Browsing by Author "Mohan Rao, B.M."

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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.
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)