We addressed two interesting video-based health measurements. First is video-based Heart Rate (HR) estimation, known as video-based Photoplethysmography (PPG) or videoplethysmography (VHR). We adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generated more accurate HR estimates and allowed other applied filters to work more effectively. Measuring HR at the presence of motions is one of the most challenging problems in recent VHR studies. We investigated and described the motion effects in VHR in terms of the angle change of the subject’s skin surface in relation to the light source. Based on this understanding, we discussed the future work on how we can compensate for the motion artifacts. Another important health information addressed in this thesis is Videosomnography (VSG), a range of video-based methods used to record and assess sleep vs. wake states in humans. Traditional behavioral-VSG (B-VSG) labeling requires visual inspection of the video by a trained technician to determine whether a subject is asleep or awake. We proposed an automated VSG sleep detection system (auto-VSG) which employs motion analysis to determine sleep vs. wake states in young children. The analyses revealed that estimates generated from the proposed Long Short-term Memory (LSTM)-based method with long-term temporal dependency are suitable for automated sleep or awake labeling.

The main objective of this work was the development of a computer-based Expert Sleep Analysis Methodology (ESAM) to aid sleep care physicians in the diagnosis of pre-Parkinson's disease symptoms using polysomnogram data. ESAM is significant because it streamlines the analysis of the human sleep cycles and aids the physician in the identification, treatment, and prediction of sleep disorders. In this work four aspects of computer-based human sleep analysis were investigated: polysomnogram interpretation, pre-processing, sleep event classification, and abnormal sleep detection. A review of previous developments in these four areas is provided along with their relationship to the establishment of ESAM. Polysomnogram interpretation focuses on the ambiguities found in human polysomnogram analysis when using the rule based 1968 sleep staging manual edited by Rechtschaffen and Kales (R&K). ESAM is presented as an alternative to the R&K approach in human polysomnogram interpretation. The second area, pre-processing, addresses artifact processing techniques for human polysomnograms. Sleep event classification, the third area, discusses feature selection, classification, and human sleep modeling approaches. Lastly, abnormal sleep detection focuses on polysomnogram characteristics common to patients suffering from Parkinson's disease. The technical approach in this work utilized polysomnograms of control subjects and pre-Parkinsonian disease patients obtained from the Emory Clinic Sleep Disorders Center (ECSDC) as inputs into ESAM. The engineering tools employed during the development of ESAM included the Generalized Singular Value Decomposition (GSVD) algorithm, sequential forward and backward feature selection algorithms, Particle Swarm Optimization algorithm, k-Nearest Neighbor classification, and Gaussian Observation Hidden Markov Modeling (GOHMM). In this study polysomnogram data was preprocessed for artifact removal and compensation using band-pass filtering and the GSVD algorithm. Optimal features for characterization of polysomnogram data of control subjects and pre-Parkinsonian disease patients were obtained using the sequential forward and backward feature selection algorithms, Particle Swarm Optimization, and k-Nearest Neighbor classification. ESAM output included GOHMMs constructed for both control subjects and pre-Parkinsonian disease patients. Furthermore, performance evaluation techniques were implemented to make conclusions regarding the constructed GOHMM's reflection of the underlying nature of the human sleep cycle.