A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators

Tang, Xiaochen; Hu, Qisong; Tang, Wei

doi:10.1109/tbcas.2018.2823275

Cited by 76 publications

(28 citation statements)

References 45 publications

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“…11. At present, many Rpeak detection algorithms or hardware implementations with higher detection performance than PT algorithm or QSWT algorithm have been published, such as [3], [28]- [31]. It can be inferred that using the proposed compression system combined with these R-peak detection methods can also achieve similar CRs.…”

Section: A Effect Of Different R-peak Detection Algorithms On Crmentioning

confidence: 95%

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Jia

et al. 2020

IEEE Access

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Long-term electrocardiogram (ECG) monitoring requires high-ratio lossless compression techniques to reduce data transmission energy and data storage capacity. In this paper, we have proposed a high-ratio ECG compression system with low computational complexity. Firstly, as the morphologies of the ECG change over time, we divide the signal of each heartbeat cycle into two regions. To achieve high prediction accuracy, a 1 st order linear predictor and a combination of the template predictor and 3 rd order linear predictor are applied in the two regions respectively. Secondly, we introduce a context-based error modeling module to the system, which cancels the statistical bias of the prediction algorithm and further improves the prediction accuracy. Thirdly, we modify the Golomb-Rice encoding algorithm to adaptively encode the prediction errors, while preserving a code space for packaging the information that is necessary for prediction. We evaluate the proposed system by using the MIT-BIH Arrhythmia Database (ARRDB). The experimental results show that with memory requirements as low as 444 to 14556 total variables this system achieves a compression ratio (CR) from 2.975 to 3.040, suggesting that it is highly applicable to both the low-power design and the cloud.

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Section: A Effect Of Different R-peak Detection Algorithms On Crmentioning

confidence: 95%

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

Jia

et al. 2020

IEEE Access

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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%

“…Also, the EMD‐based method provides acceptable results only for few MIT/BIH arrhythmia database (MBDB) records . The PDM‐based QRS detection method faces major challenges in several situations: (1) when ECG waveform having similar characteristics to that of the QRS complex; (2) for low amplitude QRS complex; (3) when the QRS complex is affected by severe baseline wandering; and (4) for premature ventricular contraction (PVC) and aberrated atrial premature beats . For all these situations, the PDM‐based QRS detection algorithm produces large false peaks .…”

Section: Introductionmentioning

confidence: 99%

“…The PDM‐based QRS detection method faces major challenges in several situations: (1) when ECG waveform having similar characteristics to that of the QRS complex; (2) for low amplitude QRS complex; (3) when the QRS complex is affected by severe baseline wandering; and (4) for premature ventricular contraction (PVC) and aberrated atrial premature beats . For all these situations, the PDM‐based QRS detection algorithm produces large false peaks . The Shannon energy and Hilbert transform‐based methods detect several false peaks for long pause ECG signals .…”

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%

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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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Accurate RR-Interval Detection with Daubechies Filtering and Adaptive Thresholding

Rif'an

Rieger

Wang

2019

Lecture Notes in Electrical Engineering

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A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators

Cited by 76 publications

References 45 publications

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

A Lossless Electrocardiogram Compression System Based on Dual-Mode Prediction and Error Modeling

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

Accurate RR-Interval Detection with Daubechies Filtering and Adaptive Thresholding

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