High Accuracy Epileptic Seizure Detection System Based on Wearable Devices Using Support Vector Machine Classifier

Fawzy, Mohamed; Mostafa, Hassan

doi:10.1109/icm52667.2021.9664898

Cited by 3 publications

(1 citation statement)

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“…The model achieved a specificity and sensitivity of about 90 % each with a lead time of 700 ms, eclipsing the 200 to 400 ms lead times described in the literature for IMU-based systems. In another application of activity assessment, seizure detection has recently received attention for the lack of robust monitoring solutions outside of the clinic [99][100] and for the importance of effective monitoring in the evaluation of seizure treatment. Fawzy and Mostafa used three commercial devices to collect accelerometry, PPG, electrodermal activity (EDA, a measure of skin impedance related to sweat excretion), temperature, and EMG.…”

Section: Rehabilitation and Activity Assessmentmentioning

confidence: 99%

Advances in Biosignal Sensing and Signal Processing Methods with Wearable Devices

2022

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Analysis & Sensing www.analysis-sensing.org Review doi.org/10.1002/anse.202200062Wearable devices have received significant attention recently for their ability to monitor critical physiological signals noninvasively, such as electrocardiography, electroencephalography, electromyography, and photoplethysmography. These biointegrated wearable systems can potentially fill gaps in conventional clinical practice by providing highly cost-effective health characterization and portable continuous health monitoring. Further, the physiological signals measured by wearables require post-processing to derive meaningful values, such as heart rate or blood oxygen saturation. This requirement, in conjunction with the smaller form factor and limited sensor count of the miniaturized systems, often necessitates robust signal processing and data analysis to approach the stringent performance specifications of conventional medical devices, and machine learning techniques have found success in filling this analytical role for their ability to learn complex functional relationships. Thus, this review outlines a systematic summary of the latest research on various wearable devices and their biosignal sensing and signal processing methods, emphasizing machine learning. We also discuss the developmental challenges and advantages of current machine-learning methods, while suggesting research directions for future studies.

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Section: Rehabilitation and Activity Assessmentmentioning

confidence: 99%