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Automated embolic signal detection using Deep Convolutional Neural Network.

Praotasna Sombune, Phongphan Phienphanich, Sutanya Phuechpanpaisal

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
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    This study shows Deep Convolutional Neural Networks (CNNs) can accurately detect cerebral embolic signals (ES) from transcranial Doppler (TCD) ultrasound, aiding real-time stroke risk diagnosis.

    Area of Science:

    • Biomedical Engineering
    • Medical Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Cerebral embolic signals (ES) detected via transcranial Doppler (TCD) ultrasound are crucial indicators of stroke risk.
    • Accurate and real-time detection of ES is essential for timely clinical intervention.
    • Traditional methods for ES detection often rely on handcrafted features, which can be time-consuming and less adaptable.

    Purpose of the Study:

    • To investigate the efficacy of Deep Neural Networks (DNNs), specifically Convolutional Neural Networks (CNNs), for automated cerebral embolic signal (ES) detection.
    • To develop a system that integrates with TCD devices for real-time stroke risk assessment.
    • To bypass the need for manual feature extraction and selection in ES detection.

    Main Methods:

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    • Utilized spectrograms of TCD signals as input for a Deep Convolutional Neural Network (CNN).
    • Employed an Adaptive Gain Control (AGC) approach for real-time capture of suspected ES.
    • Evaluated the CNN's ability to classify signals as ES, artifact (AF), or normal (NR) intervals.
    • Tested the system on data from 19 subjects undergoing procedures that generate emboli.

    Main Results:

    • The CNN-based system achieved an average sensitivity of 83.0%, specificity of 80.1%, and accuracy of 81.4%.
    • Demonstrated significantly reduced development time compared to traditional methods.
    • The system effectively distinguished between ES, artifacts, and normal intervals.

    Conclusions:

    • Deep Convolutional Neural Networks offer a promising, accurate, and efficient approach for automated cerebral embolic signal detection.
    • The developed system has potential applications in various clinical ES monitoring settings and wearable devices.
    • Future work with larger datasets and computational resources will further enhance performance and adaptability for diverse demographic needs.