Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
2 results
Search Results
Item Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition(Elsevier Ltd, 2022) Chandrasekar, A.; Shekar, D.D.; Hiremath, A.C.; Chemmangat, K.The electrocardiogram is a widely used measurement for individual heart conditions, and much effort has been put into automatic arrhythmia diagnosis using machine learning. However, the classification performance is hampered by the use of less representative data in conjunction with traditional machine learning models. This paper proposes a novel algorithm for pre-processing raw Electrocardiogram signals via Gaussian Assisted Signal Smoothing. In this method, the ECG signal is modeled as a low pass component and a weighted sum of Gaussians. The Gaussians are used to model the peak characteristics of the signal, effectively preserving its structure and morphology while eliminating the noise, which is evident by the enhanced peak signal-to-noise ratio of the GASS signal. The R peaks obtained from the Pan Tompkins algorithm are used to extract the heartbeats from the filtered signal using a windowing technique. A cascaded combination of a Convolutional Neural Network and a Quadratic Support Vector Machine is then used to classify the heartbeats. The CNN model has 131,661 parameters, making it much lighter than previously reported works. The MIT-BIH Arrhythmia Database was used for our experiments. Across eleven classes, our results reveal that the model has an accuracy of 97.63% and an average F1 score of 0.9263. In contrast, previous works have primarily focused on a one vs. all or a five-class classification. From a signal processing standpoint, the proposed method offers a promising solution for Signal Filtering and Arrhythmia Classification. © 2021 Elsevier LtdItem A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis(Institute of Physics, 2024) Kumar Koppolu, P.; Chemmangat, K.Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.