Automated video-based detection of nocturnal convulsive seizures in a residential care setting

Paroxysmal Cerebral Disorder

Special Issue on Principles of Autonomous Neurodynamics and Understanding Epilepsy Introduction

Paroxysmal Cerebral Disorder

Quantification of unidirectional nonlinear associations between multidimensional signals.

In this paper, we present a rigorous, general definition of the nonlinear association index, known as h2. Proving equivalence between different definitions we show that the index measures the best dynamic range of any nonlinear map between signals. We present also a construction for removing the influence of one signal from another, providing, thus, the basis of an independent component analysis. Our definition applies to arbitrary multidimensional vector-valued signals and depends on an aperture function. In this way, the bin-related classic definition of h2 can be generalized. We show that upon choosing suitable aperture functions the bin-related intuitive definition can be deduced. Special attention is dedicated to the direction of the association index that in general is taken in only one sense. We show that for linearly coupled signals high associations are always bidirectional. As a consequence, high asymmetric nonlinear associations are indicators of nonlinear relations, possibly critical, between the dynamic systems underlying the measured signals. We give a simple simulated example to illustrate this property. As a potential clinical application, we show that unidirectional associations between electroencephalogram (EEG) and electromyogram (EMG) recorded from patient with pharmacologically intractable epilepsy can be used to study the cortical involvement in the generation of motor seizures

Automated video-based detection of nocturnal motor seizures in children

Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real-time video-based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video-sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3-17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%-100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0-0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior-related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real-time use.Paroxysmal Cerebral Disorder

Coherency and connectivity in oscillating neural networks: linear partialization analysis

This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization. Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems (common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization, removing the influence of the common input. We further explored the network responses to periodic external input. We found that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold, characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization work

Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse

Paroxysmal Cerebral Disorder