Topics

Clinical Trials About "deep learning model accurately identify sleep stages" RSS

03:56 EDT 30th March 2020 | BioPortfolio

We list hundreds of Clinical Trials about "deep learning model accurately identify sleep stages" on BioPortfolio. We draw our references from global clinical trials data listed on ClinicalTrials.gov and refresh our database daily.

More Information about "deep learning model accurately identify sleep stages" on BioPortfolio

We have published hundreds of deep learning model accurately identify sleep stages news stories on BioPortfolio along with dozens of deep learning model accurately identify sleep stages Clinical Trials and PubMed Articles about deep learning model accurately identify sleep stages for you to read. In addition to the medical data, news and clinical trials, BioPortfolio also has a large collection of deep learning model accurately identify sleep stages Companies in our database. You can also find out about relevant deep learning model accurately identify sleep stages Drugs and Medications on this site too.

Showing "deep learning model accurately identify sleep stages" Clinical Trials 1–25 of 18,000+

Extremely Relevant

AI-Assisted Facial Surgical Planning

Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis.


Relevant

Deep-learning Based Location of Spine CT

CT-guided epidural steroid injection (ESI) thrives among interventional radiologists. Although X-ray fluoroscopy still remains as the gold standard to guide ESI, CT guidance has the merits of accurate location of needle tip and relatively low radiation doses with intermittent mode. However, it is time-consuming for doctors to select the optimal plane for needle placement and manually segment spinal structures. The main of the study is to automatically identify the optimal plane...

Comparison of Dreem to Clinical PSG for Sleep Monitoring in Healthy Adults

This study aims to evaluate the accuracy of automated sleep analysis by the Dreem dry-EEG headband and deep learning algorithm in comparison to the consensus of 5 sleep technologists' manual scoring of a gold-standard clinical polysomnogram (PSG) record in healthy adult volunteers during an overnight clinic-based sleep study.


Validation of Readiband™ Actigraph and Associated Sleep/Wake Classification Algorithms

This pilot study will assess feasibility and to obtain initial estimates of efficacy of Sleep Activity and Task Effectiveness (SAFTE) model, which can accurately estimate the impact of scheduling factors and sleep history on both safety and productivity. The SAFTE model will be used to asses cancer-related fatigue and study potential associations of change in sleep patterns to tumor recurrence in patients with high grade glioma. Data will be collected using the Readiband™ Sle...

A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the o...

Analyze Sleep Stages by Portable ECG Device

Sleep is a fundamental need for human and is associated with the working performance and the disease occurrence. Furthermore, the amount of people with sleep disorder or apnea increased largely. Thus, the analysis of sleep stages and the measurement of sleep quality became more important recently. In clinical settings, the analysis of daily sleep quality depends on actigraphy and sleep log. However, the uses of actigraphy and sleep log are not really convenient for patients. In...

Comparison of Dreem to Clinical PSG for Sleep Monitoring in Apnea Patients

This study aims to evaluate the accuracy of apnea detection and automated sleep analysis by the Dreem dry-EEG headband and deep learning algorithm in comparison to the consensus of 5 sleep technologists' manual scoring of a gold-standard clinical polysomnogram (PSG) record in adults during a physician-referred overnight sleep study due to suspicion of sleep-disordered breathing.

Association of Eye With Hepatobiliary Disorders :Qualitative Analysis Via Deep Learning

Artificial Intelligence and Machine Learning techniques may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep-learning models to predict the hepatobiliary disease by using ocular images.

Using Deep Learning Methods to Analyze Automated Breast Ultrasound Images, to Establish a Diagnosis, Therapy Assessment and Prognosis Prediction Model of Breast Cancer.

The purpose of this study is using a deep learning method to analyze the automated breast ultrasound (ABUS) imagings, establish and evaluate a diagnosis, therapy assessment and prognosis prediction model of breast cancer. The model would provide important references for further early prevention, early diagnosis and personalized treatment.

Artificial Intelligence/Machine Learning Modeling on Time to Palliative Care Review in an Inpatient Hospital Population

Investigators are testing whether machine learning prediction models integrated into a health care model will accurately identify participants who may benefit from a comprehensive review by a palliative care specialist, and decrease time to receiving a palliative care consult in an inpatient setting.

Sleep-dependent Learning in Aging

The specific objective of this proposed research is to understand whether deficits in sleep-dependent memory changes reflect age-related changes in sleep, memory, or both. The central hypothesis is that changes in both memory and sleep contribute to age-related changes in sleep-dependent memory processing. To this end, the investigators will investigate changes in learning following intervals of sleep (overnight and nap) and wake in young and older adults.

Sleep Apnoea in Haemodialysis

Sleep disturbance is a significant issue in people undergoing dialysis. More than 80% of haemodialysis patients complain of difficulty sleeping. Inadequate sleep can cause poor daytime function and increased risk of motor vehicle incidents. One of the common reasons for sleep disturbance in dialysis patients is sleep apnoea. Sleep apnoea involves pauses in breathing that occur during sleep. Each pause can last only a few seconds or minutes. Severe sleep apnoea reduces oxygen s...

Effects of Eszopiclone on Sleep-dependent Learning in Schizophrenia

This study will determine whether eszopiclone will normalize sleep patterns and restore sleep-dependent enhancement of motor skill learning in patients with schizophrenia. The investigators will compare subjects taking a placebo to those taking 3 mg of eszopiclone with regard to: sleep architecture and sleep latency as measured by actigraphy and polysomnography and sleep-dependent motor skill learning.

Set Your Goal: Engaging Go/No-Go Active Learning

This study will test a computational model reinforcement learning in depression and anxiety and test the extent to which the computational model predicts response to an adapted version of behavioral activation psychotherapy. The model will be based on a data from a computer task of reinforcement learning during 3T functional magnetic resonance imaging at baseline.

A Naturalistic Study Investigating Sleep and Cognitive Learning in Children With and Without Tic Disorders

This study will explore the relationship between sleep, learning, cognition, mood and behaviour in children with Tic Disorders (Tourette Syndrome and Chronic Tic Disorder) compared to typically developing peers.

Role of Sleep in Cardiovascular Functions

Sleep and particularly deep sleep are playing an important role for brain and body health. Poor sleep has been associated with risk factors for cardiovascular disease and moreover, is hypothesized to increased mortality risk of cardiovascular diseases. Yet, the role of specific sleep processes for cardiovascular function remains unclear. Particularly deep sleep, which is manifested by large amplitude, low frequency oscillations is of importance for the restorative functions of ...

Diagnostic Performance of Deep Learning for Angle Closre

Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy is a contact examination and needs a long learning curve. Anterior segment optical coherence tomography (AS-OCT) is a non-contact test which can obtain three dimensional images of the anterior segment within seconds. Therefore, we designed the study to verify if AS-OCT based deep learning algo...

Brain Maturation and Sleep

In this study, the investigator evaluates whether there are age-specific diurnal changes in markers of cortical plasticity in children, adolescents and adults. The question will be investigated by the quantification of brain metabolites and structural brain volumes using magnet resonance imaging (MRI) and electrophysiological markers using sleep encephalography (sleep EEG). In a second step, it will be tested how these markers of cortical plasticity change depending on a modula...

Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows, to identify those at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. We will also compare the sensitivity and specificity of UmbiFlow device with the machine learning model in predicting ad...

Detection of Airway Obstruction by Manometry in Different Sleep Stages and Comparing it to DISE

Patients with OSA receive manometry measurements with Apneagraph (AG) during one night of sleep. All patients are simultaneously evaluated with polysomnography. Patients who are not eligible for CPAP therapy are additionally studied with drug-induced sleep endoscopy (DISE). The frequency and obstructions patterns in different sleep stages are assessed. In addition obstruction patterns detected with AG are compared with DISE examination in the selected cases.

Multistate Relative Survival Model

Colorectal cancer (CRC) has high incidence and is associated with high case fatality. In France, the 5-year survival, pooled across all cancer stages at diagnosis, ranges from 57% in men to 60% in women. About one third of patients diagnosed with CRC will develop a metachronous recurrence during the following years. It is of paramount importance to accurately identify factors associated with the increased risk of progression and death, in order to develop effective follow-up an...

Sleep and Memory in Children

Sleep exerts a dual effect on learning: on the one hand, good sleep quality allows good daytime aptitudes leading to knowledge acquisition. On the other hand, sleep after learning is necessary for knowledge consolidation. A key role of sleep has clearly been demonstrated in memory consolidation in adults. Sleep leads to strengthen memory by promoting brain plasticity. Surprisingly, sleep influence on learning stabilization has scarcely been studied during childhood and in child...

Estimating Progression Rate and Distinguishing Parkinsonian Syndromes Using Imaging and Deep Learning

The goal of this study is to develop a model to predict the progression rate of Parkinson disease (PD) and use this model to distinguish between similar diseases such as PD, Progressive Supranuclear Palsy (PSP), and Multiple System Atrophy (MSA).

Extinction Learning in Youth With Tourette Syndrome

Aversive sensory phenomena such as premonitory urges play a central role in the behavioral treatment model of tics. Extinction learning and extinction recall are learning processes implicated within this model, but have not been directly evaluated in youth with Tourette syndrome (TS). This study examines extinction learning and extinction recall in youth with TS using an experimental task. This study will also explore the relationship between extinction processes (i.e., extinct...

Enhancement of Sleep With Wearables

Sleep, specifically deep sleep, plays a central role in healthy brain function, cardio-vascular processes, mood and quality of life. Auditory stimulation during one night of sleep has previously been shown to improve deep sleep and along with memory formation in both young and older adults. Yet, it remains unclear whether long-term auditory stimulation considerably improves sleep quality over longer time periods and how it affects daytime functioning such as cognition, mood, qu...


More From BioPortfolio on "deep learning model accurately identify sleep stages"

Quick Search