Conference Papers

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

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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.
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    Gesture Prediction Using Surface-EMG Signals
    (Springer Science and Business Media Deutschland GmbH, 2025) Panigrahi, S.; Seal, S.; Lal, S.; Naik, G.
    Gesture prediction plays a crucial role in enhancing human-computer interaction by enabling intuitive and natural control methods, thereby reducing reliance on traditional input devices. It significantly improves accessibility for individuals with physical disabilities by providing alternative means of communication and control. Moreover, gesture prediction has broad applications in fields such as robotics, virtual reality, and prosthetics, enhancing both the functionality and user experience of these technologies. This study presents the design and development of an Electromyogram (EMG) signal-based gesture recognition system utilizing recent Deep Learning (DL) techniques. The Hyser EMG dataset was used for experimentation, and its data was pre-processed and analyzed using both sliding window and a combination of sliding window and Fourier transform methods. The performance of the EMG signal-based gesture recognition system was evaluated and compared across different DL models. The results demonstrate that RCCGNet-based gesture prediction outperforms other models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.