Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
6 results
Search Results
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 SVM based methods for arrhythmia classification in ECG(2010) Kohli, N.; Verma, N.K.; Roy, A.In this study, Support Vector Machine (SVM) based methods have been used to classify the electrocardiogram (ECG) arrhythmias. Among various existing SVM methods, three well-known and widely used algorithms one-against-one, one-against-all, and fuzzy decision function are used here to distinguish between the presence and absence of cardiac arrhythmia and classifying them into one of the arrhythmia groups. The various types of arrhythmias in the Cardiac Arrhythmias ECG database chosen from University of California at Irvine (UCI) to train SVM, include ischemic changes (coronary artery disease), old inferior myocardial infarction, sinus bradycardy, right bundle branch block, and others. The results obtained through implementation of all three methods are thus compared as per their accuracy rate in percentages and the performance of the SVM classifier using one-against-all (OAA) method was found to be better than other techniques. ECG arrhythmia data sets are of generally complex nature and SVM based one-against-all method is found to be of vital importance for classification based diagnosing diseases pertaining to abnormal heart beats. ©2010 IEEE.Item A Method for QRS Delineation Based on STFT Using Adaptive Threshold(Elsevier, 2015) Shaik, B.S.; Naganjaneyulu, G.V.S.S.K.R.; Chandrasheker, T.; Narasimhadhan, A.V.Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection. © 2015 The Authors.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 Transmission and storage of medical images with patient information(Elsevier Ltd, 2003) Acharya, A.U.; Subbanna Bhat, S.; Kumar, M.S.; Min, L.C.Digital watermarking is a technique of hiding specific identification data for copyright authentication. This technique is adapted here for interleaving patient information with medical images, to reduce storage and transmission overheads. The text data is encrypted before interleaving with images to ensure greater security. The graphical signals are interleaved with the image. Two types of error control-coding techniques are proposed to enhance reliability of transmission and storage of medical images interleaved with patient information. Transmission and storage scenarios are simulated with and without error control coding and a qualitative as well as quantitative interpretation of the reliability enhancement resulting from the use of various commonly used error control codes such as repetitive, and (7,4) Hamming code is provided. © 2003 Elsevier Science Ltd. All rights reserved.Item 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.