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Seizure detection algorithm for neonates based on wave-sequence analysis.

Journal Article


Abstract


  • Objective

    The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant.

    Methods

    The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms.

    Results

    The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour.

    Conclusions

    The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms.

    Significance

    The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring.

Publication Date


  • 2006

Citation


  • Navakatikyan, M. A., Colditz, P. B., Burke, C. J., Inder, T. E., Richmond, J., & Williams, C. E. (2006). Seizure detection algorithm for neonates based on wave-sequence analysis.. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 117(6), 1190-1203. doi:10.1016/j.clinph.2006.02.016

Web Of Science Accession Number


Start Page


  • 1190

End Page


  • 1203

Volume


  • 117

Issue


  • 6

Abstract


  • Objective

    The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant.

    Methods

    The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms.

    Results

    The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour.

    Conclusions

    The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms.

    Significance

    The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring.

Publication Date


  • 2006

Citation


  • Navakatikyan, M. A., Colditz, P. B., Burke, C. J., Inder, T. E., Richmond, J., & Williams, C. E. (2006). Seizure detection algorithm for neonates based on wave-sequence analysis.. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 117(6), 1190-1203. doi:10.1016/j.clinph.2006.02.016

Web Of Science Accession Number


Start Page


  • 1190

End Page


  • 1203

Volume


  • 117

Issue


  • 6