Real-Time Analysis of Physiological Data to Support Medical Applications

Abstract

This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.

Triggers and Monitoring in Intelligent Personal Health Record

“…Apiletti et al [1] proposed a framework for monitoring the patient's health condition via real-time analysis of physiological data. Given historical physiological data, this framework automatically learns detection models tailored to specific conditions, such as a particular disease or a specific patient.…”

A 4-Layer Supervising Unit for Extra-Corporal Circulation

“…Garg et al [12] give a systematic summary on the effects of CDSS on patient and practitioner performance. Recently Apiletti et al [13] introduced a flexible framework for physiological signal processing which also includes data mining methods to assess a patient's health status and to detect potential risks. The requirements and specifications for such systems are manifold and strongly depend on the application.…”

“…Standard features include the calculation of minimum, maximum and mean values of a signal. Especially in medical data analysis distance to given threshold values (alarm thresholds), long and short-term trend analysis is of interest (see [13]). Also pattern detection methods such as QRS detection for ECG signals or the algorithm described below belong to this layer.…”

“…So far, we implemented the framework in C++ with focus on the data acquisition and analysis layer. We integrated standard analysis techniques as well as trend analysis as described in [13].…”

Probabilistic model definition for physiological state monitoring

“…More recently [4] proposes to extract some features from physiological data and to classify them using a risk criterion. Numerous studies propose very complex signal processing techniques for physiological data modeling such as heart rate values [5,6] without considering context-dependencies.…”