Related papers: Application of Machine Learning to Sleep Stage Cla…
Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders. With the advancement of smart technologies, sensor data related to sleeping patterns can be captured easily. In this paper, we propose a…
Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achieve near-human level agreement with human-scored reference…
Humans approximately spend a third of their life sleeping, which makes monitoring sleep an integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed. The network takes…
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of…
Epileptic seizure prediction has gained considerable interest in the computational Epilepsy research community. This paper presents a Machine Learning based method for epileptic seizure prediction which outperforms state-of-the art methods.…
Anxiety is a common mental health condition characterised by excessive worry, fear and apprehension about everyday situations. Even with significant progress over the past few years, predicting anxiety from electroencephalographic (EEG)…
Electroencephalogram monitoring devices and online data repositories hold large amounts of data from individuals participating in research and medical studies without direct reference to personal identifiers. This paper explores what types…
The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test…
Being able to analyze and interpret signal coming from electroencephalogram (EEG) recording can be of high interest for many applications including medical diagnosis and Brain-Computer Interfaces. Indeed, human experts are today able to…
Objective: To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods: A deep learning -based algorithm was designed and trained using 53 EEG recordings from a…
Sleep apnea, a prevalent sleep disorder, involves repeated episodes of breathing interruptions during sleep, leading to various health complications, including cognitive impairments, high blood pressure, heart disease, stroke, and even…
The electroencephalogram (EEG) is one of the most precious technologies to understand the happenings inside our brain and further understand our body's happenings. Automatic prediction of oncoming seizures using the EEG signals helps the…
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and…
The detection of pilots' mental states is important due to the potential for their abnormal mental states to result in catastrophic accidents. This study introduces the feasibility of employing deep learning techniques to classify different…
Sleep stages pattern provides important clues in diagnosing the presence of sleep disorder. By analyzing sleep stages pattern and extracting its features from EEG, EOG, and EMG signals, we can classify sleep stages. This study presents a…
Recently, growing health awareness, novel methods allow individuals to monitor sleep at home. Utilizing sleep sounds offers advantages over conventional methods like smartwatches, being non-intrusive, and capable of detecting various…
Background and Aim: Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc. Sleep-related diseases could be diagnosed using Convolutional…
In this article we present the results of our research related to the study of correlations between specific visual stimulation and the elicited brain's electro-physiological response collected by EEG sensors from a group of participants.…
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy…
Sleep stage classification from electroencephalogram (EEG) is significant for the rapid evaluation of sleeping patterns and quality. A novel deep learning architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from…