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  1. Book ; Online ; E-Book: High performance and power efficient electrocardiogram detectors

    Kumar, Ashish / Kumar, Manjeet / Komaragiri, Rama S.

    (Energy Systems in Electrical Engineering)

    2023  

    Author's details Ashish Kumar, Manjeet Kumar, Rama S. Komaragiri
    Series title Energy Systems in Electrical Engineering
    Keywords Biomedical engineering
    Subject code 170
    Language English
    Size 1 online resource (206 pages)
    Publisher Springer
    Publishing place Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 981-19-5303-1 ; 9789811953026 ; 978-981-19-5303-3 ; 9811953023
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: Automatic seizure detection and classification using super-resolution superlet transform and deep neural network -A preprocessing-less method.

    Tripathi, Prashant Mani / Kumar, Ashish / Kumar, Manjeet / Komaragiri, Rama S

    Computer methods and programs in biomedicine

    2023  Volume 240, Page(s) 107680

    Abstract: Context: Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in ... ...

    Abstract Context: Epilepsy, characterized by recurrent seizures, is a chronic brain disease that affects approximately 50 million. Recurrent seizures characterize it. A seizure, a burst of uncontrolled electrical activity between brain cells, results in temporary changes in behavior, level of consciousness, and involuntary movements. An accurate prediction of seizures can improve the standard of living in epileptic subjects. The increasing capabilities of machine learning and computer-assisted devices can detect seizures accurately with minimal human intervention.
    Proposed approach: This paper proposes a method to detect seizure and non-seizure events using superlet transform (SLT) and a deep convolution neural network: VGG-19. The electroencephalogram (EEG) dataset from the University of Bonn is used to validate the efficacy of the proposed method.
    Methodology: SLT, a high-resolution time-frequency technique, converts EEG records into two-dimensional (2-D) images. SLT provides a high-resolution time-frequency representation reflecting the oscillation bursts in an EEG record. The time-frequency representations as 2-D images are fed to a pre-trained convolutional neural network: VGG-19. The last layers of VGG-19 are replaced with new layers to accommodate the different classification problems.
    Results: The proposed method achieved an accuracy of 100% for all seven seizure and non-seizure detection cases considered in this work. In the case of three and five-class classification problems, the proposed method has better accuracy than other existing methods. The CHB-MIT scalp EEG database is also used to assess the effectiveness of the proposed method, which achieved a classification accuracy of 94.3% in distinguishing between seizure and non-seizure events.
    Conclusion: The results obtained using the proposed methodology show the efficacy of the proposed method in accurately detecting seizures and other brain activity with the least pre-processing and human involvement. The proposed method can assist medical practitioners by saving their effort and time.
    MeSH term(s) Humans ; Signal Processing, Computer-Assisted ; Seizures/diagnosis ; Neural Networks, Computer ; Epilepsy/diagnosis ; Machine Learning ; Electroencephalography/methods
    Language English
    Publishing date 2023-06-22
    Publishing country Ireland
    Document type Journal Article
    ZDB-ID 632564-6
    ISSN 1872-7565 ; 0169-2607
    ISSN (online) 1872-7565
    ISSN 0169-2607
    DOI 10.1016/j.cmpb.2023.107680
    Database MEDical Literature Analysis and Retrieval System OnLINE

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