"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.

Quantifying time-varying coordination of multimodal speech signals using correlation map analysis.

This paper demonstrates an algorithm for computing the instantaneous correlation coefficient between two signals. The algorithm is the computational engine for analyzing the time-varying coordination between signals, which is called correlation map analysis (CMA). Correlation is computed around any pair of points in the two input signals. Thus, coordination can be assessed across a continuous range of temporal offsets and be detected even when changing over time due to temporal fluctuations. The correlation algorithm has two major features: (i) it is structurally similar to a tunable filter, requiring only one parameter to set its cutoff frequency (and sensitivity), (ii) it can be applied either uni-directionally (computing correlation based only on previous samples) or bi-directionally (computing correlation based on both previous and future samples). Computing instantaneous correlation for a range of time offsets between two signals produces a 2D correlation map, in which correlation is characterized as a function of time and temporal offset. Graphic visualization of the correlation map provides rapid assessment of how correspondence patterns progress through time. The utility of the algorithm and of CMA are exemplified using the spatial and temporal coordination of various audible and visible components associated with linguistic performance.