AIMC Topic: Sleep
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Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.
Sensors (Basel, Switzerland)
Jul 25, 2024
Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are c...
State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.
Scientific reports
Jul 14, 2024
Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was traine...
Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.
Sensors (Basel, Switzerland)
Jul 3, 2024
Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is po...
Predicting long-term sleep deprivation using wearable sensors and health surveys.
Computers in biology and medicine
Jul 2, 2024
Sufficient sleep is essential for individual well-being. Inadequate sleep has been shown to have significant negative impacts on our attention, cognition, and mood. The measurement of sleep from in-bed physiological signals has progressed to where co...
Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram.
PloS one
Jul 2, 2024
BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, it is not clear whether sedation-induced states are similar to the biological sleep. We explored if sedative-induced states resemble biological sleep u...
Predicting daily recovery during long-term endurance training using machine learning analysis.
European journal of applied physiology
Jun 20, 2024
PURPOSE: The aim of this study was to determine if machine learning models could predict the perceived morning recovery status (AM PRS) and daily change in heart rate variability (HRV change) of endurance athletes based on training, dietary intake, s...
Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.
Scientific reports
May 31, 2024
Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical period during the life course. Analyzing data from 2971 participants (M = 11.94, SD = 0.64 years) wear...
Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.
Sensors (Basel, Switzerland)
May 24, 2024
Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such meas...
A machine learning model to predict the risk of perinatal depression: Psychosocial and sleep-related factors in the Life-ON study cohort.
Psychiatry research
May 23, 2024
Perinatal depression (PND) is a common complication of pregnancy associated with serious health consequences for both mothers and their babies. Identifying risk factors for PND is key to early detect women at increased risk of developing this conditi...
The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.
Scientific reports
May 13, 2024
Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome...
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