Browsing by Author "Marwaha, P."

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Accurate detection of congestive heart failure using electrocardiomatrix technique
(Springer, 2022) Sharma, K.; Mohan Rao, B.M.; Marwaha, P.; Kumar, A.
Congestive Heart Failures (CHFs) are prevalent, expensive, and deadly, causing damage or overload to the pumping power of the heart muscles. These leads to severe medical issues amongst humans and contribute to a greater death risk of numerous diseases at a later stage. We need accurate and less difficult techniques to detect these problems in our world with a growing population which will prevent many diseases and reduce deaths. In this work, we have developed a technique to diagnose CHF using the Electrocardiomatrix (ECM) technique. The 1-D ECG signals are transformed to a colourful 3D matrix to diagnose CHF. The detection of CHF using ECM are then compared with annotated CHF Electrocardiogram (ECG) signals manually. It has been found that ECM is able to detect the affected CHF duration from the ECG signals. Also, the ECM provides the reduction in both false positive and false negative which in turn improves the detection accuracy. The performance of the proposed approach has been tested on BIDMC CHF database. The proposed method achieved an accuracy of 97.6%, sensitivity of 98.0%, specificity of 97.0%, precision of 99.4%, and F1-Score of 98.3%. From this study, it has been revealed that the ECM technique allows the accurate, intuitive, and efficient detection of CHF and using ECM practitioners can diagnose the CHF without sacrificing the accuracy. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Detection of atrial fibrillation based on Stockwell transformation using convolutional neural networks
(Springer Science and Business Media B.V., 2023) Mohan Rao, B.M.; Kumar, A.; Bachwani, N.; Marwaha, P.
Atrial Fibrillation (AF) is a pervasive cardiac cantering rhythm that is harmful and causes heart-related complications. AF causes an irregular heartbeat, which may show up at intervals known as proximal AF or for a long duration known as persistent AF. Such irregularities lead to the unhealthy functioning of the heart and cause various cardiac problems. So, the early detection of AF can help in planning efficient treatment, though automatic and early detection of AF is a challenging task. In this study presents a novel approach to detecting AF signals from electrocardiogram (ECG) signals that is based on the combination of a Stockwell Transformation (ST) technique with a 2-D convolutional neural network (CNN). These traditional AF detection methods are used for the isolation of ventricular and atrial activities. The proposed method uses the MIT-BIH database of AF and normal sinus rhythm (NSR) for training and testing. However, the proposed method analyses the time–frequency features of the ECG, and to improve the prior AF detection, a 2D-CNN model is trained and tested for the overall accuracy (Acc), sensitivity (Se), specificity (Sp), and positive predictive value (PPV) performance measures. The results indicated that the proposed method improved the overall performance significantly, providing better accuracy and precision compared to other existing models. The experimental results performed on the proposed approach provide significant improvements in Acc, Se, Sp, and PPV of 99.543%, 99.50%, 99.58%, and 99.57%, respectively. The proposed method can be used in the emergency healthcare department, and it helps cardiologists in the accurate detection of heart problems. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.
Improved cross sample entropy with error-metric based cardiac variability time series evaluation
(Springer Science and Business Media B.V., 2024) Sharma, K.; Sunkaria, R.K.; Marwaha, P.
The cardiac rate variability analysis is a tool used to diagnose pathological and physiological variations in subjects in the premature stages. The cross-sample entropy (CSE) measure is used to analyze cardiac variability to diagnose cardiovascular diseases. In the proposed work, CSE is evaluated to detect arrhythmia subjects. It has been observed that CSE is restricted by a fixed threshold and any distance measure for cardiac disorder detection. In the proposed work, a new measure, named the error-metric cross sample entropy (E-metricCSE), is introduced to detect various cardiac disorders by using dynamic threshold and an error metric, root mean square error (RMSE). It signifies that the use of the RMSE makes the proposed algorithm most convenient for noise free data when compared to a distance metric. Different sets of MIX (Q) processes are executed on both real and simulated data to test the effectiveness of the proposed method. It is further noticed that the proposed algorithm is more consistent and more effective to quantify pathological and physiological subjects than the original CSE. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
Machine Learning Techniques Used for Diagnosing Cardiac Abnormalities using Electrocardiogram
(Apple Academic Press, 2024) Mohan Rao, B.; Kumar, A.; Marwaha, P.; Bage, A.
Atrial fibrillation (AF) damages around 1–2% of the human body and is the most serious heart arrhythmia in human healthcare. As per the guidelines of World Health Organization (WHO), coronary thrombosis (CT) is the main cause of mortality throughout world and in India. As a consequence of coronary artery disorder, high blood pressure, alcoholic abuse, and a life in emotional stress, heart arrhythmias, or irregular heartbeats, are among the most prevalent CTs. In addition to the CTs described, an irregularity in heart rhythm is produced by mental stress in long duration, which is a difficult problem for researchers to solve. One of the most important areas of study after the development of the electrocardiogram (ECG) and robust machine learning algorithms is the early detection of cardiac arrhythmia using automated electronic methods. As a gold standard for studying heart function, cardiologists and researchers rely on the ECG because it records changes in electrical activity connected to the cardiac cycles. The research was conducted on ECG analysis and categorization utilizing classic and novel artificial intelligence (AI) methods. As a result of the research, a detailed report is expected. Recent years have seen a rise in the use of AI methods to detect arrhythmia signs automatically and early. This chapter examines the literature of the past few years to assess the performance of artificial intelligence and other computer-based systems for processing ECG data for the detection of heart problems. Performance measurements such as specificity, sensitivity, accuracy, positive predictive value (PPV), etc. are used to evaluate machine learning (ML) and deep learning (DL) methods for detecting the AF from ECG signal analysis and categorization. These are crucial in the early detection of AF because accurate detection of heart problems can reduce mortality. © 2025 Apple Academic Press, Inc.
MSE model incorporating Fourier decomposition method: HRV study of ECG signals for atrial fibrillation and congestive heart failure
(Elsevier Ltd, 2023) Kumar, A.; Diksha; Mohan Rao, B.M.; Marwaha, P.; Jaiswal, A.
Traditional techniques to evaluate the complexity of biological signals forgo the numerous time scales present in such data. When these algorithms were applied to real-world datasets gathered in health and illness states, they produced inconsistent results. Sample entropy (SampEn) has been utilized extensively to evaluate the complexity of RR-interval time series. Using multi-scale Entropy analysis incorporating Sample Entropy, several research on the complexity of physiological signals have been conducted. The primary disadvantage of MSE is that coarse-graining results in a noticeable and considerable loss of information. For small scales, the method proves to be ineffective. So, for the MSE analysis of physiological data, we have employed the FDM rather than coarse-graining. This FDM, which is based on the Fourier theory, is excellent for studying nonlinear and nonstationary time series. This approach generates a TFE distribution that displays the data's fundamental premise. This will provide a number of frequency bands with varying cut-off frequencies but consistent data across all of them. To further validate our suggested technique, we used “ANOVA analysis” to compare our results to the entropy analysis of a synthetic simulated database containing white noise (WN) and power noise (PN) signals. © 2023 The Author(s)