Abstract: System for individualized multiple-disease quantitative risk assessment, comprising: a database storing clinical data of patients with prognostic variables including biological signals; an analysis module for processing the biological signals for a given patient to automatically calculate prognostic parameters of diagnostic relevance for said patient; a risk-evaluation module for estimating, using a risk-evaluation model that considers predictor variables among the prognostic variables stored in the database and the prognostic parameters calculated by the analysis module, an individualized hazard ratio of having an event or developing a medical condition within a given period of time for the given patient; and an individualized multiple-disease quantitative risk assessment module for computing, based on the risk-evaluation model and specified thresholds for each of the predictor variables, a quantification of the risk for the given patient for each of the events or medical conditions under evaluation.

Abstract: Disclosed embodiments include a method for automatic identification of activity and rest periods based on an actigraphy signal. According to an embodiment, the method comprises (a) applying one or more filters to the actigraphy signal to generate a filtered actigraphy signal, and (b) comparing the filtered actigraphy signal against an adaptive threshold to generate an output signal corresponding to the activity and rest periods. The method can be implemented as part of a medical apparatus or medical system for automatic activity/rest identification from actigraphy signals.

Abstract: Disclosed embodiments include a web-enabled system especially adapted for clinical research and clinical management of cardiovascular risk using ambulatory blood pressure monitoring (ABPM) data, actigraphy data, and clinical data. According to a disclosed embodiment the web-enabled system comprises (a) a web-enabled graphical user interface to enable a user to securely authenticate, securely upload clinical data, and navigate through a plurality of software modules; (b) a database to store user profiles, protocols, clinical data, ambulatory blood pressure monitoring (ABPM) recordings, actigraphy recordings, research data, and study results; and (c) a plurality of statistical methods and analysis techniques especially designed to automatically analyze the clinical cardiovascular risk data, and create customized reports of the results designed to assess cardiovascular risk based on ABPM and actigraphy.

Abstract: Disclosed embodiments include a method for automatic identification of activity and rest periods based on an actigraphy signal. According to an embodiment, the method comprises (a) applying one or more filters to the actigraphy signal to generate a filtered actigraphy signal, and (b) comparing the filtered actigraphy signal against an adaptive threshold to generate an output signal corresponding to the activity and rest periods. The method can be implemented as part of a medical apparatus or medical system for automatic activity/rest identification from actigraphy signals.

Abstract: Disclosed embodiments include a web-enabled system especially adapted for clinical research and clinical management of cardiovascular risk using ambulatory blood pressure monitoring (ABPM) data, actigraphy data, and clinical data. According to a disclosed embodiment the web-enabled system comprises (a) a web-enabled graphical user interface to enable a user to securely authenticate, securely upload clinical data, and navigate through a plurality of software modules; (b) a database to store user profiles, protocols, clinical data, ambulatory blood pressure monitoring (ABPM) recordings, actigraphy recordings, research data, and study results; and (c) a plurality of statistical methods and analysis techniques especially designed to automatically analyze the clinical cardiovascular risk data, and create customized reports of the results designed to assess cardiovascular risk based on ABPM and actigraphy.