Faculty Publications
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Publications by NITK Faculty
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Item AR modeling of heart rate signals(Institute of Electrical and Electronics Engineers Inc., 2004) Nayak, J.; Subbanna Bhat, P.; Acharya, A.U.; Niranjan, U.C.; Sing, O.W.The electrocardiogram (ECG) is a representative signal containing information about the condition of the heart. The shape and size of the P-QRS-T wave, the time intervals between its various peaks etc may contain useful information about the nature of disease afflicting the heart. However, the human observer can not directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the heart rate variability signal is used as the base signal for the highly useful in diagnostics. This paper deals with the analysis of eight cardiac abnormalities using Auto Regressive (AR), modeling technique. The results are tabulated below for specific example. © 2004 IEEE.Item ECG Signal Classification using Continuous Wavelet Transform Scalogram and Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2024) Keerthan Kumar, T.G.K.; Ogare, M.K.; Koolagudi, S.G.Automated classification of electrocardiogram (ECG) signals is pivotal for timely and accurate diagnosis of cardiac abnormalities. In this work weintroduces a new method for classifying electrocardiogram (ECG) signals by merging signal processing and deep learning techniques. We utilize Continuous Wavelet Transform (CWT) to convert one-dimensional ECG signals into scalogram images, capturing both temporal and frequency details. By employing transfer learning, we fine-tune a pre-trained AlexNet Convolutional Neural Network (CNN) to categorize ECG signals into three types: arrhythmia, congestive heart failure, and normal sinus rhythm. We extensively compare our method with existing approaches, demonstrating its superior performance with an accuracy of 96%. The hierarchical structure of AlexNet enables the extraction of intricate features from ECG signals, surpassing other models that suffer from shallow architectures and reliance on manual feature engineering. Our approach not only improves automated ECG analysis but also holds promise for enhancing clinical diagnosis and management of cardiovascular conditions. © 2024 IEEE.Item The heart rate is a non-stationary signal, and its variation can contain indicators of current disease or warnings about impending cardiac diseases. The indicators can be present at all times or can occur at random, during certain intervals of the day. However, to study and pinpoint abnormalities in large quantities of data collected over several hours is strenuous and time consuming. Hence, heart rate variation measurement (instantaneous heart rate against time) has become a popular, non-invasive tool for assessing the autonomic nervous system. Computer-based analytical tools for the in-depth study and classification of data over day-long intervals can be very useful in diagnostics. The paper deals with the classification of cardiac rhythms using an artificial neural network and fuzzy relationships. The results indicate a high level of efficacy of the tools used, with an accuracy level of 80-85%. © IFMBE: 2004.(Classification of cardiac abnormalities using heart rate signals) Acharya, A.U.; Kumar, A.; Subbanna Bhat, P.; Lim, C.M.; Iyengar, S.S.; Kannathal, N.; Krishnan, S.M.2004