Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

Acharya, U. Rajendra; Fujita, Hamido; Adam, Muhammad; Lih, Oh Shu; Sudarshan, Vidya K.; Hong, Tan Jen; Koh, Joel En Wei; Hagiwara, Yuki; Chua, Chua K.; Poo, Chua Kok; San, Tan Ru

doi:10.1016/j.ins.2016.10.013

Cited by 208 publications

(76 citation statements)

References 37 publications

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“…For HF, it has both FF and NFF. Table 2 shows some of the methodologies that have been proposed for cardiovascular diseases classification [90][91][92][93][94] or ECG recognition [95][96][97][98][99]. Eleven machine learning or optimization algorithms have been applied in [90][91][92][93][94][95][96][97][98][99].…”

Section: Cardiovascular Diseasesmentioning

confidence: 99%

“…Due to the multitude of smart healthcare applications, only four applications in the field of diseases diagnosis, cardiovascular diseases [82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99], diabetes mellitus [100][101][102][103][104][105][106][107][108][109][110][111][112], Alzheimer's disease and other forms of dementias [113][114][115][116][117][118][119][120][121][122][123][124][125][126], and tuberculosis [127][128][129][130][13...…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Cardiovascular Diseasesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…These characters make ATFFWT flexible by allowing one to control the -factor, redundancy, and dilation factor [11]. Recently, it has been applied for characterization of coronary artery disease [22,23], myocardial infarction ECG signals [23], and detection of congestive heart failure using heart rate variability (HRV) signals [24]. Matlab toolbox of ATFFWT method is available at http://web.itu.edu.tr/ibayram/AnDWT/.…”

Section: Decomposition Of the Beats Based On Atffwtmentioning

confidence: 99%

J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

Zhou

Zhao

et al. 2018

Mathematical Problems in Engineering

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As a new important index of the electrocardiogram (ECG) of ventricular bipolar play, J wave plays an increasingly significant role in the clinical diagnosis. The existence of J wave hints at potential crisis of fatal disease and even death. Nowadays, however, it can hardly meet the clinical needs where the diagnosis of J wave variation only depends on experience of clinicians. Therefore, a new technique which is capable of detecting J wave using analytic time-frequency flexible wavelet transformation (ATFFWT) is proposed in this paper. We have used ATFFWT to decompose the processed ECG signals into the desired subbands. Further, Fuzzy Entropy (FE) is computed from each subband to capture more hidden and meaningful information. Feature scoring method is applied to select optimal feature set. Finally, the extracted features are fed to Least Squares-Support Vector Machine (LS-SVM) classifier. The 10-fold cross validation is used to obtain reliable and stable performance and to avoid the overfitting of the model. Our proposed algorithm has achieved accuracy of 97.61% for Morlet Wavelet (MW) kernel in comparison to 97.56% for Radial Basis Function (RBF) kernel. The developed effective algorithm can be used to design an expert system to aid clinicians in their regular diagnosis.

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“…The types of wavelet basis used, as well as other parameters, are mainly determined by experience and experiment, and there is a lack of systematic theoretical analysis. Based on the analysis of vanishing moment and frequency characteristics of the wavelet filter, this paper analyze how to select and use the appropriate wavelet basis function and how to filter the noise of the specific frequency characteristic in the process of wavelet transform 6 .…”

Section: Introductionmentioning

confidence: 99%

Study on Optimal Selection of Wavelet Vanishing Moments for ECG Denoising

Peng

Wang

2017

Sci Rep

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The frequency characteristics of wavelets and the vanishing moments of wavelet filters are both important parameters of wavelets. Clarifying the relationship between the wavelet frequency characteristics and the vanishing moments of the wavelet filter can provide a theoretical basis for selecting the best wavelet. In this paper, the frequency characteristics of wavelets were analyzed by mathematical modeling, the mathematical relationship between wavelet frequency characteristics and vanishing moments was clarified, the optimal wavelet base function was selected hierarchically according to the amplitude frequency characteristics of ECG signal, and an accurate notch filter was realized according to the frequency characteristics of the noise. The experimental results showed that the optimal orthogonal wavelet analysis for the ECG signals with different frequency characteristics could make the high frequency energy distribution sparser, and the method proposed in this paper could effectively preserve the singularity of the signal and reduce the signal distortion.

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Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study

Cited by 208 publications

References 37 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

J Wave Autodetection Using Analytic Time-Frequency Flexible Wavelet Transformation Applied on ECG Signals

Study on Optimal Selection of Wavelet Vanishing Moments for ECG Denoising

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