Out-of-hospital multimodal seizure detection: a pilot study.

Background: Out-of-hospital seizure detection aims to provide clinicians and patients with objective seizure documentation in efforts to improve the clinical management of epilepsy. In-patient studies have found that combining different modalities helps improve the seizure detection accuracy. In this study, the objective was to evaluate the viability of out-of-hospital seizure detection using wearable ECG, accelerometry and behind-the-ear electroencephalography (EEG). Furthermore, we examined the signal quality of out-of-hospital EEG recordings. Methods: Seventeen patients were monitored for up to 5 days. A support vector machine based seizure detection algorithm was applied using both in-patient seizures and out-of-hospital electrographic seizures in one patient. To assess the content of noise in the EEG signal, we compared the root-mean-square (RMS) of the recordings to a reference threshold derived from manually categorised segments of EEG recordings. Results: In total 1427 hours of continuous EEG was recorded. In one patient, we identified 15 electrographic focal impaired awareness seizures with a motor component. After training our algorithm on in-patient data, we found a sensitivity of 91% and a false alarm rate (FAR) of 18/24 hours for the detection of out-of-hospital seizures using a combination of EEG and ECG recordings. We estimated that 30.1% of the recorded EEG signal was physiological EEG, with an RMS value within the reference threshold. Conclusion: We found that detection of out-of-hospital focal impaired awareness seizures with a motor component is possible and that applying multiple modalities improves the diagnostic accuracy compared with unimodal EEG. However, significant challenges remain regarding a high FAR and that only 30.1% of the EEG data represented usable signal.

Towards a wearable multi-modal seizure detection system in epilepsy: A pilot study.

OBJECTIVE: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection. METHODS: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies. RESULTS: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research. CONCLUSIONS: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG. SIGNIFICANCE: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions.

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.

The topology of the directed clique complex as a network invariant.

We introduce new algebro-topological invariants of directed networks, based on the topological construction of the directed clique complex. The shape of the underlying directed graph is encoded in a way that can be studied mathematically to obtain network invariants such as the Euler characteristic and the Betti numbers. Two different cases illustrate the application of the Euler characteristic. We investigate how the evolution of a Boolean recurrent artificial neural network is influenced by its topology in a dynamics involving pruning and strengthening of the connections, and to show that the topological features of the directed clique complex influence the dynamical evolution of the network. The second application considers the directed clique complex in a broader framework, to define an invariant of directed networks, the network degree invariant, which is constructed by computing the topological invariant on a sequence of sub-networks filtered by the minimum in- or out-degree of the nodes. The application of the Euler characteristic presented here can be extended to any directed network and provides a new method for the assessment of specific functional features associated with the network topology.