Fast T Wave Detection Calibrated by Clinical Knowledge with Annotation of P and T Waves

Elgendi, Mohamed; Eskofier, Bjoern M.; Abbott, Derek

doi:10.3390/s150717693

Cited by 46 publications

(46 citation statements)

References 34 publications

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“…Therefore, all Pareto solutions were sorted in descending order according to the overall accuracy (objective function J ) [17,19,22,23,24,25]; and thus, the first combination is considered the optimal Pareto solution.…”

Section: Resultsmentioning

confidence: 99%

“…When applied to the well-known MIT-BIH Arrhythmia Database, an SE of 99.78% and a +P of 99.87% were attained [22]. The TERMA-based QRS detector outperformed most of the well-known QRS detector, such as Pan–Tompkins [4] (SE of 90.95% and +P of 99.56%) and Hamilton–Tompkins [28] (SE of 99.69% and +P of 99.77%).For T wave detection in ECG signals: Over the MIT-BIH Arrhythmia Database, the TERMA-based T wave detector achieved an SE of 99.86% and a +P of 99.65%, which are promising results for handling the non-stationary effects, low SNR, normal sinus rhythm (NSR), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC) and premature atrial contraction (PAC) in ECG signals [25]. The TERMA-based T wave detector was not compared to other algorithms as the annotation of T-waves was published in 2015.…”

Section: Resultsmentioning

confidence: 99%

“…For T wave detection in ECG signals: Over the MIT-BIH Arrhythmia Database, the TERMA-based T wave detector achieved an SE of 99.86% and a +P of 99.65%, which are promising results for handling the non-stationary effects, low SNR, normal sinus rhythm (NSR), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC) and premature atrial contraction (PAC) in ECG signals [25]. The TERMA-based T wave detector was not compared to other algorithms as the annotation of T-waves was published in 2015.…”

Section: Resultsmentioning

confidence: 99%

“…It offers good transition band characteristics at low coefficient orders, which makes it efficient to implement [21]. All research was carried out using the TERMA method, a third-order Butterworth filter with a passband of

F_{1}

–

F_{2}

Hz to remove baseline wander and high frequencies [17,19,22,23,24,25], where

F_{1}

is the starting frequency and

F_{2}

is the stopping frequency, as shown in Line 3 in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

Elgendi

2016

Biosensors

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Biomedical signals contain features that represent physiological events, and each of these events has peaks. The analysis of biomedical signals for monitoring or diagnosing diseases requires the detection of these peaks, making event detection a crucial step in biomedical signal processing. Many researchers have difficulty detecting these peaks to investigate, interpret and analyze their corresponding events. To date, there is no generic framework that captures these events in a robust, efficient and consistent manner. A new method referred to for the first time as two event-related moving averages (“TERMA”) involves event-related moving averages and detects events in biomedical signals. The TERMA framework is flexible and universal and consists of six independent LEGO building bricks to achieve high accuracy detection of biomedical events. Results recommend that the window sizes for the two moving averages (W1 and W2) have to follow the inequality (8×W1)≥W2≥(2×W1). Moreover, TERMA is a simple yet efficient event detector that is suitable for wearable devices, point-of-care devices, fitness trackers and smart watches, compared to more complex machine learning solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

F_{1}

–

F_{2}

Hz to remove baseline wander and high frequencies [17,19,22,23,24,25], where

F_{1}

is the starting frequency and

F_{2}

is the stopping frequency, as shown in Line 3 in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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TERMA Framework for Biomedical Signal Analysis: An Economic-Inspired Approach

Elgendi

2016

Biosensors

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“…The detection of each wave (e.g. P or T wave) also constitutes a research topic on its own [47,48]. In this research, we employ a comparatively lightweight peak detection algorithm (i.e., Algorithm 2) for detection of P, Q, S and T waves.…”

Section: Detection Of the Locations Of P Q S And T Wavesmentioning

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

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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...

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