Automatic Video Analysis and Classification of Sleep-related Hypermotor seizures and Disorders of Arousal.

Moro, Matteo; Pastore, Vito Paolo; Marchesi, Giorgia; Proserpio, Paola; Tassi, Laura; Castelnovo, Anna; Manconi, Mauro; Nobile, Giulia; Cordani, Ramona; Gibbs, Steve A; Odone, Francesca; Casadio, Maura; Nobili, Lino (2023). Automatic Video Analysis and Classification of Sleep-related Hypermotor seizures and Disorders of Arousal. Epilepsia, 64(6), pp. 1653-1662. Wiley 10.1111/epi.17605

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OBJECTIVE

Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy with seizures occurring mostly during sleep. SHE seizures present different motor characteristics ranging from dystonic posturing to hyperkinetic motor patterns, sometimes associated with affective symptoms and complex behaviors. Disorders of Arousal (DOA) are sleep disorders with paroxysmal episodes that may present analogies with SHE seizures. Accurate interpretation of the different SHE patterns and their differentiation from DOA manifestations can be difficult, expensive, and require highly skilled personnel not always available. Furthermore, it is operator dependent.

METHODS

Common techniques for human motion analysis, such as wearable sensors (e.g., accelerometers) and motion capture systems, have been considered to overcome these problems. Unfortunately, these systems are cumbersome and they require trained personnel for markers and sensors positioning, limiting their use in the epilepsy domain. To overcome these problems, recently, a lot of effort has been spent in studying automatic methods based on video analysis for the characterization of human motion. Systems based on computer vision and deep learning have been exploited in many fields, but epilepsy has received limited attention.

RESULTS

In this paper we present a pipeline composed by a set of 3D Convolutional Neural Networks that, starting from video recordings, reached an overall accuracy of 80% in the classification of different SHE semiology patterns and DOA.

SIGNIFICANCE

The preliminary results obtained in this study highlighted the fact that our deep learning pipeline could be used by physicians as a tool to support them in the differential diagnosis of the different patterns of SHE and DOA, and encourage for further investigation.

Interest & Impact

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5 Citations in Web of Science ®
6 Citations in Scopus

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