Combination of EEG and ECG for improved automatic neonatal seizure detection Barry R. Greene a, * , Geraldine B. Boylan b , Richard B. Reilly a,c , Philip de Chazal a , Sean Connolly d a School of Electrical, Electronic & Mechanical Engineering, University College Dublin, Ireland b Department of Paediatrics and Child Health, University College Cork, Ireland c Cognitive Neurophysiology Laboratory, St. Vincent’s Hospital, Fairview, Dublin, Ireland d Department of Clinical Neurophysiology, St. Vincent’s University Hospital, Dublin, Ireland Accepted 7 February 2007 Available online 29 March 2007 Abstract Objective: Neonatal seizures are the most common central nervous system disorder in newborn infants. A system that could automat- ically 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 detec- tion performance of the proposed system is a significant improvement on previous reported results for automated neonatal seizure detection. Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Neonatal seizure detection; EEG; ECG; EKG 1. Introduction Seizures in the neonate require immediate medical atten- tion and represent a distinctive sign of central nervous sys- tem dysfunction. There is increasing evidence that neonatal seizures have an adverse effect on neurodevelopmental out- come, and predispose to cognitive, behavioural, or epileptic complications in later life (Levene, 2002). Neonatal seizures occur in 6% of low birth-weight infants (Volpe, 2001) and in approximately 2% of all newborns admitted to a neona- tal ICU (Scher et al., 1993a). Seizures in this age-group are often subtle, difficult to diagnose and may be clinically silent, particularly after antiepileptic drug treatment, mak- ing diagnosis by clinical observation alone very unreliable (Boylan et al., 2002). Electroencephalography (EEG) is the most reliable method available to detect the majority of neonatal seizures but interpretation requires special expertise that is not readily available in most neonatal intensive care units least so on a 24-h basis. A system that could automatically detect the presence of seizures in 1388-2457/$32.00 Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2007.02.015 * Corresponding author. Tel.: +353 21 490 3793. E-mail address: barry.greene@ee.ucd.ie (B.R. Greene). www.elsevier.com/locate/clinph Clinical Neurophysiology 118 (2007) 1348–1359