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.

Time-Frequency Distribution Moments of Heart Rate Variability for Neonatal Seizure Detection

In this paper, we propose features extracted from the heart rate variability (HRV) based on the first and second conditional moments of time-frequency distribution (TFD) as an additional guide for seizure detection in newborn. The features of HRV in the low frequency band (LF: 0-0.07 Hz), mid frequency band (MF: 0.07-0.15 Hz), and high frequency band (HF: 0.15-0.6 Hz) have been obtained by means of the time-frequency analysis using the modified-B distribution (MBD). Results of ongoing time-frequency research are presented. Based on our preliminary results, the first conditional moment of HRV which is also known as the mean/central frequency in the LF band and the second conditional moment of HRV which is also known as the variance/instantaneous bandwidth (IB) in the HF band can be used as a good feature to discriminate the newborn seizure from the non-seizure

Foetal malformations and seizure control: 52 months data of the Australian Pregnancy Registry

The Australian Pregnancy Registry, affiliated European Register of Antiepileptic drugs in Pregnancy (EURAP), recruits informed consenting women with epilepsy on treatment with antiepileptic drugs (AEDs), those untreated, and women on AEDs for other indications. Enrolment is considered prospective if it has occurred before presence or absence of major foetal malformations (FMs) are known, or retrospective, if they had occurred after the birth of infant or detection of major FM. Telephone Interviews are conducted to ascertain pregnancy outcome and collect data about seizures. To date 630 women have been enrolled, with 565 known pregnancy outcomes. Valproate (VPA) above 1100 mg/day was associated with a significantly higher incidence of FMs than other AEDs (P < 0.05). This was independent of other AED use or potentially confounding factors on multivariate analysis (OR = 7.3, P < 0.0001). Lamotrigine (LTG) monotherapy (n = 65), has so far been free of malformations. Although seizure control was not a primary outcome, we noted that more patients on LTG than on VPA required dose adjustments to control seizures. Data indicate an increased risk of FM in women taking VPA in doses > 1100 mg/day compared with other AEDs. The choice of AED for pregnant women with epilepsy requires assessment of balance of risks between teratogenicity and seizure control.