detect R-peaks. Some threshold-based techniques with other criteria have also been used to specify the threshold.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the state-of-the-art methods for R-peak detection are based on wavelet transform [ 8 – 10 ], simple mathematical operations [ 6 , 11 , 12 ], hidden Markov models, and machine learning. Wavelet transform is a suitable approach for considering the non-stationary behavior of the ECG signal.…”

Section: Introductionmentioning

confidence: 99%

A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection

Fotoohinasab

Hocking

Afghah

2021

Computers in Biology and Medicine

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The electrocardiogram (ECG) signal is the most widely used non-invasive tool for the investigation of cardiovascular diseases. Automatic delineation of ECG fiducial points, in particular the R-peak, serves as the basis for ECG processing and analysis. This study proposes a new method of ECG signal analysis by introducing a new class of graphical models based on optimal changepoint detection models, named the graph-constrained changepoint detection (GCCD) model. The GCCD model treats fiducial points delineation in the non-stationary ECG signal as a changepoint detection problem. The proposed model exploits the sparsity of changepoints to detect abrupt changes within the ECG signal; thereby, the R-peak detection task can be relaxed from any preprocessing step. In this novel approach, prior biological knowledge about the expected sequence of changes is incorporated into the model using the constraint graph, which can be defined manually or automatically. First, we define the constraint graph manually; then, we present a graph learning algorithm that can search for an optimal graph in a greedy scheme. Finally, we compare the manually defined graphs and learned graphs in terms of graph structure and detection accuracy. We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.

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Biogeography-Based Optimization Technique for Optimal Design of IIR Low-Pass Filter and Its FPGA Implementation

Susmitha

Karthik

Saha

et al. 2020

Evolutionary Computing and Mobile Sustainable Networks

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An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal

Cited by 38 publications

References 50 publications

Efficient Design of Zero-Phase Riesz Fractional Order Digital Differentiator Using Manta-Ray Foraging Optimisation for Precise Electrocardiogram QRS Detection

Efficient Design of Zero-Phase Riesz Fractional Order Digital Differentiator Using Manta-Ray Foraging Optimisation for Precise Electrocardiogram QRS Detection

A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection

Biogeography-Based Optimization Technique for Optimal Design of IIR Low-Pass Filter and Its FPGA Implementation

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