Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Electrocardiogram

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    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.
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    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.
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    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.
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    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.