Performance Investigation of Somfit Sleep Staging Algorithm.

Purpose: To investigate accuracy of the sleep staging algorithm in a new miniaturized home sleep monitoring device - Compumedics® Somfit. Somfit is attached to patient's forehead and combines channels specified for a pulse arterial tonometry (PAT)-based home sleep apnea testing (HSAT) device with the neurological signals. Somfit sleep staging deep learning algorithm is based on convolutional neural network architecture.

Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications.

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving.

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual's cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers.

Objective: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers.

Approach: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20).

Variations in the accuracy of the ECG based detection of obstructive sleep apnoea (OSA) for different numbers of ECG leads and categories of OSA events.

This study investigates the finding that there is a more pronounced change to ECG physiological predictors during apnoea events compared to hypopnoea events and therefore accurate detection of hypopnoea events is likely to be more challenging than detection of apnoea events. The relevant statistical analysis was conducted by generating logistic regression models from the two data sets: the first one containing only the apnoea events and controls and the second data set containing only the hypopnoea events and controls. The discriminating ability of the model from the apnoea data set (AUC = 0.

Power analysis of gamma frequencies (30 - 47Hz), adjusting for muscle activity (80 - 97Hz), in anesthesia: a comparison between young adults, middle-aged and the elderly.

This study looks at the role of EEG gamma activity, and the influence of facial EMG (80-97 Hz), in predicting consciousness during anesthesia. It also studies the association between the conventional depth of anesthesia index, BIS (Aspect Medical Systems), and EEG gamma and EMG activity. Data has been collected from 21 adult patients and grouped into young adults (18 - 39 yrs, n=3), middle-aged (40 - 64 yrs, n=10) and the elderly (65+ yrs, n=8).