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Congestive heart failure detection based on Electrocardiomatrix Method with ECG Signal

Rao B. Mohan, Kumar Aman

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 3

291--304

EN

Congestive heart failure (CHF) is a prevalent, expensive to treat, and dangerous disease inwhich the pumping capacity of the heart muscle is reduced due to injury or stress. It causesmajor medical problems in humans and contribute to many diseases, thus increasing themortality rate. In a world with a growing population, there is a need for more precise andsimpler approaches to detect such conditions, which can prevent many diseases and lead toa lower mortality rate. The main goal here is to use electrocardiomatrix (ECM) approachto perform the task of detecting CHF. It is detected quickly and accurately with thisapproach, as ECM converts 2D electrocardiogram (ECG) data into a 3D-colored matrix.The approach is tested using ECG readings from the Beth Israel Deaconess Medical Center(BIDMC) CHF Database on the Internet (Physionet.org). The ECM outcomes of are thencompared to manual readings of ECG data. The ECM results achieved the accuracy of96.89%, the sensitivity of 97.53%, the precision of 99.1%, the F1-score of 97.76%, and thespecificity of 96.02% for CHF. This research shows that the ECM approach is a good wayfor machines and practitioners to interpret long-term ECG readings while maintainingaccuracy.

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ECG signals-based automated diagnosis of congestive heart failure using Deep CNN and LSTM architecture

Kusuma S., Jothi K. R.

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

247--257

EN

In humans, Congestive Heart Failure (CHF) refers to the chronic progressive condition that drastically influences the pumping potentiality of the heart muscle. This CHF has the possibility of increasing health expenditure, morbidity, mortality and minimized quality of life. In this context, Electrocardiogram (ECG) is considered as the simplest and a non-invasive diagnosis method that aids in detecting and demonstrating the realizable changes in CHF. However, diagnosing CHF based on manual exploration of ECG signals is frequently impacted by errors as duration and small amplitude of the signals either investigated separately or in the integration is determined to neither specific nor sensitive. At this juncture, the reliability and diagnostic objectivity of ECG signals during the CHF detection process may be enhanced through the inclusion of automated computer-aided system. In this paper, Deep CNN and LSTM Architecture (DCNN-LSTM)-based automated diagnosis system is proposed for detecting CHF using ECG signals. In specific, CNN is included for the purpose of extracting deep features and LSTM is used for attaining the objective of CHF detection using the extracted features. This proposed DCNN-LSTM is evolved with minimal pre-processing of ECG signals and does not involve any classification process or manual engineered features during diagnosis. The experimentation of the proposed DCNN-LSTM conducted using the real time ECG signals datasets confirmed an accuracy of 99.52, sensitivity of 99.31%, specificity of 99.28%, F-Score of 98.94% and AUC of 99.9%, respectively.

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Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions

Rohila Ashish, Sharma Ambalika

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

1140--1154

EN

Background and objective: Sudden cardiac death (SCD) is an unexpected loss in functioning of the heart. It generally occurs within 1 h of onset of symptoms. No reliable method for early detection of SCD is available. Therefore, the development of a non-invasive method for risk identification remains a topic of utmost interest. This paper presents a novel approach for detection of the risk of SCD by performing a comparative analysis of heart rate variability (HRV) in normal subjects as well as patients with coronary artery disease and heart failure. Methods: HRV of four subject groups, normal sinus rhythm, coronary artery disease, congestive heart failure, and SCD, has been analyzed. The analysis was performed by using nonlinear techniques and time-frequency representation obtained by generalized S-transform. The extracted features were examined for their clinical significance by using the Kruskal–Wallis one-way analysis of variance and multiple comparisons. Eventually, classification was performed using support vector machines and decision tree classifiers to separate the individuals at risk of SCD. Results: The performance of the proposed methodology has been evaluated using PhysioNet open-access databases. Statistical analysis shows that HRV in SCD group differs significantly from other groups. For classification, an accuracy of 91.67% was achieved with 83.33% sensitivity, 94.64% specificity, and 84.75% precision. Conclusion: The experimental results obtained by analyzing retrospective data seem promising. However, the methodology needs to be tested on larger databases to generalize the findings. Prospective studies on its clinical usefulness may help in developing a concrete diagnostic technique.

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Accurate automated detection of congestive heart failure using eigenvalue decomposition based features extracted from HRV signals

Sharma Rishi Raj, Kumar Ashish, Pachori Ram Bilas, Acharya U. Rajendra

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 2

312--327

EN

Congestive heart failure (CHF) is a cardiac abnormality in which heart is not able to pump sufficient blood to meet the requirement of all the parts of the body. This study aims to diagnose the CHF accurately using heart rate variability (HRV) signals. The HRV signals are non-stationary and nonlinear in nature. We have used eigenvalue decomposition of Hankel matrix (EVDHM) method to analyze the HRV signals. The lowest frequency component (LFC) and the highest frequency component (HFC) are extracted from the eigenvalue decomposed components of HRV signals. After that, the mean and standard deviation in time domain, mean frequency calculated from Fourier-Bessel series expansion, k-nearest neighbor (k-NN) entropy, and correntropy features are evaluated from the decomposed components. The ranked features based on t-value are fed to least-squares support vector machine (LS-SVM) classifier with radial basis function (RBF) kernel for automated diagnosis of CHF HRV signals. The study is performed on three normal datasets and two CHF datasets. Our proposed system has yielded an accuracy of 93.33%, sensitivity of 91.41%, and specificity of 94.90% using 500 HRV samples. The automated toolkit can aid cardiac physicians in the accurate diagnosis of CHF patients to confirm their findings with our system. Hence, it will help to provide timely treatment for CHF patients and save life.