Automated anomalous child repetitive head movement identification through transformer networks.

The increasing prevalence of behavioral disorders in children is of growing concern within the medical community. Recognising the significance of early identification and intervention for atypical behaviors, there is a consensus on their pivotal role in improving outcomes. Due to inadequate facilities and a shortage of medical professionals with specialized expertise, traditional diagnostic methods have been unable to effectively address the rising incidence of behavioral disorders. Hence, there is a need to develop automated approaches for the diagnosis of behavioral disorders in children, to overcome the challenges with traditional methods. The purpose of this study is to develop an automated model capable of analyzing videos to differentiate between typical and atypical repetitive head movements in. To address problems resulting from the limited availability of child datasets, various learning methods are employed to mitigate these issues. In this work, we present a fusion of transformer networks, and Non-deterministic Finite Automata (NFA) techniques, which classify repetitive head movements of a child as typical or atypical based on an analysis of gender, age, and type of repetitive head movement, along with count, duration, and frequency of each repetitive head movement. Experimentation was carried out with different transfer learning methods to enhance the performance of the model. The experimental results on five datasets: NIR face dataset, Bosphorus 3D face dataset, ASD dataset, SSBD dataset, and the Head Movements in the Wild dataset, indicate that our proposed model has outperformed many state-of-the-art frameworks when distinguishing typical and atypical repetitive head movements in children.

Automated sleep scoring: A review of the latest approaches.

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a human expert, according to official standards. It could appear then a suitable task for modern artificial intelligence algorithms. Indeed, machine learning algorithms have been applied to sleep scoring for many years. As a result, several software products offer nowadays automated or semi-automated scoring services. However, the vast majority of the sleep physicians do not use them. Very recently, thanks to the increased computational power, deep learning has also been employed with promising results. Machine learning algorithms can undoubtedly reach a high accuracy in specific situations, but there are many difficulties in their introduction in the daily routine. In this review, the latest approaches that are applying deep learning for facilitating and accelerating sleep scoring are thoroughly analyzed and compared with the state of the art methods. Then the obstacles in introducing automated sleep scoring in the clinical practice are examined. Deep learning algorithm capabilities of learning from a highly heterogeneous dataset, in terms both of human data and of scorers, are very promising and should be further investigated.