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Automated Detection of Focal Seizures Using Subcutaneously Implanted Electrocardiographic Device: A Proof-of-concept Study.

EPILEPSIA(2023)

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摘要
Phase 2 studies showed that focal seizures could be detected by algorithms using heart rate variability (HRV) in patients with marked autonomic ictal changes. However, wearable surface electrocardiographic (ECG) devices use electrode patches that need to be changed often and may cause skin irritation. We report the first study of automated seizure detection using a subcutaneously implantable cardiac monitor (ICM; Confirm Rx, Abbott). For this proof-of-concept (phase 1) study, we recruited six patients admitted to long-term video-electroencephalographic monitoring. Fifteen-minute epochs of ECG signals were saved for each seizure and for control (nonseizure) epochs in the epilepsy monitoring unit (EMU) and in the patients' home environment (1-8 months). We analyzed the ICM signals offline, using a previously developed HRV algorithm. Thirteen seizures were recorded in the EMU, and 41 seizures were recorded in the home-monitoring period. The algorithm accurately identified 50 of 54 focal seizures (sensitivity = 92.6%, 95% confidence interval [CI] = 85.6%-99.6%). Twelve of the 13 seizures in the EMU were detected (sensitivity = 92.3%, 95% CI = 77.2%-100%), and 38 of the 41 seizures in the out-of-hospital setting were detected (sensitivity = 92.7%, 95% CI = 84.7%-100%). Four false detections were found in the 141 control (nonseizure) epochs (false alarm rate = 2.7/24 h). Our results suggest that automated seizure detection using a long-term, subcutaneous ICM device is feasible and accurate in patients with focal seizures and autonomic ictal changes.
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关键词
Epilepsy Detection,EEG Analysis,Deep Learning for EEG,Heart Rate Variability,Neurovisceral Integration
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