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Rationale, aims and objectives: Single‐group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to ‘interrupt’ the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. Method: In this paper, we introduce a machine‐learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter‐initiated Proposition 99 to reduce smoking rates. Results: The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single‐group ITSA design. Conclusions: Given the widespread use of ITSA for evaluating observational data and the increasing use of machine‐learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single‐group ITSA design. [ABSTRACT FROM AUTHOR]

Sandwave migration rate is of critical importance for the hydrodynamic research as well as the coastal engineering application. To acquire this information, the correlation matching algorithm is applied in this paper. By searching for similar seafloor areas between multi-temporal digital terrain models, the migration rate can be estimated. To account for the impact of the morphological distortion as well as outliers in the survey data, the correlation matching is adjusted based on the robust estimation theory. By executing the correlation matching based on the least median square criterion, a robust initial estimate is obtained. Afterwards, the potential outliers are detected based on the initial matching result, and the corresponding weights are adjusted to zero. Finally, correlation matching is executed with the updated weights. With the above procedure, the impact of outliers can be accounted for. The developed method is applied on the bathymetry time-series data collected in Monterey Canyon, California. Comparison shows that the presented algorithm significantly improves the credibility of the estimation. The generated migration vector field not only reveals the main tendency of the migration rate in the region, but also highlights the unique local migration patterns, which is helpful for understanding the mechanism of the sandwave migration. [ABSTRACT FROM AUTHOR]

Embedding of diagnostic and health care information requires secure encryption and watermarking. This research paper presents a comprehensive study for the behavior of some well established watermarking algorithms in frequency domain for the preservation of stroke-based diagnostic parameters. Two different sets of watermarking algorithms namely: two correlation-based (binary logo hiding) and two singular value decomposition (SVD)-based (gray logo hiding) watermarking algorithms are used for embedding ownership logo. The diagnostic parameters in atherosclerotic plaque ultrasound video are namely: (a) bulb identification and recognition which consists of identifying the bulb edge points in far and near carotid walls; (b) carotid bulb diameter; and (c) carotid lumen thickness all along the carotid artery. The tested data set consists of carotid atherosclerotic movies taken under IRB protocol from University of Indiana Hospital, USA-AtheroPoint™ (Roseville, CA, USA) joint pilot study. ROC (receiver operating characteristic) analysis was performed on the bulb detection process that showed an accuracy and sensitivity of 100 % each, respectively. The diagnostic preservation (DP) for SVD-based approach was above 99 % with PSNR (Peak signal-to-noise ratio) above 41, ensuring the retention of diagnostic parameter devalorization as an effect of watermarking. Thus, the fully automated proposed system proved to be an efficient method for watermarking the atherosclerotic ultrasound video for stroke application. [ABSTRACT FROM AUTHOR]

Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application. [ABSTRACT FROM AUTHOR]

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome. [ABSTRACT FROM AUTHOR]