In the context of railway safety, it is crucial to know the positions of all trains moving along the infrastructure. In this contribution, we present an algorithm that extracts the positions of ...moving trains for a given point in time from Distributed Acoustic Sensing (DAS) signals. These signals are obtained by injecting light pulses into an optical fiber close to the railway tracks and measuring the Rayleigh backscatter. We show that the vibrations of moving objects can be identified and tracked in real-time yielding train positions every second. To speed up the algorithm, we describe how the calculations can partly be based on graphical processing units. The tracking quality is assessed by counting the inaccurate and lost train tracks for two different types of cable installations.
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 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 τ = 5 and τ = 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.
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.
Background:
Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, ...in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child’s own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis.
Method:
Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (
https://clinicaltrials.gov/ct2/show/NCT03528096
). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children.
Result:
Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen’s kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization.
Conclusion:
3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.
The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a ‘restless’ child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) ...during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Journal of Psychiatric Research). To develop the PG-PL, seven research assistants annotated three original Fidgety Philip cartoons. Their annotations were analyzed with descriptive statistics. To review the PG-PL's performance, the same seven research assistants annotated 12 snapshots with free hand annotations, followed by using the PG-PL, each time in randomized sequence and on two separate occasions. After achieving satisfactory inter-observer agreements, the PG-PL annotation software was used for reviewing videos where the same seven research assistants annotated 12 one-minute long video clips. The video clip annotations were finally used to develop a machine learning algorithm for automated movement detection (Journal of Psychiatric Research). These data together demonstrate the value of the PG-PL for manually annotating human movement patterns. Researchers are able to reuse the data and the first version of the machine learning algorithm to further develop and refine the algorithm for differentiating movement patterns.
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.
Abstract
Background
Sleep-related rhythmic movement disorder is characterized by repetitive gross-motor movements at sleep onset or during sleep, which result in clinical consequences such as impact ...on daytime functioning and injury. No well-established therapies exist today. Substituting the patient’s movements with external sensory stimulation may offer a treatment modality. The aim of the current study was to test the feasibility and acceptability of vestibular stimulation using a rocking bed (Somnomat) in children with rhythmic movement disorder and to assess children’s movement preference.
Methods
Children with rhythmic movement disorder (
n
= 6, Age: 5–14 years) were studied over three nights in a sleep laboratory: adaptation night (normal bed) and randomised-order baseline (Somnomat) and intervention nights (Somnomat). Child’s preferred movement direction (head-to-toe or side-to-side) and frequency (between 0.25 and 2 Hz), determined during an afternoon protocol, were applied using the Somnomat for 1 h after lights out, and in response to subsequent episodes of rhythmic movement during intervention nights. Comfort assessed using a questionnaire, and objective sleep parameters assessed using videosomnography, were compared.
Results
The participants’ sometimes violent rhythmic movements did not disturb device performance. All children rated intervention nights equally or more comfortable than baseline nights. Self-reported sleep quality, as well as the number and duration of movement episodes did not significantly differ between baseline and intervention nights.
Conclusions
Providing rocking movements using the Somnomat is both technically feasible and acceptable to the target population. The therapeutic value of this novel stimulus substitution for rhythmic movement disorder should now be evaluated in a larger sample over a longer period in the home setting.
Trial registration
The trial was retrospectively registered at clinicaltrials.gov (
NCT03528096
) on May 17th 2018.
3D detection of the central sleep apnoea syndrome Garn, Heinrich; Kohn, Bernhard; Wiesmeyr, Christoph ...
Current directions in biomedical engineering,
09/2017, Letnik:
3, Številka:
2
Journal Article
Recenzirano
Odprti dostop
In polysomnography, an oronasal thermal airflow sensor and respiratory inductance plethysmography (RIP) belts at thorax and abdomen are used to detect central sleep apnoea. These sensors are ...uncomfortable to wear, can disturb the patient’s sleep, and data quality can be significantly di-minished if a sensor slips off the patient. Contactless meas-urements would be a desirable alternative. We utilized a 3D time-of-flight sensor to monitor respiratory-related chest movements to decipher epochs of normal breathing and ap-noea in ten adult patients with a total of 467 apnoea events. Time-synchronized comparisons of 3D measurements of chest movements due to respiration to polysomnography signals from rip belts and nasal airflow proved that the 3D sensor provided largely equivalent results. This new tech-nique could support the diagnosis of central sleep apnoea and Cheyne-Stokes respiration.
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