Insomnia Identification via Electroencephalography

Insomnia is a serious sleep disorder caused by abnormal or excessive neural activity in the brain. An estimated 50 million people worldwide are thought to be affected by this condition, which is the second most severe neurological disease after stroke. In order to ensure a quick recovery, an early and accurate diagnosis of insomnia enables more effective drug and treatment administration. This study proposes a method that uses deep learning to automatically identify patients with insomnia. A set of optimal features are extracted from spectral and temporal domains, including the relative power of {\sigma}, \b{eta} and {\gamma} bands, the total power, the absolute slow wave power, the power ratios of {\theta}, {\alpha}, {\gamma}, \b{eta}, {\theta}/{\alpha}, {\theta}/\b{eta}, {\alpha}/{\gamma} and {\alpha}/\b{eta}, mean, zero crossing rate, mobility, complexity, sleep efficiency and total sleep time, to accurately quantify the differences between insomnia patients and healthy subjects and develops a 1D CNN model for the classification process. With the experiments use Fp2 and C4 EEG channels with 50 insomnia patients and 50 healthy subjects, the proposed model arrives 99.34% accuracy without sleep stage annotation. Using the features only from a single channel, the study proposes a smart solution for insomnia patients which allows machine learning to be to simplify current sleep monitoring hardware and improve in-home ambulatory monitoring.

Enhancing object detection robustness: A synthetic and natural perturbation approach

Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robustness of object detection models against natural perturbations, such as varying lighting conditions, blur, and brightness. We analyze four state-of-the-art deep neural network models, Detr-ResNet-101, Detr-ResNet-50, YOLOv4, and YOLOv4-tiny, using the COCO 2017 dataset and ExDark dataset. By simulating synthetic perturbations with the AugLy package, we systematically explore the optimal level of synthetic perturbation required to improve the models robustness through data augmentation techniques. Our comprehensive ablation study meticulously evaluates the impact of synthetic perturbations on object detection models performance against real-world distribution shifts, establishing a tangible connection between synthetic augmentation and real-world robustness. Our findings not only substantiate the effectiveness of synthetic perturbations in improving model robustness, but also provide valuable insights for researchers and practitioners in developing more robust and reliable object detection models tailored for real-world applications.