ECG Morphological Marking using Discrete Wavelet Transform

Sampath, A.; Sumithira, T. R.

doi:10.3233/idt-160264

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The system based on convolutional ANN proposed in this paper showed results slightly superior to the previous system based on pattern recognition [2] and similar to the work [20].…”

Section: Resultssupporting

confidence: 74%

“…Based on the fact that the normal cardiocycle shape changes slowly over time and each current cardiocycle can be distorted by artifacts and noises, it makes sense to recognize the characteristic segments of average cardiocycle shape. The average cardiocycle of several (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) cardiocycles having a high correlation coefficient with each other is calculated. Then the selected cardiocycles are averaged pointwise.…”

Section: The Previous Version Of the Cardiac Cycle Morphology Analysismentioning

confidence: 99%

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The electrocardiogram signal morphology analysis based on convolutional neural network

Zavoyskih

Korobeynikov

Menlitdinov³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The analysis of electrocardiogram signal morphology based on convolutional neural network is considered. Input data is obtained by splitting the signal into cardiac cycles. The calculation the average cycle is performed to exclude the artefacts. The Haar wavelet transform of the average cycle is performed. The images of size 200x6 are input data for the recognition system: 200 – number of counts constituting the cycle; 6 – number of Haar transform time scales. This work is a reconsideration of the previous work of the authors. The training samples base of marked cardiac cycle segments is the same (1500 cycles): the average cycle and the segment’s start and end indexes. In the previous work, the original composite system consisting of several modules was used as a recognition system. In current work it is proposed to use the convolutional neural network with the special structure: 4 convolutional layers, 2 dense layers, and 200 outputs for every of 3 segment. The recognition system based on neural network showed results slightly superior to the previous system. The percent of acceptable localization of the segments is the following: P – 82.2%, QRS – 88.7%, and T – 85.4%. The proposed system effectively solves the problem using the standard modules of modern artificial neural networks.

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“…The system based on convolutional ANN proposed in this paper showed results slightly superior to the previous system based on pattern recognition [2] and similar to the work [20].…”

Section: Resultssupporting

confidence: 74%

Section: The Previous Version Of the Cardiac Cycle Morphology Analysismentioning

confidence: 99%

The electrocardiogram signal morphology analysis based on convolutional neural network

Zavoyskih

Korobeynikov

Menlitdinov³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

View full text Add to dashboard Cite

show abstract

“…Related literature review of this study almost 67 % paper used all using wavelet at the extraction stage of its feature. With the use of Discrete Wavelet Transform-DWT method consists of 50% journals used using such methods include: [15], [18], [20], [24][25], [28][29][30][31][32], [35], [38], and [41]. Continuous Wavelet Transform (CWT) include: [17], [21], [23], and [39][40].…”

Section: B Feature Extractionmentioning

confidence: 99%

“…Some researchers are using methods other than Wavelet at the extraction stage of the feature as seen in Table II. PCA [24] Filtering raw signal and extraction of beats [25] P & R peak detection [27] Denoising using notch filter [29] Noise Repair [30] Denoising [31] Key Features and Frequencies [32] Denoising [38] Baseline Drift Removal and Denoising, Noise Removal [39] Data Denoising and Data Segmentation [40] Noise Identification [41] Mean [41]…”

Section: B Feature Extractionmentioning

confidence: 99%

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Use of Wavelets in Electrocardiogram Research: a Literature Review

Darwan¹,

Mustafidah

2021

Jurnal Informatika

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Currently the introduction and detection of heart abnormalities using electrocardiogram (ECG) is very much. ECG conducted many research approaches in various methods, one of which is wavelet. This article aims to explain the trends of ECG research using wavelet approach in the last ten years. We reviewed journals with the keyword title "ecg wavelet" and published from 2011 to 2020. Articles classified by the most frequently discussed topics include: datasets, case studies, pre-processing, feature extraction and classification/identification methods. The increase in the number of ECG-related articles in recent years is still growing in new ways and methods. This study is very interesting because only a few researchers focus on researching about it. Several approaches from many researchers are used to obtain the best results, both by using machine learning and deep learning. This article will provide further explanation of the most widely used algorithms against ECG research with wavelet approaches. At the end of this article it is also shown that the critical aspect of ECG research can be done in the future is the use of datasets, as well as the extraction of characteristics and classifications by looking at the level of accuracy.

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Sparse based recurrent neural network long short term memory (rnn-lstm) model for the classification of ecg signals

Sampath¹,

R²

2022

Applied Artificial Intelligence

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ECG Morphological Marking using Discrete Wavelet Transform

Cited by 6 publications

References 11 publications

The electrocardiogram signal morphology analysis based on convolutional neural network

The electrocardiogram signal morphology analysis based on convolutional neural network

Use of Wavelets in Electrocardiogram Research: a Literature Review

Sparse based recurrent neural network long short term memory (rnn-lstm) model for the classification of ecg signals

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