TY  - JOUR
  - Greene, BR, Boylan, GB, Reilly, RB, de Chazal, P, Connolly, S
  - 2007
  - June
  - Clinical Neurophysiology
  - Combination of EEG and ECG For Improved Automatic Neonatal Seizure Detection
  - Validated
  - ()
  - 118
  - 6
  - 1348
  - 1359
  - 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. (C) 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved..
  - DOI 10.1016/j.clinph.2007.02.015
DA  - 2007/06
ER  - 

@article{V726676,
   = {Greene,  BR and  Boylan,  GB and  Reilly,  RB and  de Chazal,  P and  Connolly,  S },
   = {2007},
   = {June},
   = {Clinical Neurophysiology},
   = {Combination of EEG and ECG For Improved Automatic Neonatal Seizure Detection},
   = {Validated},
   = {()},
   = {118},
   = {6},
  pages = {1348--1359},
   = {{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. (C) 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved..}},
   = {DOI 10.1016/j.clinph.2007.02.015},
  source = {IRIS}
}