EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575[Formula: see text]h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data.
Neonatal seizures patterns evolve with changing frequency, morphology and propagation. This study is an initial attempt to incorporate the characteristics of temporal evolution of neonatal seizures into our developed neonatal seizure detector. The previously designed SVM-based neonatal seizure detector is modified by substituting the Gaussian kernel with the Gaussian dynamic time warping kernel, to enable the SVM to classify variable length sequences of feature vectors of neonatal seizures. The preliminary results obtained compare favorably with the conventional SVM. The fusion of the two approaches is expected to improve the current state of the art neonatal seizure detection system.
Hypoxic-ischemic HI injury at the time of birth could lead to severe neurological dysfunction at an older age. Therapeutic hypothermia can be used to treat HI if severity of injury is determined within 6 hours of birth. EEG is generally used to assess the brain injury but it is neither widely recorded after birth nor is the expertise to interpret it commonly available. This study presents a novel system to classify HI injury using heart rate variability. The system makes decisions based on long-term statistical features extracted from the short-term HRV features. The preliminary results show the promising performance and robustness of the proposed method given a poor quality dataset. This tool can serve as decision support system in remote maternity units to help clinical staff to initiate hypothermia.
During a course of electroconvulsive therapy (ECT), the level of currency necessary to induce an epileptic seizure in a patient may either remain relatively stable or-more often-may require repeated upward adjustment over time due to a constantly increasing seizure threshold. We aimed to determine whether a common polymorphism of the brain-derived neurotrophic factor (BDNF), which constitutes an important and ubiquitously expressed neurotrophine in the brain, affects the stimulation threshold of ECTs required to induce an epileptic seizure over time. Twenty-seven adult patients who underwent at least 12 consecutive ECT sessions were analyzed for the stimulation intensities required during the course of the stimulation as well as their BDNF gene status. We could not find a relation between the Val/Met polymorphism of the BDNF and the development of the seizure threshold during the course of the ECT sessions. Mechanisms and predispositions other than the BDNF polymorphism investigated in this study are responsible for the change in seizure thresholds over the course of ECT.
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