Robust and sensitive video motion detection for sleep analysis

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 (MCC) improved with 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 (