Adaptive QRS Detection Based on Maximum A Posteriori Estimation

Börjesson, Per Ola; Pahlm, Olle; Sörnmo, Leif; Nygårds, Mats-Erik

doi:10.1109/tbme.1982.324901

Cited by 54 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [46], a comparison between the first-derivative-based methods, especially Pan Tompkins, Hamilton-Tompkins with Hilbert transform (HT) methods, is drawn. In [47], an adaptive algorithm based on maximum a posteriori (MAP) estimation is described. A mathematical model of pulse-shaped waveform for QRS detection is developed, and its estimation procedure is estimated.…”

Section: R-peak Detection Methodsmentioning

confidence: 99%

See 1 more Smart Citation

ECG Signal Analysis

Gupta

Mitra

Bera

2013

ECG Acquisition and Automated Remote Processing

View full text Add to dashboard Cite

Section: R-peak Detection Methodsmentioning

confidence: 99%

“…To adapt with varying QRS morphology, the detector (estimator) adjusts the parameters with incoming beats. In [48], the same model as in [47] is dealt with; however, a simplification in the model is introduced.…”

Section: R-peak Detection Methodsmentioning

confidence: 99%

ECG Signal Analysis

Gupta

Mitra

Bera

2013

ECG Acquisition and Automated Remote Processing

View full text Add to dashboard Cite

“…A variety of techniques of QRS detection from raw ECG signals were presented in their studies, including thresholding, syntactic methods [24][25][26], Hidden Markov Models [27], neural networks [28][29][30][31], template matching [32], matched filters [28,33], singularity techniques [34] and zero-crossing [35]. According to Elgendi et al [23], thresholding methods appear to be the most computationally efficient for QRS detection using portable battery operated devices.…”

Section: Related Workmentioning

confidence: 99%

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Pandit

Zhang

Liu

et al. 2017

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) A Lightweight QRS AbstractBackground and Objectives: Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods:A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results:The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusio...

show abstract

“…Börjesson et al [7] proposed an alternative to serial detection by selecting the 'best' peaks out of all the peaks that occur within a defined interval. Roughly, the 'best' peaks are those that have the largest amplitudes and greatest width.…”

Section: Introductionmentioning

confidence: 99%

Sinus rhythm heart rate estimation in high noise environments by application ofa prioriRR interval statistics

Hopenfeld

2014

Journal of Medical Engineering & Technology

View full text Add to dashboard Cite

Most existing heart beat detection algorithms serially process peaks, which can be either noise or true beats. Serial processing can result in inaccurate detections in the context of high noise. The proposed method relies on the relative regularity of sinus rhythm RR interval changes to select the best sequences of peaks in a 5-10 s long segment of cardiac data. The best sequences with a current data segment are subjected to a trending analysis, to determine whether their associated RR intervals fit within a pattern of prior best segments. The RR regularity scores and the results of the trending analysis are combined into a single sequence score and the final sequence for a segment is chosen from the best sequences based on this overall score. The current heart rate estimate is updated with the final sequence's RR interval by an adaptive filter that weights the overall score. Twenty-four hour RR interval records for 54 normal individuals were parsed into 10-s segments and corrupted with spurious 'noise' peaks, which resulted in a revised RR interval series that included a number of false RR intervals. The algorithm was run on these corrupted RR interval series. The percentages of mean heart rate values within 5 beats min(-1) of the true value were 95%, 88% and 77% for 10, 20 and 30 added noise spikes, respectively. The percentages of mean heart rate values within 10 beats min(-1) of the true value were 98%, 96% and 91% for 10, 20 and 30 added noise spikes, respectively. Accuracy was higher for data segments characterized by relatively low RR interval variability. The proposed algorithm shows promise for estimating average heart rate for sinus rhythm in high noise environments.

show abstract

Adaptive QRS Detection Based on Maximum A Posteriori Estimation

Cited by 54 publications

References 19 publications

ECG Signal Analysis

ECG Signal Analysis

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Sinus rhythm heart rate estimation in high noise environments by application ofa prioriRR interval statistics

Contact Info

Product

Resources

About