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Contactless Polysomnography: What Radio Waves Tell Us about Sleep
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful.
Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients
Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.
Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation
The objective is to automatically detect abnormal respiration and estimate the Apnea-Hypopnea-Index (AHI) with a wearable respiratory device, compared to an SpO2 signal or polysomnography using a large (n = 412) dataset serving as ground truth.
Automated Respiratory Event Detection Using Deep Neural Networks
Using 9, 656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals.
Natural Language Processing to Detect Cognitive Concerns in Electronic Health Records Using Deep Learning
Dementia is under-recognized in the community, under-diagnosed by healthcare professionals, and under-coded in claims data.
HAMLET: Interpretable Human And Machine co-LEarning Technique
Although cEEG monitoring yields large volumes of data, labeling costs and difficulty make it hard to build a classifier.
SLEEPNET: Automated Sleep Staging System via Deep Learning
Sleep disorders, such as sleep apnea, parasomnias, and hypersomnia, affect 50-70 million adults in the United States (Hillman et al., 2006).
