A Joint QRS Detection and Data Compression Scheme for Wearable Sensors

John, Deepu; Lian, Yong

doi:10.1109/tbme.2014.2342879

Cited by 109 publications

(68 citation statements)

References 30 publications

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“…The optimal W and q values were determined by 8 after testing various combinations of ( W , q ) with W ranging from 3 to 6 and q ranging from 9 to 17. It was found that optimal detection QRS accuracy was achieved with W = 3 and q = 15.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, Deepu et al . 8 used prediction error as a marker to locate the QRS complex in ECG signals. By contrast, Method III does not need the linear predictor step used in Method I.…”

Section: Discussionmentioning

confidence: 99%

“…Method I applied a coding-packaging scheme that gives a practical, fixed-length 16-bit output and has low hardware complexity 8 . The coding-packaging routine is based on the two’s complement, which is a mathematical operation on binary numbers, representation of the prediction error signal.…”

Section: Discussionmentioning

confidence: 99%

“…However, Rice-Golomb coding is highly complex and requires a dedicated memory 8 . The two-stage adaptive predictor and the Huffman coding schemes 11 achieved a BCR of 2.43, but Huffman coding is highly complex, generates variable-length coded data, and would need further packaging to interface with a standard input/output 8 . Simple predictor and Huffman coding 12 achieved a BCR of 1.92.…”

Section: Discussionmentioning

confidence: 99%

“…Developing an algorithm that enables the ECG analysis (such as QRS detection and beat-to-beat interval estimation) to be performed locally on the ECGag is also needed 7,8 . Because the wireless communication between the ECGag and the ECGau unit (cf.…”

Section: Introductionmentioning

confidence: 99%

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Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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Section: Resultsmentioning

confidence: 99%

“…Therefore, Deepu et al . 8 used prediction error as a marker to locate the QRS complex in ECG signals. By contrast, Method III does not need the linear predictor step used in Method I.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems

Kumar

Komaragiri

Kumar

2019

Circuit Theory & Apps

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Summary A low power and high‐performance digital electrocardiogram (ECG) detector has become a basic requirement in modern implantable cardiac pacemakers. A fractional operator‐based digital ECG detector for modern pacemaker systems is proposed in this work. Instead of conventional thresholding, an adaptive slope prediction threshold is utilized for the detection of ECG peaks. A stochastic search‐based algorithm, namely, cuckoo search algorithm, is used to design an optimal fractional operator that is used for ECG denoising. It has been found that the proposed adaptive slope prediction threshold increases the QRS complex detection performance. A low detection error rate (DER) ranges from 0.01% to 0.56%, positive predictivity (P+) ranges from 99.32% to 99.98%, sensitivity (Se) ranges from 99.45% to 99.98%, and a detection accuracy (Acc) ranges from 99.43% to 99.96% for different databases are achieved for the proposed ECG detector, which is better compared with the existing ECG detectors. The proposed design of fractional order operator based on the lattice wave digital filter (LWDF) requires a minimum number of the multipliers for its structural realization.

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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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A Joint QRS Detection and Data Compression Scheme for Wearable Sensors

Cited by 109 publications

References 30 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

Design of efficient fractional operator for ECG signal detection in implantable cardiac pacemaker systems

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

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