OBJECTIVE: Neonatal seizures are the most common central nervous system disorder in newborn infants. A system that could automatically detect the presence of seizures in neonates would be a significant advance facilitating timely medical intervention. METHODS: A novel method is proposed for the robust detection of neonatal seizures through the combination of simultaneously-recorded electroencephalogram (EEG) and electrocardiogram (ECG). A patient-specific and a patient-independent system are considered, employing statistical classifier models. RESULTS: Results for the signals combined are compared to results for each signal individually. For the patient-specific system, 617 of 633 (97.52%) expert-labelled seizures were correctly detected with a false detection rate of 13.18%. For the patient-independent system, 516 of 633 (81.44%) expert-labelled seizures were correctly detected with a false detection rate of 28.57%. CONCLUSIONS: A novel algorithm for neonatal seizure detection is proposed. The combination of an ECG-based classifier system with a novel multi-channel EEG-based classifier system has led to improved seizure detection performance. The algorithm was evaluated using a large data-set containing ECG and multi-channel EEG of realistic duration and quality. SIGNIFICANCE: Analysis of simultaneously-recorded EEG and ECG represents a new approach in seizure detection research and the detection performance of the proposed system is a significant improvement on previous reported results for automated neonatal seizure detection.
OBJECTIVE:Neonatal seizures are the most common central nervous system disorder in newborn infants. A system that could automatically detect the presence of seizures in neonates would be a significant advance facilitating timely medical intervention. METHODS: A novel method is proposed for the robust detection of neonatal seizures through the combination of simultaneously-recorded electroencephalogram (EEG) and electrocardiogram (ECG). A patient-specific and a patient-independent system are considered, employing statistical classifier models. RESULTS: Results for the signals combined are compared to results for each signal individually. For the patient-specific system, 617 of 633 (97.52%) expert-labelled seizures were correctly detected with a false detection rate of 13.18%. For the patient-independent system, 516 of 633 (81.44%) expert-labelled seizures were correctly detected with a false detection rate of 28.57%. CONCLUSIONS: A novel algorithm for neonatal seizure detection is proposed. The combination of an ECG-based classifier system with a novel multi-channel EEG-based classifier system has led to improved seizure detection performance. The algorithm was evaluated using a large data-set containing ECG and multi-channel EEG of realistic duration and quality. SIGNIFICANCE: Analysis of simultaneously-recorded EEG and ECG represents a new approach in seizure detection research and the detection performance of the proposed system is a significant improvement on previous reported results for automated neonatal seizure detection.
Authors: Joyeeta Mitra; John R Glover; Periklis Y Ktonas; Arun Thitai Kumar; Amit Mukherjee; Nicolaos B Karayiannis; James D Frost; Richard A Hrachovy; Eli M Mizrahi Journal: J Clin Neurophysiol Date: 2009-08 Impact factor: 2.177
Authors: Massimiliano de Zambotti; John Trinder; Alessandro Silvani; Ian M Colrain; Fiona C Baker Journal: Neurosci Biobehav Rev Date: 2018-03-30 Impact factor: 8.989
Authors: Joel R Martin; Paolo G Gabriel; Jeffrey J Gold; Richard Haas; Suzanne L Davis; David D Gonda; Cynthia Sharpe; Scott B Wilson; Nicolas C Nierenberg; Mark L Scheuer; Sonya G Wang Journal: J Clin Neurophysiol Date: 2022-03-01 Impact factor: 2.590