Physiological signal quality assessment method and system based on constraint estimation

Abstract

The invention relates to a physiological signal quality evaluation method and system based on constrained estimation. The method comprises the following steps: receiving to-be-evaluated signal sections of a quasi-periodic physiological signal, performing pretreatment, waveform analysis identification and signal period segmentation to the signal sections, carrying out feature point detection to each cycle of signal section, and extracting preset physiological feature parameters of the physiological signal; for each signal section, combining the extracted physiological feature parameters to form feature vectors, performing constraint-based modeling according to transcendental knowledge of the physiological signal, and further establishing an analyzable evaluation system with constraint timing; and using a constraint evaluation model to trace sequential change of physiological parameters, combing a preset rule base and sequential change information, rating the signal quality of the quasi-periodic physiological signal, evaluating the validity of signal data of the signal sections of the quasi-periodic physiological signal, updating a time sequence evaluation system, and rating according to iteration of the periodic signal sections until the signal quality rating of all the signal sections of the quasi-periodic physiological signal is finished.

Description

【Technical field】 [0001] The invention relates to the field of physiological signal quality assessment, in particular to a constraint estimation-based physiological signal quality assessment method and system. 【Background technique】 [0002] For periodic physiological signals, such as ECG, photoplethysmography, respiration, blood pressure and other signals, as common human physiological signals, they contain important physiological significance in both frequency domain and time domain. It has important research value in various research and application fields such as cardiovascular disease diagnosis, sleep respiratory disease diagnosis, clinical vital sign monitoring and family disease early warning. Take a photoplethysmogram signal (PPG for short), which is referred to as a pulse wave signal, as an example. From the temporal morphology, the ascending branch reflects the passive expansion of the rapid ejection of the ventricle, and the descending branch reflects the retract...

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Problems solved by technology

[0005] However, existing methods cannot cope with changing environments, and cannot truly identify motion interference artifacts and normal physiological changes, and there are still problems of misjudgment artifacts that lead to missed alarms or false alarms

In the case where the local deformation of the signal waveform is serious and the extracted physiological feature parameters are still valid, the method of using morphological information to analyze the signal quality may determine that the signal is severely disturbed by motion and cannot contain effective physiological feature information.

In the case that the signal waveform is good but the extracted physiological feature parameters change in time series due to physiological reasons, the existing analysis methods may determine that motion interference due to abnormal changes in physiological features makes the extracted physiological features invalid and confuses motion artifacts Effects of interference and physiological changes

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