Ivan Osorio1, B F J Manly2. 1. University of Kansas Medical Center, United States. Electronic address: iosorio@kumc.edu. 2. Manly-Biostatistics Ltd., United States.
Abstract
PURPOSE: Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. METHODS: Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. RESULTS: Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. CONCLUSION: Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures.
PURPOSE: Heart rate-based seizure detection is a viable complement or alternative to ECoG/EEG. This study investigates the role of various biological factors on the probability of clinical seizure detection using heart rate. METHODS: Regression models were applied to 266 clinical seizures recorded from 72 subjects to investigate if factors such as age, gender, years with epilepsy, etiology, seizure site origin, seizure class, and data collection centers, among others, shape the probability of EKG-based seizure detection. RESULTS: Clinical seizure detection probability based on heart rate changes, is significantly (p<0.001) shaped by patients' age and gender, seizure class, and years with epilepsy. The probability of detecting clinical seizures (>0.8 in the majority of subjects) using heart rate is highest for complex partial seizures, increases with a patient's years with epilepsy, is lower for females than for males and is unrelated to the side of hemisphere origin. CONCLUSION: Clinical seizure detection probability using heart rate is multi-factorially dependent and sufficiently high (>0.8) in most cases to be clinically useful. Knowledge of the role that these factors play in shaping said probability will enhance its applicability and usefulness. Heart rate is a reliable and practical signal for extra-cerebral detection of clinical seizures originating from or spreading to central autonomic network structures.
Authors: Sharon Chiang; Rosalind W Picard; Winston Chiong; Robert Moss; Gregory A Worrell; Vikram R Rao; Daniel M Goldenholz Journal: Neurology Date: 2021-07-27 Impact factor: 11.800
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