IEEE J Biomed Health Inform - Robust and sensitive video motion detection for sleep analysis.

Tópicos

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Resumo

In this paper, we propose a camera-based system combining video motion detection, motion estimation, and texture analysis with machine learning for sleep analysis. The system is robust to time-varying illumination conditions while using standard camera and infrared illumination hardware. We tested the system for periodic limb movement (PLM) detection during sleep, using EMG signals as a reference. We evaluated the motion detection performance both per frame and with respect to movement event classification relevant for PLM detection. The Matthews correlation coefficient improved by a factor of 2, compared to a state-of-the-art motion detection method, while sensitivity and specificity increased with 45% and 15%, respectively. Movement event classification improved by a factor of 6 and 3 in constant and highly varying lighting conditions, respectively. On 11 PLM patient test sequences, the proposed system achieved a 100% accurate PLM index (PLMI) score with a slight temporal misalignment of the starting time (<1 s) regarding one movement. We conclude that camera-based PLM detection during sleep is feasible and can give an indication of the PLMI score.

Resumo Limpo

paper propos camerabas system combin video motion detect motion estim textur analysi machin learn sleep analysi system robust timevari illumin condit use standard camera infrar illumin hardwar test system period limb movement plm detect sleep use emg signal refer evalu motion detect perform per frame respect movement event classif relev plm detect matthew correl coeffici improv factor compar stateoftheart motion detect method sensit specif increas respect movement event classif improv factor constant high vari light condit respect plm patient test sequenc propos system achiev accur plm index plmi score slight tempor misalign start time s regard one movement conclud camerabas plm detect sleep feasibl can give indic plmi score

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