Personalized baseline correction for detection of infection from physiological signals

EP4746762A1Pending Publication Date: 2026-05-27KONINKLIJKE PHILIPS NV
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Patent Information

Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
KONINKLIJKE PHILIPS NV
Filing Date
2024-07-12
Publication Date
2026-05-27

AI Technical Summary

Technical Problem

Existing unobtrusive vital sign monitoring systems face challenges in detecting early onset symptoms of infections due to individual variability in physiological signals, which complicates the training and accuracy of AI models.

Method used

A personalized baseline correction system that uses a calibration process to create a personalized normalization process for each patient, considering the entire distribution of baseline values rather than just the mean and variance, to standardize physiological data and improve detection of subtle vital sign changes.

Benefits of technology

The system effectively reduces data variability, improves normalization of patient monitoring data without assuming a Gaussian distribution, minimizes data requirements for AI models, and enhances the detection of early onset symptoms of infections.

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Abstract

A system (1) includes a monitoring device (10) including at least one sensor (12). The wearable monitoring device is configured to be worn by an associated patient and the at least one sensor configured to measure physiological sensor data (13) of the associated patient wearing the monitoring device. An electronic processor (16) is integrated with or in wireless communication with the monitoring device. The electronic processor is programmed to: calibrate a personalized normalization process for the associated patient by applying a calibration process to physiological sensor data measured of the associated patient during a baseline time period; apply the personalized normalization process to physiological sensor data measured of the associated patient during an analysis time period to generate normalized physiological sensor data; analyze the normalized physiological sensor data to identify a condition of the associated patient; and output an indication of the identified condition of the associated patient.
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