Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

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.

Is Fidgety Philip's ground truth also ours? The creation and application of a machine learning algorithm.

BACKGROUND: Behavioral observations support clinical in-depth phenotyping but phenotyping and pattern recognition are affected by training background. As Attention Deficit Hyperactivity Disorder, Restless Legs syndrome/Willis Ekbom disease and medication induced activation syndromes (including increased irritability and/or akathisia), present with hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors), we first developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting. METHODOLOGY & RESULTS: The PG-PL was applied for annotating 12 1-min sitting-videos (inter-observer agreements >85%->97%) and these manual annotations were used as a ground truth to develop an automated algorithm using OpenPose, which locates skeletal landmarks in 2D video. We evaluated the algorithm's performance against the ground truth by computing the area under the receiver operator curve (>0.79 for the legs, arms, and feet, but 0.65 for the head). While our pixel displacement algorithm performed well for the legs, arms, and feet, it predicted head motion less well, indicating the need for further investigations. CONCLUSION: This first automated analysis algorithm allows to start the discussion about distinct phenotypical characteristics of H-behaviors during structured behavioral observations and may support differential diagnostic considerations via in-depth phenotyping of sitting behaviors and, in consequence, of better treatment concepts.

3D detection of periodic limb movements in sleep.

The standard polysomnographic method for detecting periodic limb movements in sleep (PLMS) includes measuring the electromyography (EMG) signals from electrodes at the left and right tibialis anterior muscles. This procedure has disadvantages as the cabling affects the patients quality of sleep and the electrodes tend to come off during the night, deteriorating data quality. We used contactless monitoring of body movements by a 3D time-of-flight camera mounted above the bed. Changes in the 3D silhouette indicate motion. Contactless detection of PLMS has several substantial advantages over the EMG and provides more complete and more specific diagnostic data: (1) Motor events caused by other leg muscles than tibialis anterior muscles are fully captured by the 3D method, but missed by EMG. (2) 3D does not react to tonic muscle contractions, where such contractions cause strong deflections in EMG which are annotated as limb movements by most PSG apparatus. Another aspect turned out to be of high practical relevance: Deflections in EMG traces are frequently caused by poor electrode contacts, potentially causing false movement annotations. This can lead to substantial overestimation of the automatically computed PLM index. Contactless sensing completely avoids such problems.

"Diagnosis by behavioral observation" home-videosomnography - a rigorous ethnographic approach to sleep of children with neurodevelopmental conditions.

INTRODUCTION: Advanced video technology is available for sleep-laboratories. However, low-cost equipment for screening in the home setting has not been identified and tested, nor has a methodology for analysis of video recordings been suggested. METHODS: We investigated different combinations of hardware/software for home-videosomnography (HVS) and established a process for qualitative and quantitative analysis of HVS-recordings. A case vignette (HVS analysis for a 5.5-year-old girl with major insomnia and several co-morbidities) demonstrates how methodological considerations were addressed and how HVS added value to clinical assessment. RESULTS: We suggest an "ideal set of hardware/software" that is reliable, affordable (∼$500) and portable (=2.8 kg) to conduct non-invasive HVS, which allows time-lapse analyses. The equipment consists of a net-book, a camera with infrared optics, and a video capture device. (1) We present an HVS-analysis protocol consisting of three steps of analysis at varying replay speeds: (a) basic overview and classification at 16× normal speed; (b) second viewing and detailed descriptions at 4-8× normal speed, and (c) viewing, listening, and in-depth descriptions at real-time speed. (2) We also present a custom software program that facilitates video analysis and note-taking (Annotator(©)), and Optical Flow software that automatically quantifies movement for internal quality control of the HVS-recording. The case vignette demonstrates how the HVS-recordings revealed the dimension of insomnia caused by restless legs syndrome, and illustrated the cascade of symptoms, challenging behaviors, and resulting medications. CONCLUSION: The strategy of using HVS, although requiring validation and reliability testing, opens the floor for a new "observational sleep medicine," which has been useful in describing discomfort-related behavioral movement patterns in patients with communication difficulties presenting with challenging/disruptive sleep/wake behaviors.

Computer-assisted sleep classification according to the standard of the American Academy of Sleep Medicine: validation study of the AASM version of the Somnolyzer 24 × 7.

BACKGROUND: In 2007, the AASM Manual for the Scoring of Sleep and Associated Events was published by the American Academy of Sleep Medicine (AASM). Concerning the visual classification of sleep stages, these new rules are intended to replace the rules by Rechtschaffen and Kales (R&K). METHODS: We adapted the automatic R&K sleep scoring system Somnolyzer 24 × 7 to comply with the AASM rules and subsequently performed a validation study based on 72 polysomnographies from the Siesta database (56 healthy subjects, 16 patients, 38 females, 34 males, aged 21-86 years). Scorings according to the AASM rules were performed manually by experienced sleep scorers and semi-automatically by the AASM version of the Somnolyzer. Manual scorings and Somnolyzer reviews were performed independently by at least 2 out of 8 experts from 4 sleep centers. RESULTS: In the quality control process, sleep experts corrected 4.8 and 3.7% of the automatically assigned epochs, resulting in a reliability between 2 Somnolyzer-assisted scorings of 99% (Cohen's kappa: 0.99). In contrast, the reliability between the 2 manual scorings was 82% (kappa: 0.76). The agreement between the 2 Somnolyzer-assisted and the 2 visual scorings was between 81% (kappa: 0.75) and 82% (kappa: 0.76). CONCLUSION: The AASM version of the Somnolyzer revealed an agreement between semi-automated and human expert scoring comparable to that published for the R&K version with a validity comparable to that of human experts, but with a reliability close to 1, thereby reducing interrater variability as well as scoring time to a minimum.

Cyclic alternating pattern and sleep quality in healthy subjects--is there a first-night effect on different approaches of sleep quality?

OBJECTIVE: Cyclic alternating pattern (CAP) is defined as periodic EEG activity during NREM sleep that reflects unstable sleep and represents a marker of instability of the sleep process. The aim of the present investigation was to analyze sleep quality of 28 healthy subjects (mean age 53.3+/-21.3 years) over two consecutive nights and determine potential differences between them ("first-night effect"). METHODS: Evaluations comprised objective and subjective sleep variables as well as macrostructural and microstructural variables of sleep. RESULTS: Macrostructural analysis showed significant differences between the first and the second sleep laboratory night in REM latency (122.39+/-60.46 min vs. 95.43+/-36.60 min; T=3.431; p=0.002) and the amount of sleep stage 1 (42.60+/-21.80 min vs. 39.70+/-18.95 min; T=2.223; p=0.035). Microstructural analysis revealed a significant decrease in the CAP rate (1st night: 33.29%; 2nd night: 26.34%; T=3.288; p=0.003) and in the amount of subtype A2 (74.79+/-43.47 vs. 58.50+/-23.22; T=2.185; p=0.038). Subjective variables also demonstrated a significant increase of drive (T=2.564; p=0.016). CONCLUSION: Healthy subjects show hardly any macrostructural differences between the first and the second night in the sleep laboratory. On the microstructural level differences in CAP variables were found. SIGNIFICANCE: Microstructural analysis can be seen as a further approach to the classification of sleep and CAP turned out to be sensitive to environmental influences on sleep.

Sleep classification according to AASM and Rechtschaffen & Kales: effects on sleep scoring parameters.

STUDY OBJECTIVE: To investigate differences between visual sleep scoring according to the classification developed by Rechtschaffen and Kales (R&K, 1968) and scoring based on the new guidelines of the American Academy of Sleep Medicine (AASM, 2007). DESIGN: All-night polysomnographic recordings were scored visually according to the R&K and AASM rules by experienced sleep scorers. Descriptive data analysis was used to compare the resulting sleep parameters. PARTICIPANTS: Healthy subjects and patients (38 females and 34 males) aged between 21 and 86 years. INTERVENTIONS: N/A. MEASUREMENT AND RESULTS: While sleep latency and REM latency, total sleep time, and sleep efficiency were not affected by the classification standard, the time (in minutes and in percent of total sleep time) spent in sleep stage 1 (S1/N1), stage 2 (S2/N2) and slow wave sleep (S3+S4/N3) differed significantly between the R&K and the AASM classification. While light and deep sleep increased (S1 vs. N1 [+10.6 min, (+2.8%)]: P<0.01; S3+S4 vs. N3 [+9.1 min (+2.4%)]: P<0.01), stage 2 sleep decreased significantly according to AASM rules (S2 vs. N2 [-20.5 min, (-4.9%)]: P<0.01). Moreover, wake after sleep onset was significantly prolonged by approximately 4 minutes (P<0.01) according to the AASM standard. Interestingly, the effects on stage REM were age-dependent (intercept at 20 years: -7.5 min; slope: 1.6 min for 10-year age increase). No effects of sex and diagnosis were observed. CONCLUSION: The study shows significant and age-dependent differences between sleep parameters derived from conventional visual sleep scorings on the basis of R&K rules and those based on the new AASM rules. Thus, new normative data have to be established for the AASM standard.

Interindividual sleep spindle differences and their relation to learning-related enhancements.

We reported earlier that overnight change in explicit memory is positively related to the change in sleep spindle activity (between a control and a learning night). However, it remained unclear whether this effect was restricted to good memory performers and whether a general association of sleep spindles and a "sleep-related learning trait" may not account for this effect. Here we now present a secondary and more detailed analysis of our randomized multicenter study. Subjects were studied over a 4-week study period (including actigraphy and daily sleep diaries), including three overnight stays in the sleep laboratory. In the course of the study, subjects completed test-batteries of memory (Wechsler-Memory-Scale-revised; WMS) and other cognitive abilities (Raven's Advanced-Progressive-Matrices; APM) and were asked to study 160 word pairs in the evening before being tested by cued-recall. Afterwards, subjects went to bed in the laboratory with full polysomnographic montages. Additionally, subjects participated on another occasion in a non-learning control (perceptual priming) task that was counterbalanced either before or after the learning condition. Slow as well as fast spindle activities were analyzed at frontopolar and central topographies. Although it was found that spindle activity is generally (in learning as well as control nights) elevated in highly gifted subjects, spindle analyses revealed that spindle increase (control to learning night) is specifically related to explicit memory improvement overnight, independent of individual learning traits. Together these findings suggest that the spindle increase after learning is related to elaborate encoding before sleep, whereas an individual's general learning ability is well reflected in interindividual (and trait-like) differences of absolute sleep spindle activity.