Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition

Lu, Jia; Zhang, Chi; Zhu, Yongjie; Ristaniemi, Tapani; Parviainen, Tiina; Cong, Fengyu

doi:10.1016/j.cmpb.2019.105120

Cited by 36 publications

(16 citation statements)

References 41 publications

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“…In wavelet family, discrete wavelet transform (DWT) is easy to work on a computer, and the calculation efficiency is relatively higher compared with other types of wavelets. DWT is also the most widely used in the analysis of ECG [66][67][68][69][70]. Jayachandran et al took advantage of multiresolution properties of wavelet transformation to identify subtle changes in the ECG signal for detection of myocardial infarction (MI) [71].…”

Section: Wavelet Featurementioning

confidence: 99%

“…Independent components can be picked up from the mixed signals by ICA. Martis et al compared the performance of various dimensionality reduction techniques for arrhythmia classification, including PCA, LDA and ICA, based on DWT features [69]. ICA coupled with neural network yielded the highest average sensitivity, specificity, and accuracy of 99.97%, 99.83% and 99.28%, respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

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Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Xie

Zhou

et al. 2020

Sensors

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Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient’s ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.

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Section: Wavelet Featurementioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Xie

Zhou

et al. 2020

Sensors

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“…The frequency resolution is enhanced for the high value of Q. Besides, a small Q-factor wavelet is useful for smooth signal processing [41]. Here, the ratio of the total number of wavelets to the input signal frequency gives the r parameter, and it can be used to calculate the transform over-sample rate [39].…”

Section: Tunable Q-factor Wavelet Transform and Ann-based Islanding Dmentioning

confidence: 99%

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

et al. 2020

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Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

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“…Mainly, it is plenty of settings, including the mother's wavelets, level of decomposition, and the thresholding rules. These parameters are different for individual applications, and there is not a versatile way to determine the best setting for a particular task [55,56]. The definition of the DWT is given as follows:…”

Section: Concept Of Wavelet Transformationmentioning

confidence: 99%

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

Vaňuš

Fiedorova

Kubíček

et al. 2020

Sensors

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The operating cost minimization of smart homes can be achieved with the optimization of the management of the building’s technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.

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Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition

Abstract: This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details.

Cited by 36 publications

References 41 publications

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

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