CN110189822A - A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform - Google Patents
A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform Download PDFInfo
- Publication number
- CN110189822A CN110189822A CN201910436139.3A CN201910436139A CN110189822A CN 110189822 A CN110189822 A CN 110189822A CN 201910436139 A CN201910436139 A CN 201910436139A CN 110189822 A CN110189822 A CN 110189822A
- Authority
- CN
- China
- Prior art keywords
- data
- analysis
- ventilator
- wave
- wave data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The present invention provides a kind of new ventilator data analysing methods, and the functions such as cleaning, storage, inquiry and the calculating of ventilator data may be implemented, and realize the functions such as big data analysis, waveform analysis, patient model processing, AI intelligence auxiliary operation.Simultaneously, the analysis method of the application is available and analyzes the advanced statistical parameter of ventilator, available valuable medical technology information, and it can optimize for workflow management and support data are provided, by the comprehensive analysis to data, realize that the real time monitoring of urgent patient and dying early warning, clinical emphasis data show, formulate the respiratory therapy scheme for meeting individuation demand by respiratory therapy aid decision tree, auxiliary doctor.Can also easily realize classifying type summarize or timesharing between the data statistic analysis function that summarizes, help doctor easily track treatment embodiment, provide foundation for recruitment evaluation, formation clinical data closed loop provides foundation for scientific research and clinical position.
Description
Technical field
The invention belongs to medical data intelligent analysis fields, are related to one kind based on mechanical ventilation parameters and waveform and carry out intelligence
The method that can be analyzed.
Background technique
With stepping up for China's medical level and continuing to increase for medical treatment investment, ventilator this equipment via
The emergency set of ICU is changed into the daily therapeutic equipment of numerous departments such as emergency treatment, ICU, division of respiratory disease, department of anesthesia.With ventilator
Universal and installation amount promotion, doctors and nurses's more and more times are being occupied to the management of patient's mechanical ventilation.
Because China is medical, the anxiety of doctor's resource, and the respiratory therapist of China's profession is not yet universal, and ventilator is usual
It is operated by doctors and nurses.This also results in low efficiency of making the rounds of the wards, and each patient checks one by one wastes time energy and to personnel
It is required that high, the difference of judgment criteria between different doctors, results in patient and be unable to get and accurately treat.
Meanwhile in daily clinical use, ventilator can generate a large amount of data, wherein there is partial data to manage hospital
Reason, clinical diagnosis or scientific research have considerable meaning.And due to the past ventilator network savvy missing or more singly
One, cause these data that can not cause great loss by long-term storage, unified acquisition, calculating and displaying.Existing market
It is upper it is in short supply can allow the breathing machine equipment of hospital give full play to potentiality, keep up with technology trend, help school equipment management,
Further technical solution in medical services, scientific research.Based on this, applicant proposes that a kind of pair of ventilator data are divided
Mass data caused by ventilator is used the treatment work to assist doctor by the method for analysis.
Summary of the invention
In view of the above existing problems in the prior art, the present invention provides one kind to carry out intelligence based on mechanical ventilation parameters and waveform
The method that can be analyzed, the data intelligent for ventilator this kind Medical Devices provide support approach.
The invention adopts the following technical scheme:
A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform, it is characterised in that: the following steps are included:
S1 selectes analysis time section, reads all supplemental characteristics of the specified ventilator in analysis time section；
S2 judges the supplemental characteristic in analysis time section with the presence or absence of manual intervention according to supplemental characteristic read in S1
Or operation selectes analysis time section again, executes S1 again if there are manual intervention or operations for supplemental characteristic；
S3, if judging in S2, there is no manual intervention or operations in analysis time section, read setting item data, to sensor
The single value and average value of the monitoring item data periodically generated carry out threshold decision, and according to different threshold ranges, judgement is
It is no to need that Wave data is combined further to analyze；
S4 carries out Wave data if the threshold decision result in S3 is to need that Wave data is combined further to analyze first
Pretreatment, the pretreatment of the Wave data the following steps are included:
S4-1 selects filtering algorithm, is filtered to Wave data according to setting option data cases, Exclusion analysis interference；
S4-2, if the unallocated breathing phase of original waveform data, according to ideal machine ventilating model, according to each waveform situation of change
Breathing phase is divided to all Wave datas；
S5, Wave data analysis
S5-1 selects the mechanical ventilation model according to setting item data, substitutes into the supplemental characteristic of each breathing phase, describes ideal
Respiratory curve；
S5-2 calculates the identical breaths curve in actual waveform curve and the step S5-1 for each breathing phase section
Irrelevance；
S5-3 counts all irrelevance data of the step S5-2, according to the machinery selected in the step S5-1
The threshold range that ventilating model divides is analyzed, and analysis result is formed.
Further, judge in the step S2 supplemental characteristic in analysis time section with the presence or absence of manual intervention or
Whether all operation is embodied in and judges all setting item datas consistent indifference.
Further, judge whether to need to combine Wave data in the step S3 specifically: according to different threshold values
Range has following situations:
Data value continues to analyze meaningless there are larger fluctuation or interference；
Data value reflects a certain specific condition, without further analysis, can immediately arrive at analysis conclusion；
Data value needs that Wave data is combined further to analyze.
Further, supplemental characteristic in the step S1 is the running parameter and Wave data of ventilator, the ginseng
Number data include setting option data, monitoring item data, Wave data；The setting item data is can be with manual operation in ventilator
The parameter of modification, the monitoring item data are the monitoring data that sensor of breathing machine periodically generates, and the Wave data is to exhale
The monitoring data that suction machine sensor continuity generates.
Further, in the step S1, longer analysis time segment length is selected, to exclude the special of short period
Change data interference.
Further, in the step S3, the single value of item data, average value, variation range are monitored to analysis result
There is directive significance.
Further, in the step S4-1, Wave data can choose progress or not according to the difference of data model
Be filtered, filtering processing can exclude a degree of hardware (such as the equipment components such as sensor, pipeline) and
Interference caused by patient body activity, so that the case where data are reacted is more accurate.
Further, in the step S5-1, mechanical ventilation model is selected by way of automatic or manual；Institute
It states in step S5-3, analysis is the result is that formed based on mechanical ventilation model and data statistics, mechanical ventilation as used herein
Model is generally this already existing mechanical ventilation model of the prior art.
The present invention provides a kind of new ventilator data analysing method, ventilator access Internet of Things acquisition module it
Afterwards, the data that ventilator generates can by it is real-time, completely be transferred in big data analysis system, these data are by the application
Analytical, after processing, can show in the indoor display large-size screen monitors of section or far away from the plate of expert thousands of miles away
On computer.
The functions such as cleaning, storage, inquiry and the calculating of ventilator data may be implemented in the data analysing method of the application, real
The functions such as existing big data analysis, waveform analysis, patient model processing, AI intelligence auxiliary operation.Meanwhile the analysis method of the application
Advanced statistical parameter that is available and analyzing ventilator, such as: pressure variation, tidal volume variation, flow variation, time become
Different, lung stretching coefficient, atelectasis coefficient, dynamic compliance/dynamic resistance, the reference of ASV algorithm etc..
By automatically analyzing to Wave data, automaton study obtains available the data analysing method of the application
Valuable medical technology information, such as: whether ventilator parameter setting is appropriate, if there are hypoventilation, man-machine confrontation feelings
Condition analysis etc..By smartphone and category filter information, the energy of medical staff is concentrated, realizes fine-grained management, and can be with
Support data are provided for workflow management optimization.By the big data analysis to basic ventilation parameters and subtle waveform model, in real time
Monitoring breathing machine running situation realizes key clinical alarm, patient condition trend analysis, man-machine confrontation analysis, clinical expert
Instruct the functions such as assisted ventilation target setting.By comprehensive analysis to data, it is the real time monitoring of realizing urgent patient, dying
Early warning.Clinical emphasis data are shown, by respiratory therapy aid decision tree, doctor are assisted to formulate the breathing for meeting individuation demand
Therapeutic scheme.
The analysis method of the application can also easily realize classifying type summarize or timesharing between the data statistic analysis that summarizes
Function, helps doctor easily to track treatment embodiment, provides foundation for recruitment evaluation, forms clinical data closed loop, is section
It grinds and provides foundation with clinical position.
Specific embodiment
For a further understanding of the present invention, embodiment of the present invention is made into one below in conjunction with embodiment and comparative example
Detailed description is walked, but embodiments of the present invention are not limited to this.
The analysis of 1 patient's offline condition of embodiment:
S1 selectes an analysis time section, reads all supplemental characteristics of the specified ventilator in analysis time section；
S2 judges the supplemental characteristic in analysis time section with the presence or absence of manual intervention according to supplemental characteristic read in S1
Or operation, it is embodied in and judges all parameters that can be modified with manual operation, i.e. setting option parameter, if all consistent indifferences
Not；If supplemental characteristic there are manual intervention or operation, continue to analyze it is meaningless, need to select again analysis time section, again
Execute S1；If manual intervention or operation is not present in supplemental characteristic, next step is executed；
S3, if judging in S2, there is no manual intervention or operations in analysis time section, read setting item data, when reading this
Between monitoring item data in section, calculate average respiratory rate, average pressure support level (PS), average expiration end positive pressure
(PEEP), the numerical value such as average oxygen concentration (FiO2), according to current ventilator operational mode (i.e. setting option data cases) delimit with
The threshold range of upper numerical value judges whether ventilator support level is sufficiently low；
S4 determines that current patient is not suitable for carrying out off-line operation if support level is higher；
S5, if support level is sufficiently low, according to the setting item data in the period, calculate BSA, IBW of patient, SMV, SVt,
Monitoring item datas all in above data and the period are substituted into the calculation formula of mechanical ventilation model, judgement by the data such as Sf
Percentage of all calculated results in a certain threshold range.
That is: using the calculation formula abs of mechanical ventilation model ((Rate-Sf)/Sf) < 20%, the Sf of patient is substituted into,
And respiratory rate (Rate) value of all monitoring items, calculating meet the quantity of this formula and the quantity accounting of all monitoring items,
For assessing automatic respiratory rhythm.Present analysis has been drawn a conclusion, without further analysis waveform data.
2 patient of embodiment whether there is endogenous PEEP:
S1 selectes an analysis time section, reads all supplemental characteristics of the specified ventilator in analysis time section；
S2 judges the supplemental characteristic in analysis time section with the presence or absence of manual intervention according to supplemental characteristic read in S1
Or operation, it is embodied in and judges all parameters that can be modified with manual operation, i.e. setting option parameter, if all consistent indifferences
Not；If supplemental characteristic there are manual intervention or operation, continue to analyze it is meaningless, need to select again analysis time section, again
Execute S1；If manual intervention or operation is not present in supplemental characteristic, next step is executed；
S3 calculates average monitored PEEP value, judges the difference of the PEEP of the value and setting option to the monitoring item data in the period
Whether value is greater than certain threshold value, if then continuing to execute analysis；If otherwise concluding that patient, there is no endogenous PEEP；
S4 calculates the ratio of average Vti and average Vte, judges whether the value is greater than one to the monitoring item data in the period
Threshold value is determined, if then continuing to execute analysis；If otherwise concluding that patient, there may be endogenous PEEP；
S5 is filtered Wave data, and divides breathing phase；Ideal machine ventilating model is substituted into, identical breaths are described
The actual flow waveform in each breathing phase section is compared with desired flow rate of waveform, calculates and count end-tidal flow by curve
Departure degree, if departure degree be higher than certain threshold value, be determined as patient confirmation there are endogenous PEEP, if be lower than certain threshold
Value is then determined as that patient falls into there are air flue and closes.
The above described is only a preferred embodiment of the present invention, being not the limit for making any other form to the present invention
System, made any modification or equivalent variations, still fall within scope of the present invention according to the technical essence of the invention.
Claims (8)
1. a kind of method for carrying out intellectual analysis based on mechanical ventilation parameters and waveform, it is characterised in that: the following steps are included:
S1 selectes analysis time section, reads all supplemental characteristics of the specified ventilator in analysis time section；
S2 judges the supplemental characteristic in analysis time section with the presence or absence of manual intervention according to supplemental characteristic read in S1
Or operation selectes analysis time section again, executes S1 again if there are manual intervention or operations for supplemental characteristic；
S3, if judging in S2, there is no manual intervention or operations in analysis time section, read setting item data, to sensor
The single value and average value of the monitoring item data periodically generated carry out threshold decision, and according to different threshold ranges, judgement is
It is no to need that Wave data is combined further to analyze；
S4 carries out Wave data if the threshold decision result in S3 is to need that Wave data is combined further to analyze first
Pretreatment, the pretreatment of the Wave data the following steps are included:
S4-1 selects filtering algorithm, is filtered to Wave data according to setting option data cases, Exclusion analysis interference；
S4-2, if the unallocated breathing phase of original waveform data, according to ideal machine ventilating model, according to each waveform situation of change
Breathing phase is divided to all Wave datas；
S5, Wave data analysis
S5-1 selects the mechanical ventilation model according to setting item data, substitutes into the supplemental characteristic of each breathing phase, describes ideal
Respiratory curve；
S5-2 calculates the identical breaths curve in actual waveform curve and the step S5-1 for each breathing phase section
Irrelevance；
S5-3 counts all irrelevance data of the step S5-2, according to the machinery selected in the step S5-1
The threshold range that ventilating model divides is analyzed, and analysis result is formed.
2. according to the method described in claim 1, it is characterized by: judging the parameter in analysis time section in the step S2
Whether all data whether there is manual intervention or operation, be embodied in and judge all setting item datas consistent indifference
Not.
3. according to the method described in claim 1, it is characterized by: judging whether to need to combine Wave data in the step S3
Specifically: according to different threshold ranges, there is following situations:
Data value continues to analyze meaningless there are larger fluctuation or interference；
Data value reflects a certain specific condition, without further analysis, can immediately arrive at analysis conclusion；
Data value needs that Wave data is combined further to analyze.
4. according to the method described in claim 3, it is characterized by: the supplemental characteristic in the step S1 is the work of ventilator
Parameter and Wave data, the supplemental characteristic include setting item data, monitoring item data, Wave data；The setting item data
For the parameter that can be modified in ventilator with manual operation, the monitoring item data is the monitoring that sensor of breathing machine periodically generates
Data, the Wave data are the monitoring data that sensor of breathing machine continuity generates.
5. according to the method described in claim 1, it is characterized by: selecting longer analysis time segment length in the step S1
Degree, to exclude the specialization data interference of short period.
6. according to the method described in claim 1, it is characterized by: monitoring the single value of item data in the step S3, being averaged
Value, variation range have directive significance to analysis result.
7. according to the method described in claim 1, it is characterized by: Wave data is according to data model in the step S4-1
Difference, can choose progress or without filtering processing.
8. according to the method described in claim 1, it is characterized by: in the step S5-1, mechanical ventilation model by automatic or
Manually mode is selected；In the step S5-3, analysis is the result is that formed based on mechanical ventilation model and data statistics.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910436139.3A CN110189822A (en) | 2019-05-23 | 2019-05-23 | A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910436139.3A CN110189822A (en) | 2019-05-23 | 2019-05-23 | A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110189822A true CN110189822A (en) | 2019-08-30 |
Family
ID=67717463
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910436139.3A Pending CN110189822A (en) | 2019-05-23 | 2019-05-23 | A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110189822A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111383764A (en) * | 2020-02-25 | 2020-07-07 | 山东师范大学 | Correlation detection system for mechanical ventilation driving pressure and related events of breathing machine |
| WO2023102820A1 (en) * | 2021-12-09 | 2023-06-15 | 深圳迈瑞生物医疗电子股份有限公司 | Medical device and ventilation state recognition method |
| WO2024001010A1 (en) * | 2022-07-01 | 2024-01-04 | 上海术木医疗科技有限公司 | Mechanical ventilation treatment data management method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100249631A1 (en) * | 2009-03-30 | 2010-09-30 | Nihon Kohden Corporation | Respiratory waveform analyzer |
| CN104688239A (en) * | 2015-03-26 | 2015-06-10 | 北京怡和嘉业医疗科技有限公司 | Method and system for determining categories of sleep-related breathing events |
| US20170255756A1 (en) * | 2014-09-12 | 2017-09-07 | Mermaid Care A/S | Mechanical ventilation system for respiration with decision support |
| CN108186018A (en) * | 2017-11-23 | 2018-06-22 | 苏州朗开信通信息技术有限公司 | A kind of breath data processing method and processing device |
| CN108784704A (en) * | 2018-07-05 | 2018-11-13 | 广州和普乐健康科技有限公司 | A kind of breathing obstruction detection method |
| CN109589117A (en) * | 2018-10-31 | 2019-04-09 | 深圳市龙华区中心医院 | It is a kind of based on the Respiratory Medicine of big data patient monitor control method and system |
| CN109718440A (en) * | 2018-12-28 | 2019-05-07 | 北京谊安医疗系统股份有限公司 | Reduce the method and system of Breathing Suppotion equipment man-machine confrontation |
-
2019
- 2019-05-23 CN CN201910436139.3A patent/CN110189822A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100249631A1 (en) * | 2009-03-30 | 2010-09-30 | Nihon Kohden Corporation | Respiratory waveform analyzer |
| US20170255756A1 (en) * | 2014-09-12 | 2017-09-07 | Mermaid Care A/S | Mechanical ventilation system for respiration with decision support |
| CN104688239A (en) * | 2015-03-26 | 2015-06-10 | 北京怡和嘉业医疗科技有限公司 | Method and system for determining categories of sleep-related breathing events |
| CN108186018A (en) * | 2017-11-23 | 2018-06-22 | 苏州朗开信通信息技术有限公司 | A kind of breath data processing method and processing device |
| CN108784704A (en) * | 2018-07-05 | 2018-11-13 | 广州和普乐健康科技有限公司 | A kind of breathing obstruction detection method |
| CN109589117A (en) * | 2018-10-31 | 2019-04-09 | 深圳市龙华区中心医院 | It is a kind of based on the Respiratory Medicine of big data patient monitor control method and system |
| CN109718440A (en) * | 2018-12-28 | 2019-05-07 | 北京谊安医疗系统股份有限公司 | Reduce the method and system of Breathing Suppotion equipment man-machine confrontation |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111383764A (en) * | 2020-02-25 | 2020-07-07 | 山东师范大学 | Correlation detection system for mechanical ventilation driving pressure and related events of breathing machine |
| CN111383764B (en) * | 2020-02-25 | 2024-03-26 | 山东师范大学 | Correlation detection system for mechanical ventilation driving pressure and ventilator related event |
| WO2023102820A1 (en) * | 2021-12-09 | 2023-06-15 | 深圳迈瑞生物医疗电子股份有限公司 | Medical device and ventilation state recognition method |
| WO2024001010A1 (en) * | 2022-07-01 | 2024-01-04 | 上海术木医疗科技有限公司 | Mechanical ventilation treatment data management method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110189822A (en) | A method of intellectual analysis is carried out based on mechanical ventilation parameters and waveform | |
| CN112675393B (en) | Ventilator removing management system and method | |
| CN103330979B (en) | The respirator of a kind of respirator control method and application controls method | |
| CN101500633B (en) | Ventilator monitor system and method of using same | |
| Sinha et al. | Deadspace ventilation: a waste of breath! | |
| JP2005505817A (en) | A system to support clinical judgment by modeling the collected patient medical information | |
| CN108847274A (en) | A kind of vital sign data processing method and system based on cloud platform | |
| Ye et al. | FENet: a frequency extraction network for obstructive sleep apnea detection | |
| CN108257648B (en) | Medical health data management system based on big data | |
| CN110179465A (en) | Mechanical ventilation off line quantitative estimation method, device, equipment and storage medium | |
| CN110232961A (en) | A kind of speech recognition intelligent anesthesia system based on big data | |
| CN115200911A (en) | Mechanical ventilation analysis early warning method and system | |
| CN112652391A (en) | System for identifying acute exacerbation of chronic obstructive pulmonary disease | |
| CN110537917A (en) | mechanical ventilation intelligent monitoring system and monitoring method based on respiratory mechanics | |
| CN110277158A (en) | A method of being suitable for all kinds of ventilators and carries out automation adaptation and arrange storing data | |
| Durmuşoğlu et al. | Remembering Medical Ventilators and Masks in the Days of COVID-19: Patenting in the Last Decade in Respiratory Technologies | |
| Lozano-Zahonero et al. | Automated mechanical ventilation: adapting decision making to different disease states | |
| CN117352165A (en) | Postoperative rehabilitation nursing method and system for old people | |
| CN112466469A (en) | Major crisis and death risk prediction method | |
| CN205198627U (en) | Breathing machine treatment quality control system | |
| WO2024001010A1 (en) | Mechanical ventilation treatment data management method and system | |
| CN114887169A (en) | Intelligent control decision method and system for breathing machine | |
| Naseri et al. | Intelligent rule extraction in complex event processing platform for health monitoring systems | |
| Pappada et al. | Contributing factors to operating room delays identified from an electronic health record: a retrospective study | |
| CN109589117A (en) | It is a kind of based on the Respiratory Medicine of big data patient monitor control method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |