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Fault-tolerant method and device in respiratory mechanics monitoring system

A monitoring system and mechanical technology, applied in the field of medical respiratory mechanics monitoring system, can solve problems such as unsatisfactory fault tolerance mechanism, sudden change of monitoring parameters, and wrong waveform recognition, so as to eliminate adverse effects, eliminate wrong feedback or wrong diagnosis, and ensure correctness sexual effect

Inactive Publication Date: 2009-07-08
SHENZHEN MINDRAY BIO MEDICAL ELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0009] In addition, because the patient coughs or sneezes, there will be a sudden change in the monitoring parameters without a fault-tolerant mechanism, resulting in incorrect parameter calculation and even wrong waveform recognition
[0010] However, the fault-tolerant mechanism of this zero-point-spanning waveform identification method is not ideal, and cannot effectively solve these interference problems

Method used

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  • Fault-tolerant method and device in respiratory mechanics monitoring system
  • Fault-tolerant method and device in respiratory mechanics monitoring system
  • Fault-tolerant method and device in respiratory mechanics monitoring system

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specific Embodiment 1

[0058] Specific embodiment one, such as Figure 4 As shown, the main components of the respiratory mechanics monitoring system include the sensor device part, the analog amplifier circuit part, the AD acquisition part, the waveform data analysis and data processing part, and the waveform and parameter display part. machine, the processed parameters are usually also used for feedback on ventilation control. The wave recognition method and device of the present invention all take place in the wave data analysis and data processing parts.

[0059] In this embodiment, the parameters output by the respiratory mechanics detection system are subjected to fault-tolerant processing by adopting an abnormal parameter elimination method, so as to ensure the correctness of the respiratory cycle parameters.

[0060] Its specific flow chart is as Figure 5 shown.

[0061] In order to avoid erroneous feedback to the control mechanism due to large fluctuations in the derived parameters, aft...

specific Embodiment 2

[0063] Specific embodiment two, on the basis of specific embodiment one, in order to further eliminate the high-frequency respiratory signal component (waveform whose fluctuation cycle is less than the bottom limit of the respiratory cycle measurement range) introduced by the lax valve closure, according to the state of the respiratory waveform ( When wave_counter) calculates or collects the derived parameters of the respiratory cycle, the determination of the inspiratory time, the expiratory time and the IE ratio (ie, the respiratory time ratio) is added. For example, for waveforms with inspiratory time less than 160ms, expiratory time less than 200ms, and respiratory cycles with IE ratios other than 4:1 to 1:10, they are all considered as interference signals without refreshing and sending derived parameters , because this method is related to the time of inhalation and exhalation, it can be called the time limit method.

[0064] Its processing flow is as Figure 6 shown, i...

specific Embodiment 3

[0075] Specific embodiment 3. This embodiment is a solution to continue to improve on the basis of specific embodiments 1 and 2. The respiratory waveform recognition method adopts a zero-point leap-forward recognition method, and only increases the setting of positive and negative thresholds, such as Figure 7 As shown, the monitored flow value is also compared with positive and negative thresholds, instead of comparing with "0" as in the prior art. The respiratory waveform recognition process of this embodiment is as follows: Figure 8 shown, including the following steps:

[0076] In step 30, detect the flow value of the respiratory airflow, and then perform step 31;

[0077] In step 31, when the flow value is greater than the positive threshold, set cur_point_sign=1, when the flow value is less than the negative threshold, set cur_point_sign=-1, when the flow value is between the positive and negative thresholds, keep the current flow sampling point identification unchang...

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Abstract

The invention discloses a fault-tolerant method and device in a respiratory mechanics monitoring system, which is used for eliminating abnormal parameters in the derived parameters after the parameter calculation step; temporarily storing and calculating the derivation of N consecutive breathing cycles in a first-in-first-out manner Parameter, compare the parameter value P0 of the current period with the parameter value Psurp+1 of the previous non-abnormal fluctuation, if |P0-Psurp+1|<|1 / 3 Psurp+1|, then calculate the latest N periods The arithmetic mean of this derived parameter replaces the parameter value P0 of the current period; if |P0-Psurp+1|≥|1 / 3 Psurp+1|, remove all abnormal fluctuations from the calculated nearest N adjacent points The average value of the remaining points after the point replaces the parameter value P0 of the current cycle, and adds 1 to the number of continuous abnormal fluctuation points. When the number of continuous abnormal fluctuation points is greater than 3, the arithmetic mean value of these consecutive N derived parameter points replaces the current cycle The parameter value P0. The invention eliminates the adverse effect on the feedback caused by the abnormal mutation of the signal by selectively averaging the derived parameters, and ensures the correctness of the derived parameters.

Description

【Technical field】 [0001] The invention relates to a medical respiratory mechanics monitoring system, in particular to a fault-tolerant method and device in the respiratory mechanics monitoring system. 【Background technique】 [0002] In medical monitoring, the respiratory mechanics module can directly measure the flow rate of the human respiratory airflow and the pressure in the air circuit, and describe the real-time respiratory waveform of the two basic parameters, and use the periodic changes of these two parameters to calculate 18 clinical parameters. The export parameters to use. After obtaining these parameters, the respiratory mechanics module sends the values ​​of these parameters through serial communication to control anesthesia machines, ventilators or monitors. Since the monitoring parameters of respiratory mechanics directly feed back the ventilation control of the anesthesia machine and ventilator, if these parameters are wrong, it may cause the wrong action of...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): A61B5/08G06F17/00
Inventor 金巍李新胜
Owner SHENZHEN MINDRAY BIO MEDICAL ELECTRONICS CO LTD
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