ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network

Wasimuddin, Muhammad; Elleithy, Khaled; Abuzneid, Abdelshakour; Faezipour, Miad; Abuzaghleh, Omar

doi:10.1109/csci49370.2019.00180

Cited by 17 publications

(6 citation statements)

References 15 publications

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“…However, very little attention is paid towards the 2-D image-based classification of ECG in the literature surveyed. Building upon our recently published preliminary work in this area [116], we plan to further explore deep CNNs for 2-D image-based ECG classification to distinguish multiple classes of ECG beats.…”

Section: Discussionmentioning

confidence: 99%

“…Its application to cardiology goes back more than twenty years [114], [115]. In cardiology, and especially in ECG analysis, CNN has many applications such as detection of arrhythmias [85], [87], ST-changes detection [86] and Normal versus Abnormal [116] classification. There are many variations to CNN and few are stated in this paper to detect arrhythmias with Residual CNN [88], Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) network [89], [90], [92][93][94][95] as well as detecting MI [91] events.…”

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

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Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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Section: Discussionmentioning

confidence: 99%

Section: ) Traditional Ecg Classification Approachesmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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“…We write the result to the previously The value of ecg_points_all_blacks is a twodimensional matrix in which the first level is the line where the black points were. This line contains the coordinates of the Y axis for each black point, which have the following form: [12,26], [12,34], [18,23,32,40], 41, 46, 46, 46, 46, 46, 46, 46, where on the first line here are two black points with the coordinates 12 and 26; on the second line there are two black points with the coordinates 12 and 34. There are a number of black points like in [15][16][17].…”

Section: Building the Algorithm Of Image Processing In The Pythonmentioning

confidence: 99%

“…Over the past decade the scientific literature has presented a lot of works [5,[11][12][13] dealing with the development of new methods of the automatic ECG analysis. For example, using the discrete wavelet transform tool, you can increase the accuracy and sensitivity of signal recognition [14,15].…”

Section: Introductionmentioning

confidence: 99%

Methods of extracting electrocardiograms from electronic signals and images in the Python environment

Zholmagambetova¹,

Mazakov²,

Jomartova³

et al. 2020

Diagnostyka

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High-quality signal processing of an electrocardiogram (ECG) is an urgent problem in present day diagnostics for revealing dangerous signs of cardiovascular diseases and arrhythmias in patients. The used methods and programs of signal analysis and classification work with the arrays of points for mathematical modeling that must be extracted from an image or recording of an electrocardiogram. The aim of this work is developing a method of extracting images of ECG signals into a one-dimensional array. An algorithm is proposed based on sequential color processing operations and improving the image quality, masking and building a one-dimensional array of points using Python tools and libraries with open access. The results of testing samples from the ECG database and comparing images before and after processing show that the signal extraction accuracy is approximately 95 %. In addition, the presented application design is simple and easy to use. The proposed program for analyzing and processing the ECG data has a great potential in the future for the development of more complex software applications for automatic analyzing the data and determining arrhythmias or other pathologies.

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“…Therefore, different ML methods have been developed for automatic detection of abnormalities type in ECG. Specifically, existing ML techniques can be categorized as featurebased methods, wavelet transform features, and higherorder statistics (HOS) [3]. With ML-based methods different classification techniques are developed such as Support Vector Machine (SVM), Decision Tree and neural network for training and anomaly detection in ECG waveform [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

CardioLabelNet: An uncertainty estimation using fuzzy for abnormalities detection in ECG

Mishra

Tiwari

2023

Health Care Science

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Electrocardiography (ECG) abnormalities are evaluated through several automatic detection methods. Primarily, real‐world ECG data are digital signals those are stored in the form of images in hospitals. Also, the existing automated detection technique eliminates the cardiac pattern that is abnormal and it is difficult to multiple abnormalities at some instances. To address those issues in this paper conventional ECG image automated techniques CardioLabelNet model is proposed. The proposed model incorporates two stages for image abnormality detection. At first fuzzy membership is performed in the image for computation of uncertainty. In second stage, classification is performed for computation of abnormal activity. The proposed CardioLabelNet collect ECG image data set for the uncertainty estimation while taking the account of various image classes which includes the global and local entropy of image pixels. For each waveform, uncertainties are calculated on the basis of global entropy. The computation of uncertainty in the images is performed with the fuzzy membership function. The spatial likelihood estimation of a fuzzy weighted membership function is used to calculate local entropy. Upon completion of fuzzification, classification is performed for the detection of normal and abnormal patterns in the ECG signal images. Through integration of stacked architecture model classification is performed for ECG images. The proffered algorithm performance is calculated in terms of accuracy for segmentation, Dice similarity coefficient, and partition entropy. Additionally, classification parameters accuracy sensitivity, specificity, and AUC are evaluated. The proposed approach outperforms the existing methodology, according to the results of a comparative analysis.

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ECG Signal Analysis Using 2-D Image Classification with Convolutional Neural Network

Cited by 17 publications

References 15 publications

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

Methods of extracting electrocardiograms from electronic signals and images in the Python environment

CardioLabelNet: An uncertainty estimation using fuzzy for abnormalities detection in ECG

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