3D Camera and Pulse Oximeter for Respiratory Events Detection.

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 SpO2 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 SpO2 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 SpO2 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.

Contactless detection of periodic leg movements during sleep: A 3D video pilot study.

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.

A Novel Approach to Assess Sleep-Related Rhythmic Movement Disorder in Children Using Automatic 3D Analysis.

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.

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.

Optimal Window and Lattice in Gabor Transform. Application to Audio Analysis.

This article deals with the use of optimal lattice and optimal window in Discrete Gabor Transform computation. In the case of a generalized Gaussian window, extending earlier contributions, we introduce an additional local window adaptation technique for non-stationary signals. We illustrate our approach and the earlier one by addressing three time-frequency analysis problems to show the improvements achieved by the use of optimal lattice and window: close frequencies distinction, frequency estimation and SNR estimation. The results are presented, when possible, with real world audio signals.