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Automated embolic signal detection using adaptive gain control and classification using ANFIS.

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    This study introduces an automated system for real-time detection of cerebral embolic signals (ES) using transcranial Doppler ultrasound (TCD) to aid stroke risk diagnosis. The novel algorithm demonstrates superior accuracy and sensitivity compared to existing methods.

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    Area of Science:

    • Biomedical Engineering
    • Medical Signal Processing
    • Neurology

    Background:

    • Cerebral embolic signals (ES) are critical indicators of stroke risk.
    • Accurate and real-time detection of ES is essential for timely diagnosis and intervention.
    • Current methods for ES detection face limitations in accuracy and real-time processing.

    Purpose of the Study:

    • To develop and validate an automated system for high-accuracy, real-time detection of cerebral embolic signals (ES).
    • To enhance the diagnostic capabilities of transcranial Doppler (TCD) ultrasound for stroke risk assessment.
    • To compare the performance of the proposed algorithm against the current state-of-the-art (HDMR).

    Main Methods:

    • Employed Adaptive Gain Control (AGC) for real-time ES capture.
    • Utilized Adaptive Wavelet Packet Transform (AWPT) and Fast Fourier Transform (FFT) for feature extraction.
    • Implemented Sequential Feature Selection and an Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier for ES identification.

    Main Results:

    • The developed system achieved 91.5% sensitivity, 90.0% specificity, and 90.5% accuracy.
    • Cross-validation demonstrated the proposed algorithm significantly outperformed the High Dimensional Model Representation (HDMR) method.
    • Statistical analysis confirmed superior detection accuracy and sensitivity (p ~ 0) compared to HDMR.

    Conclusions:

    • The proposed automated system offers a promising advancement for real-time ES monitoring.
    • This technology has significant potential as a medical support system in clinical settings for stroke risk diagnosis.
    • The algorithm's high accuracy and real-time processing capabilities make it suitable for integration with TCD devices.