CN-122004828-A - Respiratory effort assessment system based on respiratory flow self-power spectrum

CN122004828ACN 122004828 ACN122004828 ACN 122004828ACN-122004828-A

Abstract

The invention belongs to the technical field of respirators, and relates to a respiratory effort evaluation system based on a respiratory flow self-power spectrum. The system comprises a signal acquisition module, a self-power spectrum calculation module, an extraction module, a statistical analysis module and an evaluation output module, wherein the signal acquisition module is used for acquiring continuous respiratory flow signals acquired by a flow sensor of a breathing machine in real time, the self-power spectrum calculation module is used for preprocessing the respiratory flow signals, performing time-frequency conversion and calculating to obtain a self-power spectrum, the extraction module is used for extracting energy of a preset high-frequency band in the self-power spectrum as a high-frequency energy component, the high-frequency band is higher than a main frequency band corresponding to a human body basic respiratory frequency, the statistical analysis module is used for establishing a baseline value for evaluation based on statistical data of the high-frequency energy component in historical respiratory flow data, and the evaluation output module is used for comparing the high-frequency energy component at the current moment with the baseline value, and evaluating respiratory effort state as abnormal when the energy component of the high-frequency component is smaller than the baseline value, otherwise normal.

Inventors

LIU XIANGNAN
ZENG KEJUN

Assignees

北京谊安健康科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260209

Claims (10)

1. A respiratory effort assessment system based on a respiratory flow self-power spectrum, comprising: The signal acquisition module is used for acquiring continuous respiration flow signals acquired by the flow sensor of the respirator in real time; The self-power spectrum calculation module is used for preprocessing the respiratory flow signal, converting time frequency and calculating to obtain a self-power spectrum; the extraction module is used for extracting energy of a preset high-frequency band from the self-power spectrum as a high-frequency energy component, wherein the high-frequency band is higher than a main frequency band corresponding to the basic respiratory frequency of a human body; A statistical analysis module for establishing a baseline value for evaluation based on statistical data of high frequency energy components in the historical respiratory flow data, and And the evaluation output module is used for comparing the high-frequency energy component at the current moment with the baseline value, and evaluating that the respiratory effort state is abnormal when the high-frequency energy component is smaller than the baseline value, or else, the respiratory effort state is normal.
2. The respiratory effort assessment system based on respiratory flow self-power spectrum according to claim 1, wherein the preprocessing of the self-power spectrum calculation module comprises dividing the continuous respiratory flow signal into a plurality of fixed length data segments with overlap between adjacent data segments, and applying a window function to each data segment to reduce spectral leakage.
3. The respiratory effort assessment system based on respiratory flow self-power spectrum according to claim 1, wherein the self-power spectrum of the self-power spectrum calculation module is calculated using a Welch method.
4. The respiratory effort assessment system based on respiratory flow self-power spectrum according to claim 1, wherein the extraction module has a preset high frequency band of 0.5 Hz to 1.2 Hz.
5. The respiratory effort assessment system based on respiratory flow self-power spectra of claim 1, wherein the processing of the statistical analysis module comprises: Identifying and excluding abnormal respiratory event fragments in the respiratory flow signal, the abnormal respiratory event fragments including apneas, obstructions, and cheyne-stokes respiration; Calculating an average value by using at least 20 self-power spectrum data of the stable respiration stage, and multiplying the average value by a preset correction coefficient to obtain a baseline value for evaluation, wherein the value range of the correction coefficient is 2.5 to 3.5.
6. A respiratory effort assessment system based on a respiratory flow self-power spectrum, comprising: The signal acquisition module is used for acquiring continuous respiration flow signals acquired by the flow sensor of the respirator in real time; The self-power spectrum calculation module is used for preprocessing the respiratory flow signal, converting time frequency and calculating to obtain a self-power spectrum; The frequency band marking module is used for acquiring the current respiratory frequency based on the continuous respiratory flow signal, marking the frequency band within the set range of the respiratory frequency point as a main frequency band, and marking the rest as other frequency bands; an instruction value calculation module for calculating ratio values based on the power spectrums of the current main frequency band and other frequency bands to obtain instruction values for representing the matching degree of respiratory effort and output of the breathing machine, and And the evaluation output module is used for evaluating the respiratory effort state according to the indicated value.
7. The respiratory effort assessment system based on respiratory flow self-power spectrum according to claim 6, wherein in the frequency band labeling module, the frequency band within the set range of respiratory frequency points is r±v, wherein v takes a value of 50% of the current respiratory frequency R.
8. The respiratory effort assessment system based on respiratory flow self-power spectra of claim 6, wherein the indicator calculation module obtains an indicator The method comprises the following steps: ; Wherein, the As the total power of the primary frequency band, Is the total power of other frequency bands.
9. The respiratory effort assessment method based on respiratory flow self-power spectra of claim 8, wherein the processing of the assessment output module comprises: respiratory rate between 3 and 60 times per minute, and indicates If the power judgment threshold is larger than the power judgment threshold, judging that the respiratory effort state is abnormal, otherwise, judging that the respiratory effort state is normal.
10. The respiratory effort assessment system based on a respiratory flow self-power spectrum according to claim 1 or claim 6, further comprising an adjustment instruction generation module for generating adjustment instructions to automatically or prompt adjustment of a therapy parameter of the ventilator when the assessed respiratory effort status is abnormal, the therapy parameter comprising at least one of support pressure, trigger sensitivity or respiratory frequency.

Description

Respiratory effort assessment system based on respiratory flow self-power spectrum Technical Field The invention belongs to the technical field of respirators, and particularly relates to a respiratory effort evaluation system based on a respiratory flow self-power spectrum. Background The current breathing machine sequentially and circularly performs the conversion from inflating to lung, inhaling to exhaling, discharging alveolar gas and converting exhaling to inhaling according to the designated treatment pressure, respiratory cycle, respiratory ratio and other parameters, and assists the human body to breathe, thereby solving the abnormal event in the breathing process. This requires that the ventilator be required to predict the breathing rhythm of the human body, including respiratory rate and respiratory ratio, to assist the breathing rhythm of the human body with less conflict in central nervous control, but current ventilator prediction techniques are not mature, fail to incorporate various states of the human body's breathing rhythm into control, and there are sudden changes in breathing rhythm and airway pressure, including posture variations, lesions or other factors, which may lead to the generation of a state of mismatch of ventilator output and respiratory effort, and there are a number of situations where appropriate set parameters are not titrated. Due to the influence of various external factors, the pressure change state of the breathing machine is inconsistent with the breathing action of a user, namely, the phenomenon of asynchronism of the breathing machine occurs. Regarding how to solve the problem of judging whether the current control parameters of the breathing machine match with the breathing rhythm of a patient, the prior art mainly relies on a patient filling scale to carry out subjective feeling evaluation, lacks the objective and quantitative judgment standard of the breathing effort state, the evaluation result is easily influenced by subjective factors of the patient, and the esophageal pressure and diaphragm electrical activity monitoring method is too severely required and cannot be applied to conventional treatment. The traditional method can not realize real-time breathing effort monitoring in the using process of the breathing machine through scale evaluation and post-hoc data analysis, and is difficult to discover and adjust breathing machine parameters in time so as to improve the state of a patient. Although there are some methods that use frequency domain analysis, by converting the frequency domain, looking at the matching ratio of the respiratory flow waveform and the pressure waveform, it is possible to identify man-machine unsynchronized events (e.g., ineffective triggers, double triggers, etc.). But such methods simply analyze synchronicity through the frequency domain. And respiratory synchronicity may also change due to a lag in ventilator control response, or occasional respiratory obstruction. By this method, a condition in which minute shortness of breath cannot be detected, which is often a precursor to the occurrence of a respiratory event, is detected. At the same time, this approach lacks the ability to quantitatively evaluate respiratory rhythm coordination. Disclosure of Invention The invention aims to overcome the defects of the prior art and provides a respiratory effort evaluation system based on a respiratory flow self-power spectrum. In view of the above, embodiment 1 of the present invention provides a respiratory effort evaluation system based on a respiratory flow self-power spectrum, comprising: The signal acquisition module is used for acquiring continuous respiration flow signals acquired by the flow sensor of the respirator in real time; The self-power spectrum calculation module is used for preprocessing the respiratory flow signal, converting time frequency and calculating to obtain a self-power spectrum; the extraction module is used for extracting energy of a preset high-frequency band from the self-power spectrum as a high-frequency energy component, wherein the high-frequency band is higher than a main frequency band corresponding to the basic respiratory frequency of a human body; A statistical analysis module for establishing a baseline value for evaluation based on statistical data of high frequency energy components in the historical respiratory flow data, and And the evaluation output module is used for comparing the high-frequency energy component at the current moment with the baseline value, and evaluating that the respiratory effort state is abnormal when the high-frequency energy component is smaller than the baseline value, or else, the respiratory effort state is normal. Preferably, the preprocessing of the self-power spectrum calculation module includes dividing the continuous respiratory flow signal into a plurality of fixed length data segments with overlap between adjacent data segments, and applying a window functio