Analysis of nonlinear quantitative EEG (qEEG) markers describing complexity of signal in relation to severity of Alzheimer’s disease (AD) was the focal point of this study. In this study, 79 patients diagnosed with probable AD were recruited from the multi-centric Prospective Dementia Database Austria (PRODEM). EEG recordings were done with the subjects seated in an upright position in a resting state with their eyes closed. Models of linear regressions explaining disease severity, expressed in Mini Mental State Examination (MMSE) scores, were analyzed by the nonlinear qEEG markers of auto mutual information (AMI), Shannon entropy (ShE), Tsallis entropy (TsE), multiscale entropy (MsE), or spectral entropy (SpE), with age, duration of illness, and years of education as co-predictors. Linear regression models with AMI were significant for all electrode sites and clusters, where $R^2$ is 0.46 at the electrode site C3, 0.43 at Cz, F3, and central region, and 0.42 at the left region. MsE also had significant models at C3 with $R^2>0.40$ at scales $\tau =5$ and $\tau =6$ . ShE and TsE also have significant models at T7 and F7 with $R^2>0.30$ . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.

Objective: The purpose of this study was to derive a respiratory movement signal from a 3D time-of-flight camera and to investigate if it can be used in combination with SpO $_2$ to detect respiratory events comparable to polysomnography (PSG) based detection. Methods: We derived a respiratory signal from a 3D camera and developed a new algorithm that detects reduced respiratory movement and SpO $_2$ desaturation to score respiratory events. The method was tested on 61 patients' synchronized 3D video and PSG recordings. The predicted apnea-hypopnea index (AHI), calculated based on total sleep time, and predicted severity were compared to manual PSG annotations (manualPSG). Predicted AHI evaluation, measured by intraclass correlation (ICC), and severity classification were performed. Furthermore, the results were evaluated by 30-second epoch analysis, labelled either as respiratory event or normal breathing, wherein the accuracy, sensitivity, specificity and Cohen's kappa were calculated. Results: The predicted AHI scored an ICC $r$ = 0.94 (0.90 — 0.96 at 95% confidence interval, $p$ < 0.001) compared to manualPSG. Severity classification scored 80% accuracy, with no misclassification by more than one severity level. Based on 30-second epoch analysis, the method scored a Cohen's kappa= 0.72, accuracy = 0.88, sensitivity = 0.80, and specificity = 0.91. Conclusion: Our detection method using SpO $_2$ and 3D camera had excellent reliability and substantial agreement with PSG-based scoring. Significance: This method showed the potential to reliably detect respiratory events without airflow and respiratory belt sensors, sensors that can be uncomfortable to patients and susceptible to movement artefacts.

Polysomnography systems used for the diagnosis of sleep respiratory disorders are comprised of multiple sensors, including abdomen and thorax respiratory inductance plethysmography (RIP) belts to record respiratory effort. However, RIP belts are known to be susceptible to signal loss. To resolve this, we utilized a contactless three-dimensional (3D) time-of-flight (TOF) camera to monitor respiratory effort.

Analyses of sleep-related movement disorders have gained importance due to an increase in life expectancy. The present approaches for measuring movements are based on electromyography or accelerometry and provide only local or specific results from muscles/limbs to which sensors have been attached. The motivation of this work was to investigate the detection of a more complete spectrum of sleep-related movements using a three-dimensional (3D) camera instead of the current conventional methods. In contrast to most of the previously published literature, this method allows for the detection of movements even when patients are covered with a blanket. This is the first work to evaluate movement detection with a clinical dataset and replicate the clinical environment in a laboratory setup. The laboratory setup allowed for the characterization of detectable movements through the determination of speed and amplitude limits. We used the Kinect One time-of-flight sensor to record 3D videos. Movements were quantified based on the temporal depth change in these 3D videos. A computer-controlled lifting table allowed for the controlled simulation of movements. Our algorithm detected movements with amplitude values >3.0 mm and velocity values >3.5 mm/s with an F1 score ≥95%. The shortest reliably detected duration of movement was 350 ms. In an ethically approved clinical study including 44 patients, 93.1% of electromyography-detected leg movements were also found in 3D. A significant correlation ($\rho = 0.86$ ) was found between movements detected by the 3D system and polysomnography. The 3D system detected 31.2% more movements than electromyography. In addition to obtaining a broader spectrum of movements not limited to local and muscle/limb-specific movements, the usage of a contactless 3D camera simplifies the recording setup and preserves natural sleeping behavior. The presented 3D system may become useful for diagnostic purposes during sleep studies.

Purpose Sleep respiratory events are scored based on the reduction of airflow measured by a thermistor or nasal pressure cannula, together with oxygen desaturation and arousal criteria for hypopneas. We investigated whether automatic scoring can be performed without using the uncomfortable oronasal sensors and developed an automatic scoring system that is compatible with level III home sleep apnea testing devices. Methods We developed a respiratory event detection algorithm, based on SpO₂ and respiratory effort signal measured from respiratory inductance plethysmograph (uncalibrated RIPsum), that outputs the time and duration of detected events and calculates an apnea–hypopnea-index (AHI) based on total recording time. The algorithm was tested on 98 polysomnography (PSG) recordings of patients, 77 with suspected sleep apnea and 21 without. The results were compared to annotations provided by the PSG systems where PSG AHI was computed using the total sleep time. The predicted AHI was evaluated for correlation and agreement with the PSG AHI using the intra-class correlation coefficient (ICC). Severity classification was performed and evaluated using the following categories: normal (< 5), mild (5–15), moderate (15–30), and severe (\(\ge 30\)). Results The ICC between predicted AHI and PSG AHI scored r = 0.96 (0.95–0.97, p < 0.001). The algorithm correctly predicted the severity for 74 recordings, overestimated 16, and underestimated 8. There was no misclassification by more than one severity level. Conclusion Using respiratory effort and SpO₂, our algorithm was able to detect respiratory events with high correlation and agreement compared to full PSG-based detection.

In clinical practice, the quality of polysomnographic recordings in children and patients with neurodegenerative diseases may be affected by sensor displacement and diminished total sleep time due to stress during the recording. In the present study, we investigated if contactless three‐dimensional (3D) detection of periodic leg movements during sleep was comparable to polysomnography. We prospectively studied a sleep laboratory cohort from two Austrian sleep laboratories. Periodic leg movements during sleep were classified according to the standards of the World Association of Sleep Medicine and served as ground truth. Leg movements including respiratory‐related events (A1) and excluding respiratory‐related events (A2 and A3) were presented as A1, A2 and A3. Three‐dimensional movement analysis was carried out using an algorithm developed by the Austrian Institute of Technology. Fifty‐two patients (22 female, mean age 52.2 ± 15.1 years) were included. Periodic leg movement during sleep indexes were significantly higher with 3D detection compared to polysomnography (33.3 [8.1–97.2] vs. 30.7 [2.9–91.9]: +9.1%, p = .0055/27.8 [4.5–86.2] vs. 24.2 [0.00–88.7]: +8.2%, p = .0154/31.8 [8.1–89.5] vs. 29.6 [2.4–91.1]: +8.9%, p = .0129). Contactless automatic 3D analysis has the potential to detect restlessness mirrored by periodic leg movements during sleep reliably and may especially be suited for children and the elderly.