Method and apparatus for processing a cyclic physiological signal

Abstract

The invention relates to a method and apparatus for processing a cyclic physiological signal (30, 40, 52, 53, 54). The method comprises the steps of repeatedly collecting (2) the physiological signal (30, 40, 52, 53, 54) over a time period (31, 32, 33) covering two or more cycles of the cyclic physiological signal (30, 40, 52, 53, 54), wherein a next time period (31, 32, 33) is adjacent to or overlaps with a previous time period (31, 32, 33), extracting values (3, 13) of a set of predefined parameters from the physiological signal (30, 40, 52, 53, 54) within each time period (31, 32, 33) which parameter values characterize the physiological signal (30, 40, 52, 53, 54) within the time period (31, 32, 33), and classifying (4, 14) the physiological signal (30, 40, 52, 53, 54) within each time period (31, 32, 33) based upon the extracted set of predefined parameter values. This provides for an efficient analysis of a cyclic physiological signal which is especially suitable for continuous monitoring of patients where a trend of a reliable physiological signal is more important than an instantaneous measurement of a reliable physiological signal, such as in a general ward environment of a hospital and / or in a home environment.

Description

FIELD OF THE INVENTION[0001]The invention relates to a method and apparatus for processing a cyclic physiological signal.BACKGROUND OF THE INVENTION[0002]The monitoring of vital body signs of patients, such as respiration rate and heart rate, in an intensive care environment in a hospital has requirements different from those in monitoring a patient in a general ward setting or in a home environment. The intensive care unit of a hospital requires an instantaneous and high reliability of the monitored parameters, whereas the general ward environment of a hospital is more focused on the trend of the monitored parameters.[0003]As an example, respiration rate has proven to be a good indicator of the deterioration of the condition of a patient and it plays a crucial role in early warning hospital systems in combination with other vital body signs. Therefore, a need for continuous and reliable monitoring of a respiration signal is seen especially in the intensive care units of hospitals. ...

AI Technical Summary

Benefits of technology

The present invention provides an efficient analysis of a cyclic physiological signal for continuous monitoring of patients. The method extracts values for a set of predefined parameters from the physiological signal and classifies each time period or frame of the signal as either acceptable or reject based on these parameters. This allows for the identification of time periods that are more reliable and accurate for analyzing the physiological signal. The method can be used in a general ward or home environment and is particularly useful for monitoring patients with respiratory or pulse problems. The method provides a reliable and classified physiological signal that can be used for further processing and analysis.

Problems solved by technology

While continuous monitoring of the respiration signal, from which the respiration rate is extracted, is available on bedside monitors for intensive care patients, various portable sensor systems are being developed to allow unobtrusive and prolonged measurement and monitoring of the respiration signal of mobile patients in general wards with minimal discomfort.

Motion artifact is a well known issue in patient monitoring as a whole, which refers to a contamination of the physiological signal and a degradation of the measurement quality caused by physical activities of a patient, such as posture change, movement and talking.

Especially for cyclic physiological signals it can become very difficult to extract the right frequency from the contaminated cyclic signal.

The motion artifact issue is more pronounced in a general ward setting than in an intensive care unit setting, since patients in the general ward setting generally have a more mobile activity pattern and are monitored most of the time without constant nursing surveillance, thus lacking knowledge on the presence of physical activities and the measurement context.

The problem becomes even more severe in the monitoring of patients in home healthcare settings.

There are a couple of intrinsic difficulties in these schemes that are hard to overcome.

For example, motion artifacts may be induced by multiple noise sources that are difficult to identify and estimate.

Another drawback is that these schemes usually require a large computational effort and are thus not efficient for a portable system.

Although this method does not apply adaptive noise cancellation, it still is aimed at providing a reliable instantaneous respiration signal on a peak-by-peak basis, which is important in intensive care settings, but which is not a prerequisite in a general ward setting.

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