QRS Complex Detection Using Combination of Mexican-hat Wavelet and Complex Morlet Wavelet

Zeng, Chao; Lin, Hongli; Jiang, Qiyun; Xu, Min

doi:10.4304/jcp.8.11.2951-2958

Cited by 9 publications

(1 citation statement)

References 17 publications

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“…Because CWT can use even non-orthogonal wavelet bases, the choice of the mother wavelet function for CWT is more flexible than that for DWT. For ECG feature extraction, typical wavelets include the Mexican hat and the Morlet wavelet [20], which are expected to have a similar waveform morphology to the ECG signal. The EMD approach, on the other hand, is more appropriate for cases of extracting a signal whose waveform can vary widely in time-scale: for example, EMD has outperformed WT on an experimental task of extracting the respiratory signal from observed ECG signals [15].…”

Section: Introductionmentioning

confidence: 99%

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

et al. 2021

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Recent innovations in wearable electrocardiogram (ECG) devices have enabled various personal healthcare applications based on heart rate variability (HRV). However, wearable ECGs rarely undergo visual inspection by medical experts, hence may contain noise and artifacts. Because apparent changes in the recorded ECGs caused by noise and artifacts may hamper the extraction of QRS complexes, an R-R interval (RRI) estimation algorithm tolerant to these measurement faults is required as the initial step toward HRV analysis using wearable ECGs. This paper proposes a semi-real-time RRI estimation for wearable ECGs utilizing a two-stage structure. In the preprocessing stage, we use a complex-valued wavelet that can adaptively fit to morphological variations of the QRS complex while retaining computing resources for extracting the QRS complex features. In the decision stage, we make use of complex-valued features and select appropriate QRS complexes in consideration of three features: peak magnitude, peak location, and peak morphology (phase). Initial evaluations show that the QRS complex detection performance of the proposed method achieved the F1 score of 0.952 ± 0.040 when targeting pseudo ECG data created from open data assuming wearable ECGs, and of 0.986 ± 0.018 when targeting actual ECG data recorded by using a shirttype wearable ECG device during an exercise activity. Furthermore, the proposed method was able to suppress overlook or misdetection of QRS complexes, so the obtained RRIs are closer to the reference RRIs. The proposed method therefore contributes to achieving accurate HRV analysis using wearable ECGs in terms of obtaining accurate RRIs.

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Section: Introductionmentioning

confidence: 99%

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

et al. 2021

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Damage detection in pipes based on acoustic excitations using laser-induced plasma

Kajiwara

Akita

Hosoya

2018

Mechanical Systems and Signal Processing

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A Machine Learning Approach to Objective Cardiac Event Detection

Twomey¹,

Flach

2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms A Machine Learning Approach to Objective Cardiac Event Detection N. Twomey, P. A. FlachAbstract-This paper presents an automated framework for the detection of the QRS complex from Electrocardiogram (ECG) signals. We introduce an artefact-tolerant pre-processing algorithm which emphasises a number of characteristics of the ECG that are representative of the QRS complex. With this processed ECG signal we train Logistic Regression and Support Vector Machine classification models. With our approach we obtain over 99.7% detection sensitivity and precision on the MIT-BIH database without using supplementary de-noising or pre-emphasis filters.

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QRS Complex Detection Using Combination of Mexican-hat Wavelet and Complex Morlet Wavelet

Cited by 9 publications

References 17 publications

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

R-R Interval Estimation for Wearable Electrocardiogram Based on Single Complex Wavelet Filtering and Morphology-Based Peak Selection

Damage detection in pipes based on acoustic excitations using laser-induced plasma

A Machine Learning Approach to Objective Cardiac Event Detection

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