相关论文: Automatic Sleep Stage Classification with Cross-mo…
Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance…
Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages,…
Recently proposed automatic pathological speech classification techniques use unsupervised auto-encoders to obtain a high-level abstract representation of speech. Since these representations are learned based on reconstructing the input,…
Sleep staging is essential for diagnosing sleep disorders and assessing neurological health. Existing automatic methods typically extract features from complex polysomnography (PSG) signals and train domain-specific models, which often lack…
Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram (PSG) from a single night. Although deep learning models…
Sleep is vital for people's physical and mental health, and sound sleep can help them focus on daily activities. Therefore, a sleep study that includes sleep patterns and sleep disorders is crucial to enhancing our knowledge about…
Labeled speech data from patients with Parkinsons disease (PD) are scarce, and the statistical distributions of training and test data differ significantly in the existing datasets. To solve these problems, dimensional reduction and sample…
In this paper, we propose a novel method and a practical approach to predicting early onsets of sleep syndromes, including restless leg syndrome, insomnia, based on an algorithm that is comprised of two modules. A Fast Fourier Transform is…
Individual's general well-being is greatly impacted by mental health conditions including depression and Post-Traumatic Stress Disorder (PTSD), underscoring the importance of early detection and precise diagnosis in order to facilitate…
Sleep staging is a clinically important task for diagnosing various sleep disorders, but remains challenging to deploy at scale because it because it is both labor-intensive and time-consuming. Supervised deep learning-based approaches can…
Detecting Parkinson's Disease in its early stages using EEG data presents a significant challenge. This paper introduces a novel approach, representing EEG data as a 15-variate series of bandpower and peak frequency values/coefficients. The…
Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep…
Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies…
Although deep learning algorithms have proven their efficiency in automatic sleep staging, the widespread skepticism about their "black-box" nature has limited its clinical acceptance. In this study, we propose WaveSleepNet, an…
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…
The risk of Parkinson's disease (PD) is extremely serious, and PD speech recognition is an effective method of diagnosis nowadays. However, due to the influence of the disease stage, corpus, and other factors on data collection, the ability…
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we…
Deep learning has shown significant potential in diagnosing neurodegenerative diseases from MRI data. However, most existing methods rely heavily on large volumes of labeled data and often yield representations that lack interpretability.…
Polysomnographic sleep analysis is the standard clinical method to accurately diagnose and treat sleep disorders. It is an intricate process which involves the manual identification, classification, and location of multiple sleep event…
The rapid emergence of highly adaptable and reusable artificial intelligence (AI) models is set to revolutionize the medical field, particularly in the diagnosis and management of Parkinson's disease (PD). Currently, there are no effective…