Background: Functional (un-)coupling (task-related change of functional connectivity) between different sites of the brain is a mechanism of general importance for cognitive processes. In Alzheimer's ...disease (AD), prior research identified diminished cortical connectivity as a hallmark of the disease. However, little is known about the relation between the amount of functional (un-)coupling and cognitive performance and decline in AD. Method: Cognitive performance (based on CERAD-Plus scores) and electroencephalogram (EEG)-based functional (un-)coupling measures (connectivity changes from rest to a Face-Name-Encoding task) were assessed in 135 AD patients (age: M = 73.8 years; SD = 9.0). Of these, 68 patients (M = 73.9 years; SD = 8.9) participated in a follow-up assessment of their cognitive performance 1.5 years later. Results: The amounts of functional (un-)coupling in left anterior-posterior and homotopic interhemispheric connections in beta1-band were related to cognitive performance at baseline (β = .340; p < .001; β = .274; P = .001, respectively). For both markers, a higher amount of functional coupling was associated with better cognitive performance. Both markers also were significant predictors for cognitive decline. However, while patients with greater functional coupling in left anterior-posterior connections declined less in cognitive performance (β = .329; P = .035) those with greater functional coupling in interhemispheric connections declined more (β = −.402; P = .010). Conclusion: These findings suggest an important role of functional coupling mechanisms in left anterior–posterior and interhemispheric connections in AD. Especially the complex relationship with cognitive decline in AD patients might be an interesting aspect for future studies.
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 R2 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 R2>0.40 at scales τ=5 and τ=6 . ShE and TsE also have significant models at T7 and F7 with R2>0.30 . Reductions in complexity, calculated by AMI, SpE, and MsE, were observed as the MMSE score decreased.
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 SpO
2
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 (
≥
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 SpO
2
, our algorithm was able to detect respiratory events with high correlation and agreement compared to full PSG-based detection.
The objective of this work was to develop and evaluate a classifier for differentiating probable Alzheimer’s disease (AD) from Parkinson’s disease dementia (PDD) or dementia with Lewy bodies (DLB) ...and from frontotemporal dementia, behavioral variant (bvFTD) based on quantitative electroencephalography (QEEG). We compared 25 QEEG features in 61 dementia patients (20 patients with probable AD, 20 patients with PDD or probable DLB (DLBPD), and 21 patients with bvFTD). Support vector machine classifiers were trained to distinguish among the three groups. Out of the 25 features, 23 turned out to be significantly different between AD and DLBPD, 17 for AD versus bvFTD, and 12 for bvFTD versus DLBPD. Using leave-one-out cross validation, the classification achieved an accuracy, sensitivity, and specificity of 100% using only the QEEG features Granger causality and the ratio of theta and beta1 band powers. These results indicate that classifiers trained with selected QEEG features can provide a valuable input in distinguishing among AD, DLB or PDD, and bvFTD patients. In this study with 61 patients, no misclassifications occurred. Therefore, further studies should investigate the potential of this method to be applied not only on group level but also in diagnostic support for individual subjects.
Fibre Optic Acoustic Sensing technology has a number of rail applications, however to date the promising algorithms developed, including train tracking, and trespass detection, have not been adopted ...and little ground truth data exists to validate their effectiveness. Thales have developed a trial with consortium of partners to use FOAS, Internet of Things Sensors & Smart CCTV technologies to collect ground truth data alongside FOAS data, and undertake the development of analysis and data fusion algorithms to accelerate the development of a combined solution. The work with Network Rail will also determine the best combination of sensors to improve rail operations, and will explore the next steps to implement the technology in industry. This paper summarises the trial by Thales and its partners, under the FOAS, IOT & Smart CCTV project, and highlights the findings to date and future work.
3D Camera and Pulse Oximeter for Respiratory Events Detection Coronel, Carmina; Wiesmeyr, Christoph; Garn, Heinrich ...
IEEE journal of biomedical and health informatics,
2021-Jan., 2021-Jan, 2021-1-00, 20210101, Letnik:
25, Številka:
1
Journal Article
Recenzirano
Odprti dostop
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.
Introduction To calculate the Apnea-Hypopnea Index (AHI), the detection of apneas and hypopneas is performed using multiple sensors of a polysomnography (PSG) such as nasal airflow and respiratory ...inductance plethysmography (RIP). This setup is uncomfortable and labor intensive. Therefore, we used a contactless 3D time-of-flight (TOF) camera to monitor the respiratory effort. Using this respiratory effort and SpO2, we developed an algorithm to detect apnea and hypopnea events without the need of airflow sensors and RIP belts. Methods 3D Video-PSG was performed for 53 patients with suspected sleep apnea syndrome at Advanced Sleep Research GmbH, Berlin and Johannes Kepler University Clinic, Linz, as approved by the relevant ethical committees. The respiratory effort signal (Effort3D) was derived from the camera’s depth information over the upper body region. An Effort3D-SpO2 algorithm for detecting apnea and hypopnea events was developed wherein an event is a segment of at least 10 seconds with a substantial decrease in Effort3D and an associated 4% desaturation in SpO2. The 3DSpO2-AHI was calculated from the number of detected events and the total sleep time (TST), obtained from the PSG’s hypnogram. The intraclass correlation coefficient (ICC) with its 95% confidence interval (CI) was used to measure reliability with manually scored m-AHI according to the American Academy of Sleep Medicine (AASM) scoring manual and automatically calculated PSG-AHI from the PSG. Results The ICC for 3DSpO2-AHI versus m-AHI is 0.97 (CI: 0.95-0.98) and the median absolute difference is 4. On the other hand, the ICC for 3DSpO2-AHI and PSG-AHI is 0.91 (0.85-0.95) and the median absolute difference is 4. Between m-AHI and PSG-AHI, the ICC is 0.93 (0.88 - 0.96), and the median absolute difference is 4. Conclusion The respiratory effort derived from 3D TOF camera together with SpO2 is a promising option in detecting respiratory events as it has shown excellent reliability scores, comparable to automated-PSG and manual scoring. The contactless 3D TOF camera and SpO2 is a more comfortable alternative for overnight recordings. Support (If Any) Austrian Research Promotion Agency (FFG), project ID 859622.
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.
Background
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.
Objective
We aimed to show that respiratory effort monitoring can be achieved by 3D TOF camera recording instead of RIP belts.
Materials and methods
The use of RIP belt signals is twofold. Firstly, the signals are used to classify the apnea events into obstructive, central, and mixed. Additionally, the American Academy of Sleep Medicine (AASM) Scoring Manual recommends the scoring of apneas and hypopneas using RIPSum (the sum of the abdomen and thorax RIP signals) when the airflow sensors are unavailable. We therefore used the 3D effort signal to classify apneas and compared it to the RIP signal classification. Reduced effort events from RIP and 3D effort signals were compared to the apnea and hypopnea events. Furthermore, the changes in effort during the events were compared between the two effort signals.
Results
Classification by 3D effort signal performed well, with 80% accuracy. It worked best for central apneas, with an accuracy of 99%. There was a high correlation of
r
= 0.88 (r ≠ 0,
p
= 0.0001) between the 3D effort signal events and RIPSum events. There was also a significant correlation of 0.62 (r ≠ 0,
p
= 0.0001) between 3D effort signal and RIPSum in the decrease of effort during apnea and hypopnea events.
Conclusion
We conclude that respiratory effort derived from a 3D TOF camera can be used as an alternative to RIP belts for scoring of apneas and hypopneas and classification of apneas.
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