A Real-Time QRS Detection Method Based on Phase Portraits and Box-Scoring Calculation

Hou, Zhongjie; Dong, Yonggui; Xiang, Jinxi; Li, Xuewu

doi:10.1109/jsen.2018.2812792

Cited by 45 publications

(22 citation statements)

References 27 publications

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“…According to the embedding method, the response fragment A is transformed into an m-dimensional phase-space vector by delay time coordinates, m is an embedding dimension, and τ is the delay time. According to the study of Hou et al [20], the mesh phase portraits are developed in two-dimensional phase space, so m � 2.…”

Section: Signal Fragmentsmentioning

confidence: 99%

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A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

Chen

Wen

2020

Shock and Vibration

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Effective fault detection and diagnosis (FDD) for rotating machinery is always a focus issue in improving the prognostic and health management (PHM) of the equipments. The existing usage of similarity measurement has been widely spread in searching the homologous fault responses from vibration signals, but most of them are just suitable for stable speed and cannot be applied in all variable speed conditions. In order to improve measurement performance, a fast-meshed phase portrait (FMPP) frame combining the phase-space technique and box-scoring calculation is proposed. Firstly, the variable-speed signal is divided into multiple undetermined fragments according to fault characteristic orders (FCOs). Secondly, the undetermined fragments are reconstructed into corresponding phase-space trajectories to overcome the time-delay matching inconsistency of the variable speed fragments. Thirdly, the phase-space trajectories are mapped into meshed phase portraits via box-scoring calculation. Such decisive calculation can effectively transforms the diverse unequal fragments into the phase diagrams with same size, which saves time for the subsequent similarity measurement. Finally, the proposed FMPP is tested both for accuracy and timeliness on a self-built bearing bench, where the order tracking (OT) and dynamic time warping (DTW) methods are used for the comparisons. The experiments proved the effectiveness of the proposed method.

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Section: Signal Fragmentsmentioning

confidence: 99%

“…Hence, the phase in space can be used to represent the intrinsic link of each response that the fault state excited in the signal. And boxscoring calculation [20] can map time series of different lengths into equal phase portraits to overcome the influence of variable speed.…”

Section: Introductionmentioning

confidence: 99%

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

Chen

Wen

2020

Shock and Vibration

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“…The first QRS detection algorithm was introduced by Pan and Tompkins [6]. There are other attempts for QRS detection based on shannon energy envelope (SEE) [7], wavelet transform (WT) [8]- [12], phase-space reconstruction (PSR) [13], Optimized adaptive thresholding [14], iterative state machines [15], and moving-average filters [16]. Concerning the non-QRS delineation algorithms, the main objective is to determine the peaks and boundaries of the individual QRS complexes, P and T waves.…”

Section: Introductionmentioning

confidence: 99%

An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform

Chen

Maharatna

2020

IEEE J. Biomed. Health Inform.

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The detection and delineation of QRS-complexes and T-waves in Electrocardiogram (ECG) is an important task because these features are associated with the cardiac abnormalities including ventricular arrhythmias that may lead to sudden cardiac death. In this paper, we propose a novel method for the R-peak and the T-peak detection using hierarchical clustering and Discrete Wavelet Transform (DWT) from the ECG signal. In the first step, a template of the single ECG beat is identified. Secondly, all R-peaks are detected by using hierarchical clustering. Then, each corresponding T-wave boundary is delineated based on the template morphology. Finally, the determination of T wave peaks is achieved based on the Modulus-Maxima Analysis (MMA) of the DWT coefficients. We evaluated the algorithm by using all records from the MIT-BIH arrhythmia database and QT database. The R-peak detector achieved a sensitivity of 99.89%, a positive predictivity of 99.97% and 99.83% accuracy over the validation MIT-BIH database. In addition, it shows a sensitivity of 100%, a positive predictivity of 99.83% in manually annotated QT database. It also shows 99.92% sensitivity and 99.96% positive predictivity over the automatic annotated QT database. In terms of the T-peak detection, our algorithm is verified with 99.91% sensitivity and 99.38% positive predictivity in manually annotated QT database.

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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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A Real-Time QRS Detection Method Based on Phase Portraits and Box-Scoring Calculation

Cited by 45 publications

References 27 publications

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed

An Automatic R and T Peak Detection Method Based on the Combination of Hierarchical Clustering and Discrete Wavelet Transform

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

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