Seizure detection algorithm for neonates based on wave-sequence analysis

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 30 s 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. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

Neonatal EEG classification using atomic decomposition

The electroencephalogram (EEG) is an important noninvasive tool used in the neonatal intensive care unit (NICU) for the neurologic evaluation of the sick newborn infant. It provides an excellent assessment of at-risk newborns and formulates a prognosis for long-term neurologic outcome.The automated analysis of neonatal EEG data in the NICU can provide valuable information to the clinician facilitating medical intervention. The aim of this thesis is to develop a system for automatic classification of neonatal EEG which can be mainly divided into two parts: (1) classification of neonatal EEG seizure from nonseizure, and (2) classifying neonatal background EEG into several grades based on the severity of the injury using atomic decomposition. Atomic decomposition techniques use redundant time-frequency dictionaries for sparse signal representations or approximations. The first novel contribution of this thesis is the development of a novel time-frequency dictionary coherent with the neonatal EEG seizure states. This dictionary was able to track the time-varying nature of the EEG signal. It was shown that by using atomic decomposition and the proposed novel dictionary, the neonatal EEG transition from nonseizure to seizure states could be detected efficiently. The second novel contribution of this thesis is the development of a neonatal seizure detection algorithm using several time-frequency features from the proposed novel dictionary. It was shown that the time-frequency features obtained from the atoms in the novel dictionary improved the seizure detection accuracy when compared to that obtained from the raw EEG signal. With the assistance of a supervised multiclass SVM classifier and several timefrequency features, several methods to automatically grade EEG were explored. In summary, the novel techniques proposed in this thesis contribute to the application of advanced signal processing techniques for automatic assessment of neonatal EEG recordings.

Hypoxic/ischemic models in newborn piglet: Comparison of constant FiO(2) versus variable FiO(2) delivery

A comparison of a constant (continuous delivery of 4% FiO(2)) and a variable (initial 5% FiO(2) with adjustments to induce low amplitude EEG (LAEEG) and hypotension) hypoxic/ischemic insult was performed to determine which insult was more effective in producing a consistent degree of survivable neuropathological damage in a newborn piglet model of perinatal asphyxia. We also examined which physiological responses contributed to this outcome. Thirty-nine 1-day-old piglets were subjected to either a constant hypoxic/ischemic insult of 30- to 37-min duration or a variable hypoxic/ischemic insult of 30-min low peak amplitude EEG (LAEEG < 5 mu V) including 10 min of low mean arterial blood pressure (MABP < 70% of baseline). Control animals (n = 6) received 21% FiO(2) for the duration of the experiment. At 72 h, the piglets were euthanased, their brains removed and fixed in 4% paraformaldehyde and assessed for hypoxic/ischemic injury by histological analysis. Based on neuropathology scores, piglets were grouped as undamaged or damaged; piglets that did not survive to 72 h were grouped separately as dead. The variable insult resulted in a greater number of piglets with neuropathological damage (undamaged = 12.5%, damaged = 68.75%, dead = 18.75%) while the constant insult resulted in a large proportion of undamaged piglets (undamaged = 50%, damaged = 22.2%, dead = 27.8%). A hypoxic insult varied to maintain peak amplitude EEG < 5 mu V results in a greater number of survivors with a consistent degree of neuropathological damage than a constant hypoxic insult. Physiological variables MABP, LAEEG, pH and arterial base excess were found to be significantly associated with neuropathological outcome. (c) 2006 Elsevier B.V. All rights reserved.