CN115414026A - Automatic breath segmentation method and system based on flow velocity waveform - Google Patents
Automatic breath segmentation method and system based on flow velocity waveform Download PDFInfo
- Publication number
- CN115414026A CN115414026A CN202211056849.1A CN202211056849A CN115414026A CN 115414026 A CN115414026 A CN 115414026A CN 202211056849 A CN202211056849 A CN 202211056849A CN 115414026 A CN115414026 A CN 115414026A
- Authority
- CN
- China
- Prior art keywords
- respiratory
- data
- waveform
- cycle
- time
- 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
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Pulmonology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Physiology (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a method, a system, equipment and a computer readable storage medium for automatically segmenting respiration based on flow velocity waveforms, wherein the method comprises the following steps: acquiring respiratory waveform data of a person to be measured; preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero; performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data; and inputting the characteristic data of the person to be measured into a classification model to obtain a classification result of the characteristic data.
Description
Technical Field
The invention relates to the field of respiration monitoring, in particular to a respiration automatic segmentation method based on a flow velocity waveform and a system thereof.
Background
Mechanical ventilation refers to the replacement or assistance of the work of the respiratory muscles by a ventilator when the respiratory organs are unable to maintain normal gas exchange, i.e. respiratory failure occurs. Mechanical ventilation strives for treatment time and creates conditions for respiratory failure caused by various clinical reasons and other various diseases needing respiratory function support.
Invasive mechanical ventilation therapy is an important treatment for severe patients with respiratory failure, however, inappropriate invasive mechanical ventilation may cause ventilator-related lung injury such as barotrauma and volume injury, and proper monitoring during mechanical ventilation helps to avoid adverse effects of mechanical ventilation. During invasive mechanical ventilation monitoring, the acquired flow rate and pressure waveforms are usually continuous waveforms, however, most monitoring parameters such as peak airway pressure, plateau airway pressure, positive end-expiratory pressure and the like are measured based on single-breath, so that it is important to accurately identify and segment each breath in the continuous ventilator waveform data, and the segmentation result directly influences the acquisition of the monitoring parameters during invasive mechanical ventilation.
In addition, the phenomenon of human-machine asynchrony (PVA) caused by mismatching of the patient demand and the assistance provided by the ventilator in amplitude or phase during mechanical ventilation can also cause harm to the patient, and accurate breath segmentation is helpful for identification and classification of the PVA, so that the accuracy of the PVA automatic identification algorithm is improved, and the possibility of giving auxiliary treatment decisions for different PVA types is provided.
During invasive mechanical ventilation treatment, a breathing machine mainly judges inspiration and expiration according to opening and closing of an inspiration valve and an expiration valve and data monitored by a flow sensor, a complete inspiration process and a complete respiration process are judged to be a complete breath, the start of an inspiration phase is defined as the start of the breath, and the end of an expiration phase (i.e. the start of the inspiration phase of the next breath) is defined as the end time of the breath. At present, no technology for realizing automatic segmentation of breath based on flow velocity waveform exists; for the analysis of off-line data, the respiratory segmentation method mainly comprises artificial segmentation and respiratory segmentation based on the periodic variation rule and characteristics of flow rate or airway pressure waveform. The above-mentioned method mainly has the following defects: 1. the algorithm and the monitoring system of the breathing machine can automatically split breathing, but the algorithms of different breathing machine manufacturers are different and not public, so that the automatic breathing splitting results of different brands of breathing machines are different and have no comparability, and in addition, the automatic splitting results are difficult to derive to bring difficulty for subsequent off-line analysis and data processing; 2. the manual segmentation method has the highest accuracy, but wastes time and labor, and massive respiration monitoring data generated beside a bed are difficult to segment one by one; 3. the segmentation of the breath based on the flow rate or airway pressure periodic variation rules and characteristics can be automated to a certain extent, but the accuracy is insufficient for the presence of waveform disturbances, especially PVA phenomena.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a respiration automatic segmentation method based on the flow velocity waveform, which can realize the automatic segmentation of respiration based on real-time or off-line flow velocity waveform, effectively ensure the accuracy of respiration segmentation, facilitate the identification and classification of PVA, and provide possibility for giving auxiliary treatment decisions aiming at different PVA types.
The application discloses automatic segmentation method of breathing based on velocity of flow waveform includes:
acquiring respiratory waveform data of a person to be measured;
preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero;
performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data;
and inputting the characteristic data of the person to be tested into a classification model to obtain a classification result of the characteristic data.
The respiratory waveform data comprises: respiratory flow rate data based on respiratory strike time;
the preprocessed respiratory waveform data comprises: searching a point with a flow velocity zero crossing point and a derivative larger than zero from the respiratory flow velocity waveform of the respiratory flow velocity data based on the respiratory dotting time, and respectively recording the time indexes of the point to obtain the preprocessed respiratory waveform data;
optionally, the preprocessing is to traverse the respiratory waveform data in sequence, at least once.
The preprocessed respiratory data having a plurality of respiratory cycles includes: respectively segmenting adjacent points to form a waveform corresponding to each breath to obtain breath data of a plurality of breath cycles;
optionally, the adjacent points of the nth and n +1 are respectively taken, and a single breathing cycle is defined between the points of the nth and n + 1; wherein N is more than or equal to 1 and less than N-1,N is the total number of the time indexes of all the points.
The characteristic data includes: the breath time interval of a single breath cycle, the flow rate change of the single breath cycle, the ratio of the expiration time of the single breath cycle to the total breath time of the single breath cycle, and the rising slope of the expiration waveform of the single breath cycle;
optionally, the breathing time interval of the single breathing cycle is: the time interval a between the time indexes respectively corresponding to the adjacent points;
optionally, the value range of a is 0 s-a-1s;
optionally, the flow rate variation of the single breathing cycle is: the flow rate change b between the time indexes respectively corresponding to the adjacent points;
optionally, the value range of b is 0L/min < b <20L/min;
optionally, the ratio of the expiration time of the single respiratory cycle to the total respiratory time of the single respiratory cycle is: the expiration time between the time indexes respectively corresponding to the adjacent points accounts for the proportion c% of the total respiration time between the time indexes respectively corresponding to the adjacent points;
optionally, the value range of c% is 20% -80%;
the rising slope of the expiratory waveform of the single respiratory cycle is: the rising slope d of the expiratory waveform between the time indexes respectively corresponding to the adjacent points; optionally, within the initial 100ms of inspiration, the value range of d is 10-50L/min/s.
The method or the step of inputting the feature data of the person to be measured into the classification model to obtain the classification result of the feature data of the person to be measured comprises the following steps:
inputting the breathing time interval of the single breathing cycle of the person to be tested into a classification model, and judging whether the breathing time interval of the single breathing cycle of the person to be tested falls into a range; in the case where the breathing time interval of said single breathing cycle of said subject falls within a range, outputting yes and entering a phase of inputting the variation of the flow rate of said single breathing cycle of said subject into a classification model; otherwise, the output is not, and the operation is terminated;
inputting the flow rate change of the single breathing cycle of the person to be tested into a classification model, and judging whether the flow rate change of the single breathing cycle of the person to be tested falls into a range b or not; if the change of the flow rate of the single respiratory cycle of the testee falls into the range b, outputting yes, and inputting the ratio of the expiration time of the single respiratory cycle of the testee to the total respiration time of the single respiratory cycle into a classification model; otherwise, the output is no, and the operation is terminated;
inputting the ratio of the expiration time of the single respiratory cycle of the person to be detected to the total respiratory time of the single respiratory cycle into a classification model, and judging whether the ratio of the expiration time of the single respiratory cycle of the person to be detected to the total respiratory time of the single respiratory cycle falls within a range c; under the condition that the ratio of the expiration time of the single respiration period of the testee to the total respiration time of the single respiration period falls into a range c, outputting yes, and entering a stage of ascending a slope of an expiration waveform of the single respiration period of the testee to a classification model; otherwise, the output is not, and the operation is terminated;
inputting the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected into a classification model, and judging whether the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls into a range d; if the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls within the range d, outputting yes and outputting a classification result of the respiratory waveform data of the person to be detected; otherwise, outputting no, and ending the operation.
The respiratory waveform data is respiratory waveform data having a continuous waveform signal.
An apparatus for automatic breath segmentation based on a flow rate waveform, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions that, when executed, perform the above-described flow-waveform-based breath autosegmentation method.
An analysis system for breath autosegmentation based on a flow rate waveform, comprising:
the acquisition unit is used for acquiring respiratory waveform data of a person to be measured;
the first processing unit is used for preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero;
the second processing unit is used for performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data;
and the classification unit is used for inputting the characteristic data of the person to be tested into a classification model to obtain a classification result of the characteristic data.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, implements the above-mentioned flow-waveform-based respiration autosegmentation method.
Any of the following applications:
the application of the device in man-machine disorder detection;
the application of the device in the extraction of the breathing parameters; optionally, after each breath is segmented based on the real-time or off-line flow rate waveform, the airway pressure, esophageal pressure and volume change of each breath can be used for extracting parameters with a plurality of characteristics for monitoring respiratory therapy.
The application has the following beneficial effects:
1. the application innovatively discloses a real-time or off-line flow velocity waveform-based automatic breath segmentation method, which effectively ensures the accuracy of single-mouth breath segmentation, standardizes breath segmentation results and has comparability; the method is beneficial to the identification and classification of the PVA, thereby improving the accuracy of the PVA automatic identification algorithm and providing possibility for giving auxiliary treatment decisions aiming at different PVA types;
2. the automatic breath segmentation method overcomes the defects that time and labor are wasted when manual segmentation is adopted, and the accuracy is insufficient when the breath segmentation is carried out by adopting rules based on the periodic change rule and the characteristics of flow velocity or airway pressure;
3. the method can automatically segment the respiration waveform into the respiration of a single mouth, is convenient for a doctor to quickly identify whether the respiration of the patient is normal or not, greatly shortens the diagnosis time of the doctor, and strives for valuable treatment time for an ICU patient; in addition, by referring to the segmentation rule, the automatic segmentation of the respiratory waveform can be realized by using a computer program, so that the identification and monitoring of the respiratory waveform abnormity can be conveniently carried out subsequently.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for automatic segmentation of respiration based on a flow rate waveform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for breath autosegmentation based on a flow waveform provided by an embodiment of the present invention;
FIG. 3 is a schematic flow diagram of an automatic breath segmentation system based on a flow rate waveform provided by an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a classification of a breath autosegging method based on a flow rate waveform according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the points where the flow rate crosses zero and the derivative is greater than zero according to the method for breath autosegmentation based on the flow rate waveform provided by the embodiments of the present invention;
FIG. 6 is a schematic diagram of time interval a of a method for breath autosegmentation based on a flow rate waveform according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of the flow rate variation b of the breath autosegmentation method based on the flow rate waveform provided by the embodiment of the invention;
fig. 8 is a schematic diagram of the rising slope d of the expiratory waveform of the breath auto-segmentation method based on the flow rate waveform according to the embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a respiratory auto-segmentation method based on a flow rate waveform according to an embodiment of the present invention, specifically, the method includes the following steps:
101: acquiring respiratory waveform data of a person to be measured;
in one embodiment, the respiratory waveform data comprises: respiratory flow rate data based on breath strike time; the respiration Flow rate data based on the respiration dotting Time is Flow rate waveform data with the abscissa of Time(s) and the ordinate of Flow (L/min); the breath dotting Time is Time(s), the Time is set according to the sampling frequency hz of the breathing machine and the serial number of the data, and the calculation formula is as follows: time = (number-1)/hz; number is the serial number of the sample point.
In one embodiment, the respiratory waveform data further comprises: airway pressure waveform data based on breath hit time; the airway pressure waveform data is airway pressure waveform data with the abscissa of Time and the ordinate of the airway pressure waveform data of the airway pressure data Paw (cmH 2O);
in one embodiment, the respiratory waveform data further comprises: esophageal pressure waveform data based on breath dotting time; the esophagus pressure waveform data is esophagus pressure waveform data with the abscissa being Time and the ordinate being esophagus pressure data Pes (cmH 2O);
in one embodiment, the respiratory waveform data further comprises: volume waveform data based on breath dotting time; the Volume waveform data is Volume waveform data with the abscissa of Time and the ordinate of Volume (ml); the volume data is calculated by the formula:
102: preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the division is to find all the points where the flow velocity crosses zero (one point is less than or equal to 0, and the other point is greater than or equal to 0) and the derivative is greater than zero, and the points are shown as circles in fig. 5;
in one embodiment, the preprocessed respiratory waveform data comprises: searching a point with a flow velocity zero crossing point and a derivative larger than zero from the respiratory flow velocity waveform of the respiratory flow velocity data based on the respiratory dotting time, and respectively recording the time indexes of the point to obtain the preprocessed respiratory waveform data; the preprocessing is to traverse the respiratory waveform data in sequence at least once.
In one embodiment, the pre-processed respiratory data having a plurality of respiratory cycles comprises: respectively segmenting adjacent points to form a waveform corresponding to each breath to obtain breath data of a plurality of breath cycles;
each breath is a single breath, and the definition of the single breath is as follows: one complete inspiration phase plus one complete expiration phase; the change of inspiratory Flow (Flow) from negative to positive is recorded as the beginning of a breath; the end of a breath is the previous sampling point at which the next breath begins.
Optionally, the adjacent points of the nth and n +1 are respectively taken, and a single breathing cycle is defined between the points of the nth and n + 1; wherein N is more than or equal to 1 and less than N-1,N is the total number of the time indexes of all the points.
103: performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data;
in one embodiment, the feature data comprises: the respiration time interval of a single respiration period, the flow rate change of the single respiration period, the ratio of the expiration time of the single respiration period to the total respiration time of the single respiration period, and the rising slope of the expiration waveform of the single respiration period;
optionally, the breathing time interval of the single breathing cycle is: the time interval a between the time indexes respectively corresponding to the adjacent points, i.e. the time interval between two adjacent circles, is shown by the arrow in fig. 6;
optionally, the value range of a is made of fewer than 0s and then a is made of fewer than 1s; the a is preferably made of 10 s-straw-cover a-straw-cover of 0.6s; the respiratory rate of normal persons is about 8-20 times/min, and the respiratory rate of patients can reach 30-60 times/min under pathological conditions, so the respiratory time a is in the range of 0 s-a-1s, and the extreme case that the respiratory rate is more than 100 times is extremely rare, so the preferred value range of a is 0 s-a-0.6s.
Optionally, the flow rate variation of the single breathing cycle is: the flow rate change b between the time indexes respectively corresponding to the adjacent points; as indicated by the arrows in fig. 7;
optionally, the value range of b is 0L/min < b <20L/min; b is preferably 0L/min < b <10L/min; the inspiratory flow rate of normal people is 40-60L/min, the flow rate of children is 5-10L/min, so the flow rate change b between breaths has the value range of 0L/min < b <20L/min, and considering that the inspiratory power of partial patients is insufficient, the preferable value range of b is 0L/min < b <10L/min.
Optionally, the ratio of the expiration time (time when the flow rate is negative) of the single respiratory cycle to the total respiratory time (time between adjacent 2 circles) of the single respiratory cycle is: the expiration time between the time indexes respectively corresponding to the adjacent points accounts for the proportion c% of the total respiration time between the time indexes respectively corresponding to the adjacent points;
optionally, the value range of c% is 20% -80%; c is preferably 30% -50%; according to the definition of double triggering, the expiration time between two inspiration periods is less than half of the average inspiration time, the value range of c is 20-80%, wherein the preferable value range of c is 30-50%. The arrangement can well avoid the influence of double or multiple triggering conditions on the result; the clinical tachypnea breaths, when breathing is abnormal, the breath is breathed for 2 times in a short time, and the accuracy of calculating single breath by the segmentation method is better; and a judgment is made in the middle, and when an abnormal condition occurs, the method refers to the effective assistance for how to process.
Optionally, the rising slope of the expiratory waveform of the single respiratory cycle is: the rising slope d of the expiratory waveform between the time indexes respectively corresponding to the adjacent points; as shown in the box of fig. 8
Optionally, within the initial 100ms of inspiration, the value range of d is 10-50L/min/s; d is preferably 5-25L/min/s; the flow trigger setting is about 1-5L/min, d ranges from about 10-50L/min/s within 100ms of the initial inspiration, and preferably ranges from 5-25L/min/s in view of the fractional trigger delay.
104: and inputting the characteristic data of the person to be measured into a classification model to obtain a classification result of the characteristic data.
In one embodiment, the method or step of inputting the feature data of the subject into a classification model to obtain the classification result of the feature data of the subject includes:
inputting the breathing time interval of the single breathing cycle of the person to be detected into a classification model, and judging whether the breathing time interval of the single breathing cycle of the person to be detected falls into a range; in the case where the breathing time interval of said single breathing cycle of said subject falls within a range, outputting yes and entering a phase of inputting the variation of the flow rate of said single breathing cycle of said subject into a classification model; otherwise, the output is not, and the operation is terminated; a in this step is denoted by 1 in fig. 4;
inputting the flow rate change of the single respiratory cycle of the person to be detected into a classification model, and judging whether the flow rate change of the single respiratory cycle of the person to be detected falls into a range b; if the change of the flow rate of the single respiratory cycle of the testee falls into the range b, outputting yes, and inputting the ratio of the expiration time of the single respiratory cycle of the testee to the total respiration time of the single respiratory cycle into a classification model; otherwise, the output is not, and the operation is terminated; b in this step is denoted 2 in FIG. 4;
inputting the ratio of the expiration time of the single respiration period of the person to be tested to the total respiration time of the single respiration period into a classification model, and judging whether the ratio of the expiration time of the single respiration period of the person to be tested to the total respiration time of the single respiration period falls within a range c; under the condition that the ratio of the expiration time of the single respiration period of the testee to the total respiration time of the single respiration period falls into a range c, outputting yes, and entering a stage of ascending a slope of an expiration waveform of the single respiration period of the testee to a classification model; otherwise, the output is not, and the operation is terminated; c in this step is denoted 3 in FIG. 4;
inputting the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected into a classification model, and judging whether the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls into a range d; if the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls within the range d, outputting yes and outputting a classification result of the respiratory waveform data of the person to be detected; otherwise, the output is no, and the operation is terminated. D in this step is denoted 4 in FIG. 4;
in one embodiment, the respiratory waveform data is respiratory waveform data having a continuous waveform signal.
The determination mode of the classification model comprises the following steps:
acquiring respiratory waveform data of normal people;
preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles;
performing feature selection or feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature selection or feature extraction as feature data;
and performing feature extraction on the feature data by adopting a machine learning method to obtain feature data after feature extraction, and constructing a classification model by using the feature data after feature extraction to obtain the constructed classification model.
FIG. 2 isThe embodiment of the invention provides a schematic diagram of a respiratory automatic segmentation device based on a flow velocity waveform, which comprises: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions that, when executed, perform the above-described flow-waveform-based breath autosegmentation method.
FIG. 3 isThe embodiment of the invention provides a schematic flow chart of a respiration automatic segmentation system based on a flow velocity waveform, which comprises the following steps:
an acquisition unit 301 for acquiring respiratory waveform data of a person to be measured;
the first processing unit 302 is configured to perform preprocessing on the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero;
a second processing unit 303, configured to perform feature extraction on the preprocessed respiratory data with multiple respiratory cycles, so as to obtain feature-extracted respiratory data with multiple respiratory cycles as feature data;
and the classification unit is used for inputting the characteristic data of the person to be tested into a classification model to obtain a classification result of the characteristic data.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, implements the above-mentioned flow-waveform-based respiration autosegmentation method.
The validation results of this validation example show that assigning an intrinsic weight to an indication can moderately improve the performance of the method relative to the default settings.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read Only Memory (ROM), random Access Memory (RAM), magnetic or optical disks, and the like.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by hardware that is instructed to implement by a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
While the invention has been described in detail with reference to specific embodiments thereof, it will be apparent to one skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. A respiration automatic segmentation method based on a flow velocity waveform comprises the following steps:
acquiring respiratory waveform data of a person to be measured;
preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero;
performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data;
and inputting the characteristic data of the person to be measured into a classification model to obtain a classification result of the characteristic data.
2. The method according to claim 1, wherein the respiratory waveform data comprises: respiratory flow rate data based on breath strike time;
the preprocessed respiratory waveform data comprises: searching a point with a flow velocity zero crossing point and a derivative larger than zero from the respiratory flow velocity waveform of the respiratory flow velocity data based on the respiratory dotting time, and respectively recording the time indexes of the point to obtain the preprocessed respiratory waveform data;
optionally, the preprocessing is to sequentially traverse the breathing waveform data at least once.
3. The method of claim 2, wherein the pre-processed respiration data having a plurality of respiration cycles comprises: respectively segmenting adjacent points to form a waveform corresponding to each breath to obtain breath data of a plurality of breath cycles;
optionally, the adjacent points of the nth and n +1 are respectively taken, and a single breathing cycle is defined between the points of the nth and n + 1; wherein N is more than or equal to 1 and less than N-1,N is the total number of the time indexes of all the points.
4. The method according to claim 1, wherein the characteristic data comprises: the breath time interval of a single breath cycle, the flow rate change of the single breath cycle, the ratio of the expiration time of the single breath cycle to the total breath time of the single breath cycle, and the rising slope of the expiration waveform of the single breath cycle;
optionally, the breathing time interval of the single breathing cycle is: the time interval a between the time indexes respectively corresponding to the adjacent points; the value range of a is preferably made of 10 s-a-1s;
optionally, the flow rate variation of the single breathing cycle is: the flow rate change b between the time indexes respectively corresponding to the adjacent points; the value range of b is preferably 0L/min < b <20L/min;
optionally, the ratio of the expiration time of the single respiratory cycle to the total respiratory time of the single respiratory cycle is: the expiration time between the time indexes respectively corresponding to the adjacent points accounts for the proportion c% of the total respiration time between the time indexes respectively corresponding to the adjacent points; the value range of c% is preferably 20% -80%.
5. The method according to claim 4, wherein the rising slope of the expiratory waveform of the single respiratory cycle is: the rising slope d of the expiratory waveform between the time indexes respectively corresponding to the adjacent points;
optionally, within the initial 100ms of inspiration, the value range of d is 10-50L/min/s.
6. The method for automatic segmentation of respiration based on flow velocity waveform according to claim 5, wherein the method or step of inputting the feature data of the person to be measured into a classification model to obtain the classification result of the feature data of the person to be measured comprises:
inputting the breathing time interval of the single breathing cycle of the person to be detected into a classification model, and judging whether the breathing time interval of the single breathing cycle of the person to be detected falls into a range; in the case where the breathing time interval of said single breathing cycle of said subject falls within a range, outputting yes and entering a phase of inputting the variation of the flow rate of said single breathing cycle of said subject into a classification model; otherwise, the output is no, and the operation is terminated;
inputting the flow rate change of the single breathing cycle of the person to be tested into a classification model, and judging whether the flow rate change of the single breathing cycle of the person to be tested falls into a range b or not; if the change of the flow rate of the single respiratory cycle of the testee falls into the range b, outputting yes, and inputting the ratio of the expiration time of the single respiratory cycle of the testee to the total respiration time of the single respiratory cycle into a classification model; otherwise, the output is no, and the operation is terminated;
inputting the ratio of the expiration time of the single respiratory cycle of the person to be detected to the total respiratory time of the single respiratory cycle into a classification model, and judging whether the ratio of the expiration time of the single respiratory cycle of the person to be detected to the total respiratory time of the single respiratory cycle falls within a range c; under the condition that the ratio of the expiration time of the single respiration period of the testee to the total respiration time of the single respiration period falls into a range c, outputting yes, and entering a stage of ascending a slope of an expiration waveform of the single respiration period of the testee to a classification model; otherwise, the output is not, and the operation is terminated;
inputting the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected into a classification model, and judging whether the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls into a range d; if the rising slope of the expiratory waveform of the single respiratory cycle of the person to be detected falls within the range d, outputting yes and outputting a classification result of the respiratory waveform data of the person to be detected; otherwise, outputting no, and ending the operation.
7. The method according to claim 1, wherein the respiratory waveform data is respiratory waveform data having a continuous waveform signal.
8. An apparatus for automatic breath segmentation based on a flow rate waveform, the apparatus comprising: a memory and a processor;
the memory is to store program instructions;
the processor is configured to invoke program instructions that, when executed, perform the method for breath autosegmentation based on flow rate waveforms of any of claims 1 to 7.
9. An automatic breath segmentation system based on a flow rate waveform, comprising:
the acquisition unit is used for acquiring respiratory waveform data of a person to be measured;
the first processing unit is used for preprocessing the respiratory waveform data to obtain preprocessed respiratory waveform data; cutting the preprocessed respiratory waveform data into respiratory data with a plurality of respiratory cycles to obtain preprocessed respiratory data with a plurality of respiratory cycles; the cutting is to find all the flow velocity zero crossing points and the point of which the derivative is greater than zero;
the second processing unit is used for performing feature extraction on the preprocessed respiratory data with a plurality of respiratory cycles to obtain the respiratory data with a plurality of respiratory cycles after feature extraction as feature data; and the classification unit is used for inputting the characteristic data of the person to be tested into a classification model to obtain a classification result of the characteristic data.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for flow-waveform-based automatic breath segmentation according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211056849.1A CN115414026A (en) | 2022-08-31 | 2022-08-31 | Automatic breath segmentation method and system based on flow velocity waveform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211056849.1A CN115414026A (en) | 2022-08-31 | 2022-08-31 | Automatic breath segmentation method and system based on flow velocity waveform |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115414026A true CN115414026A (en) | 2022-12-02 |
Family
ID=84201038
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211056849.1A Pending CN115414026A (en) | 2022-08-31 | 2022-08-31 | Automatic breath segmentation method and system based on flow velocity waveform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115414026A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116226231A (en) * | 2023-02-23 | 2023-06-06 | 北京思维实创科技有限公司 | Data segmentation method and related device |
-
2022
- 2022-08-31 CN CN202211056849.1A patent/CN115414026A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116226231A (en) * | 2023-02-23 | 2023-06-06 | 北京思维实创科技有限公司 | Data segmentation method and related device |
| CN116226231B (en) * | 2023-02-23 | 2023-10-27 | 北京思维实创科技有限公司 | Data segmentation method and related device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220265944A1 (en) | Detection of asynchrony | |
| EP2438855B1 (en) | Waveform interpretation for respiratory analysis | |
| CN109893732B (en) | Mechanical ventilation man-machine asynchrony detection method based on recurrent neural network | |
| CN104757975B (en) | Tidal breathing pattern is distinguished using oxygen saturation signal | |
| CN102172328B (en) | Method for detecting and disciminatng breathing patterns from respiratory signals | |
| CN102770070B (en) | For the system and method for real-time assessment mechanics of lung | |
| JP6960913B2 (en) | Anomaly detectors and methods for estimating respiratory dynamics parameters | |
| RU2621393C2 (en) | Automated spirogram analysis and interpretation | |
| US20140007878A1 (en) | Detection of ventilation sufficiency | |
| EP1765442A2 (en) | METHOD AND APPARATUS FOR NON-INVASIVE PREDICTION OF INTRINSIC POSITIVE END-EXPIRATORY PRESSURE (PEEPi) IN PATIENTS RECEIVING VENTILATOR SUPPORT | |
| WO2011070462A2 (en) | Automated annotation of clinical data | |
| KR20140077150A (en) | Event sequencing using acoustic respiratory markers and methods | |
| CN111563451A (en) | Mechanical ventilation ineffective inspiration effort identification method based on multi-scale wavelet features | |
| CN115414026A (en) | Automatic breath segmentation method and system based on flow velocity waveform | |
| CN111839494A (en) | Heart rate monitoring method and system | |
| CN111227792B (en) | Apnea detection method and system, electronic device and storage medium | |
| DE102015009056A1 (en) | Respirator and procedure for a ventilator | |
| CN115381432A (en) | Single-mouth respiration waveform-based man-machine disorder detection method and system | |
| CN113941061B (en) | Man-machine asynchronous identification method, system, terminal and storage medium | |
| CN115381431A (en) | Method and system for extracting respiratory parameters | |
| CN108064178A (en) | Dynamic overinflation is identified using expiratory gas flow combination breathing carbon dioxide signal | |
| CN113808078B (en) | Method for identifying ineffective inhalation effort based on electrical impedance tomography | |
| WO2022133942A1 (en) | Medical ventilation device and ventilation monitoring method | |
| JP2023540901A (en) | Estimation of cardiogenic artifacts in ventilator airway pressure and flow for automatic detection and resolution of patient-ventilator asynchrony | |
| CN116077021A (en) | Sleep brain electricity, snore detection and analysis device |
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 |