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Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study
Dongni Johansson1, Fredrik Ohlsson2, David Krýsl3
1Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden.
This study shows that wearable accelerometry systems can accurately detect tonic-clonic seizures (TCSs), with algorithms achieving high sensitivity and low false positive rates for improved epilepsy management.
Area of Science:
- Neurology
- Biomedical Engineering
- Wearable Technology
Background:
- Epilepsy management relies on accurate seizure frequency assessment.
- Wearable accelerometry offers a potential solution for continuous, objective seizure monitoring.
Purpose of the Study:
- To evaluate an accelerometry-based wearable system for detecting tonic-clonic seizures (TCSs).
- To compare the accuracy of different seizure detection algorithms using distinct training and testing datasets.
Main Methods:
- Prospective video-electroencephalography (video-EEG) controlled study involving 75 epilepsy surgery candidates.
- Patients wore three-axis accelerometers on each wrist during video-EEG monitoring.
- Movement data was processed into a time-frequency feature space, and algorithms (KNN, RF, SVM) were trained and tested.
Main Results:
- Thirty-seven tonic-clonic seizures (TCSs) were analyzed across 11 patients.
- K-nearest-neighbors (KNN) achieved 100% sensitivity with 0.05 false positives per hour (FP/h).
- Random forest (RF) demonstrated 90% sensitivity with a low 0.01 FP/h.
Conclusions:
- The developed accelerometry system shows high accuracy for TCS detection.
- Low false positive rates support clinical utility for long-term seizure monitoring.
- Automated detection in free-living environments can improve seizure frequency ascertainment and management.
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