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WO2025160337A1 - Détection d'obstruction de flux d'air à l'aide d'un modèle d'apprentissage automatique - Google Patents

Détection d'obstruction de flux d'air à l'aide d'un modèle d'apprentissage automatique

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Publication number
WO2025160337A1
WO2025160337A1 PCT/US2025/012862 US2025012862W WO2025160337A1 WO 2025160337 A1 WO2025160337 A1 WO 2025160337A1 US 2025012862 W US2025012862 W US 2025012862W WO 2025160337 A1 WO2025160337 A1 WO 2025160337A1
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WO
WIPO (PCT)
Prior art keywords
breathing
airflow
flow
application
flow curve
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
Application number
PCT/US2025/012862
Other languages
English (en)
Inventor
Surya P. BHATT
Sandeep BODDULURI
Arie NAKHMANI
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UAB Research Foundation
Original Assignee
UAB Research Foundation
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Filing date
Publication date
Application filed by UAB Research Foundation filed Critical UAB Research Foundation
Publication of WO2025160337A1 publication Critical patent/WO2025160337A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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—Measuring devices for evaluating the respiratory organs
    • A61B5/087—Measuring breath flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/091—Measuring volume of inspired or expired gases, e.g. to determine lung capacity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • 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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • 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
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • 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
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0015—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network characterised by features of the telemetry system
    • A61B5/0022—Monitoring a patient using a global network, e.g. telephone networks, internet
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Measuring devices for evaluating the respiratory organs
    • A61B5/097—Devices for facilitating collection of breath or for directing breath into or through measuring devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Definitions

  • Airflow obstruction is detected using spirometry.
  • patients are instructed to forcefully breathe out after a maximal inhalation into a spirometer tube as hard as they can and for as long as they can. Multiple efforts are needed to ensure that the efforts were consistently maximal.
  • Performing spirometry with forced exhalation maneuver is quite difficult for patients with respiratory diseases and is timeconsuming.
  • spirometry tests are typically not performed by the patient at home because a successful test often requires a significant amount of coaching or assistance from a medical professional.
  • Embodiments of the present disclosure are related to detection of airflow obstruction in patients with the use of spirometry and without assistance by a doctor or other medical professionals.
  • a system for measuring respiration comprising a spirometer configured to obtain regular breathing data from a patient; at least one computing device executing an application, the application, when executed, causing the at least one computing device to at least: generate a flow curve and volume curve based upon breathing data of the patient.
  • the flow curve and volume curve comprises a characterization of air flow during breathing of the patient over a specified time period.
  • the flow curve and/or the volume curves can be classified as one of normal breathing or airflow obstructed breathing based upon at least one machine learning model trained using a training data set.
  • the training data set comprises a plurality of training flow curves and volume curves.
  • the plurality of training flow curves and volume curves are respectively classified as normal breathing and/or airflow obstructed breathing.
  • the training flow curves and volume curves are a time series of flow and volume data.
  • the application can generate an alert in response to classifying the flow and/or volume curve at airflow obstructed breathing.
  • the application classifies the flow curve and/or volume curve based at least in part upon a machine learning model that is trained using the training data set. Both flow curves and/or volume curves can be used to classify normal breathing and air-obstruction breathing.
  • the training flow curves and volume curves are a time series of flow and volume data.
  • the machine learning model utilizes one-dimensional (1 D) time series classification tasks to detect the presence of airflow obstruction.
  • the application can further cause the at least one computing device to classify the flow curve as more airflow obstructed or less airflow obstructed than a previous flow curve and/or previous volume curve associated with the patient.
  • the machine learning model uses multi-dimensional time series classification tasks when flow and volume curves are used as input.
  • Feature extraction process can compute various features from each of these sequences to train or build a classifier.
  • a method comprising the steps of capturing, by a sensor of a spirometer, breathing data from a patient; generating, by a computing device of the spirometer, a flow curve and/or volume curve based at least in part on the breathing data of the patient.
  • the flow curve and/or volume curve comprise a characterization of air flow during breathing of the patient over a specified time period.
  • the flow curve and/or volume curve can be classified as one of normal breathing or airflow obstructed breathing based upon at least one machine learning model trained using a training data set.
  • the training data set comprises a plurality of training flow curves and/or volume curves.
  • the training flow curves and/or volume curves are respectively classified as normal breathing or airflow obstructed breathing.
  • the application generates an alert in response to classifying the flow curve and/or volume curve at airflow obstructed breathing.
  • the application classifies the flow curve and/or volume curve based at least in part upon a machine learning model that is trained using the training data set.
  • the machine learning model utilizes one-dimensional (1 D) time series classification tasks to detect the presence of airflow obstruction.
  • the machine learning model uses multidimensional time series classification tasks when flow and volume curves are used as input. Feature extraction process can compute various features from each of these sequences to train or build a classifier.
  • the application further causes the at least one computing device to classify the flow curve and/or volume curve as more airflow obstructed or less airflow obstructed than a previous flow curve and/or volume curve associated with the patient.
  • the method involves the application obtaining a confirmation of a classification of the flow curve and/or volume curve as one of normal breathing or airflow obstructed breathing. In response to obtaining the confirmation, the application adds the flow curve and/or volume curve with the classification to the training data set.
  • a spirometer comprising a sensor configured to measure a respiration of a patient breathing through a mouthpiece; a computing device comprising a processor and memory; an application, when executed by the processor, causes the computing device to at least capture breathing data of a patient using the sensor; determine a flow curve and/or a volume curve of the regular breathing data.
  • the flow curve and/or volume curve comprise a characterization of air flow during breathing of the patient over a specified time period; and determine a breathing classification for the flow curve and/or volume curve based at least in part on a machine learning model, the breathing classification being a normal breathing or airflow obstructed breathing.
  • the application when executed by the processor, causes the computing device to at least: render the breathing classification in a display of the spirometer.
  • the machine learning model can be stored in the memory in a serialized format.
  • FIG. 1 illustrates an example of flow curves in accordance with various embodiments of the present disclosure.
  • FIG. 2 illustrates an example networked environment in accordance with various embodiments of the present disclosure.
  • FIG. 3 is a flowchart illustrating an example of a method according to examples of the present disclosure.
  • FIG. 4 is a confusion matrix illustrating one example of experimental results according to examples of the present disclosure.
  • Airflow obstruction can be detected to facilitate diagnoses of chronic obstructive pulmonary disease (COPD) and other respiratory conditions that can be detected based upon an analysis of a patient’s breathing.
  • COPD chronic obstructive pulmonary disease
  • One standard for diagnosing COPD is the demonstration of a low ratio of the forced expiratory volume in 1 second to the forced vital capacity (FEVi/FVC), defined using the lower limit of normal (LLN), which is the 5th percentile of a normal population.
  • LLC lower limit of normal
  • Examples of the disclosure involve a method for quantifying lung disease such as COPD based upon an analysis of a specific time of period of tidal or regular breathing of the patient.
  • the specific time of period can be in a range from 10 to 120 seconds.
  • the analysis of the patient’s breathing can be based upon an analysis of regular breaths from a particular time period, such as a from 10 to 120 seconds time period or other suitable time periods.
  • the underlying premise is that even on regular breathing, individuals with airflow obstruction can have differences in their breathing patterns in the expiratory phase such that they can be differentiated from normal controls.
  • FIG. 1 illustrates example flow curves and volume curves that can be recorded by a device according to examples of the disclosure.
  • the illustrative flow curves can be captured by asking a patient to breathe regularly into a spirometer device for a period of time such as 10-120 seconds or other suitable time periods.
  • the flow curves and/or volume curves can be analyzed by a machine learning process.
  • a logistic regression analysis can be utilized to identify flow curves and/or volume curve that are indicative of an airflow obstruction in the patient.
  • boosting, regression analysis, a support vector machine algorithm, gradient boosting decision trees, 1 D convolutional neural networks, transformer-based architecture, or any other machine learning analysis can be utilized to identify airflow obstruction in the patient.
  • flow curves and/or volume curves that are marked red can be identified by examples of the disclosure as indicative of airflow obstruction and potentially indicative of COPD.
  • Flow curves and/or volume curves that are marked green can be identified as normal breathing patterns, or breathing patterns that are not indicative of COPD.
  • the machine learning process can flag uncertain flow curves and/or volume curves for follow-up by a doctor or other medical professional.
  • a machine learning process can utilize one-dimensional (1 D) time series classification tasks to detect the presence of airflow obstruction.
  • the machine learning model uses multidimensional time series classification tasks when flow and volume curves are used as input.
  • Feature extraction process can compute various features from each of these sequences to train or build a classifier.
  • a two-dimensional plot of flow or volume vs time can be utilized as an input into the machine learning process.
  • the machine learning process can be trained on data sets that include airflow-obstructed flow curves (and/or volume curves) and normal breathing flow curves (and/or volume curves).
  • FIG. 2 shown is an example implementation according to embodiments of the disclosure.
  • the network environment 200 can include a computing environment 203, and a spirometer 100, which can be in data communication with each other via a network 206.
  • the network 206 can include wide area networks (WANs), local area networks (LANs), personal area networks (PANs), or a combination thereof. These networks can include wired or wireless components or a combination thereof. Wired networks can include Ethernet networks, cable networks, fiber optic networks, and telephone networks such as dial-up, digital subscriber line (DSL), and integrated services digital network (ISDN) networks. Wireless networks can include cellular networks, satellite networks, Institute of Electrical and Electronic Engineers (IEEE) 802.11 wireless networks (/.e., WI-FI®), BLUETOOTH® networks, microwave transmission networks, as well as other networks relying on radio broadcasts. The network 206 can also include a combination of two or more networks 206. Examples of networks 206 can include the Internet, intranets, extranets, virtual private networks (VPNs), and similar networks.
  • VPNs virtual private networks
  • the spirometer 100 can comprise a device that measures the volume of air inspired and expired by a patient’s lungs.
  • the spirometer 100 can obtain breathing data from a patient over a period of time, such as for 10-120 seconds, two or more minutes, and provide a flow pattern characterizing the breathing of the patent over a specified period of time.
  • the breathing data can comprise flow or volume air inspired and expired by the patient over a time period.
  • the spirometer 100 can include a mouthpiece, a sensor 103, a controller 106, a display 109, a network interface 112, and other suitable components.
  • the mouthpiece can be an apparatus location for the patient to position their around .
  • the mouthpiece can receive an inhale or exhale of breath from the patient.
  • the sensor 103 can represent one or more sensors that are used to measure respiratory parameters for a patient breathing into the apparatus.
  • the sensor 103 can include a flow sensor, a pressure sensor, and other suitable sensors.
  • a pressure sensor e.g., a differential pressure sensor
  • an electrical signal e.g., an analog signal, a digital signal, etc.
  • the controller 106 can represent a computing device, a processor, a microcontroller, and other suitable processing devices.
  • the controller 106 can be used to execute one or more applications for controlling the operations of the spirometer 100, such as initiating the measurement user data (e.g., patient data), communicating with the computing environment 203, determining air flow obstruction diagnoses, displaying data (e.g., air flow obstruction diagnoses, instructions for improved measurements, etc ), and other suitable functionality.
  • the display 109 such as liquid crystal displays (LCDs), gas plasma-based flat panel displays, organic light emitting diode (OLED) displays, electrophoretic ink (“E-ink”) displays, foldable OLED displays, or other types of display devices.
  • the display 109 can be a component of the spirometer 100 or can be connected to the spirometer 100 through a wired or wireless connection.
  • the network interface 112 can enable the spirometer 100 provide the flow pattern via the network 206 to the computing environment 203 for airflow obstruction analysis as is described herein.
  • the network interface 112 can be data communication transceiver that communicates according to one or more wired or wireless communication protocols.
  • the spirometer 100 can be connected to a computing device so that an application executed by the computing device can provide a flow pattern of a patient to the computing environment 203 via the network 206.
  • the spirometer 100 can be configured to execute various applications such as a device application 115 or other applications.
  • the device application 115 can be executed in the spirometer 100 to access network content served up by the computing environment 203 or other servers, thereby rendering a user interface on the display 109.
  • the device application 1 15 can include a browser, a dedicated application, or other executable, and the user interface can include a network page, an application screen, or other user mechanism for obtaining user input.
  • the spirometer 100 can be configured to execute applications beyond the device application 115 such as browser applications, social networking applications, health- related applications, or other applications.
  • the computing environment 203 can include one or more computing devices that include a processor, a memory, and/or a network interface.
  • the computing devices can be configured to perform computations on behalf of other computing devices or applications.
  • such computing devices can host and/or provide content to other computing devices in response to requests for content.
  • such computing devices can be a central computing device installed within a vehicle.
  • the computing environment 203 can employ a plurality of computing devices that can be arranged in one or more server banks or computer banks or other arrangements. Such computing devices can be located in a single installation or can be distributed among many different geographical locations.
  • the computing environment 203 can include a plurality of computing devices that together can include a hosted computing resource, a grid computing resource or any other distributed computing arrangement.
  • the computing environment 203 can correspond to an elastic computing resource where the allotted capacity of processing, network, storage, or other computing-related resources can vary over time.
  • the components executed on the computing environment 203 include an airflow obstruction detection application 209, and other applications, services, processes, systems, engines, or functionality not discussed in detail herein.
  • the airflow obstruction detection application 209 can be executed to perform various actions.
  • the airflow obstruction detection application 209 can detect airflow obstruction or potential COPD conditions of a patient based upon an analysis of flow curves (and/or volume curves provided by a spirometer 100 associated with the patient.
  • the airflow obstruction detection application 209 can utilize a machine learning algorithm that is trained using a training dataset comprising training flow patterns from healthy patients and patients with airflow obstruction.
  • the airflow obstruction detection application 209 can alert the patient, a doctor, or other medical professionals.
  • the patient can be referred for further analysis or treatment for the possible airflow obstruction.
  • the airflow obstruction detection application 209 can preprocess breathing data from the spirometer 100 to generate flow curves and/or volume curves for analysis.
  • the airflow obstruction detection application 209 can obtain breathing data from the spirometer 100 of a patient and utilize flow-time information to recreate the curves for analyses.
  • the original flow signal can be detrended and scaled.
  • a straight line can be fitted to the original signal (to find a drift) and subtracted from it (to detrend it). Then the mean of the signal can be subtracted, and the result is divided by the range of recorded values (max-min).
  • Other preprocessing pipelines can also be utilized to obtain and process breathing data from a spirometer 100 associated with a patient to generate flow curves and/or volume curves characterizing the breathing data.
  • the airflow obstruction detection application 209 can utilize a machine learning process that employs one-dimensional (1 D) time series classification tasks to detect the presence of airflow obstruction.
  • the machine learning process can be trained on data sets that include airflow-obstructed flow curves (and/or volume curves) and normal breathing flow curves (and/or volume curves).
  • a convolutional neural network (CNN) or a transformer can be generated and utilized by the airflow obstruction detection application 209 to classify flow curves and/or volume curves obtained from a spirometer 100 as obstructed or normal.
  • the neural network can be trained using a training data set as described herein.
  • the data store 212 can be representative of a plurality of data stores 212, which can include relational databases or non-relational databases such as object-oriented databases, hierarchical databases, hash tables, or similar keyvalue data stores, as well as other data storage applications or data structures. Moreover, combinations of these databases, data storage applications, and/or data structures may be used together to provide a single, logical, data store.
  • the data stored in the data store 212 is associated with the operation of the various applications or functional entities described below. This data can include training data 215, user data 217, and potentially other data.
  • the training data 215 represents data with which a machine learning process utilized by the airflow obstruction detection application 209 can be trained.
  • the training data 215 can comprise training flow curves 224 (and/or volume curves) that represent prior flow curves (and/or volume curves) btained from other patients.
  • the training flow curves 224 can be anonymized and tagged as obstructed or normal. With a sufficiently large set of training data 215, the airflow obstruction detection application 209 can be trained to identify normal and obstructed flow curves.
  • the data store 212 can also comprise user data 217.
  • User data 217 can represent data obtained from a spirometer 100 for analysis by the airflow obstruction detection application 209.
  • the user data 217 can comprise flow curves 227 (and/or volume curves).
  • the flow curves 227 can be generated by the airflow obstruction detection application 209 according to a pre-processing pipeline as described above.
  • the flow curves 227 can be generated from the breathing data obtained from one or more spirometers 100.
  • the spirometer 100 can pre-process breathing data from the patient to generate flow curves 227 for analysis by the airflow obstruction detection application 209.
  • the airflow obstruction detection application 209 can store a particular flow curve 227 (and/or volume curve) as a training flow curve 224 to further train and refine the neural network or other machine learning algorithm that identifies normal or airflow obstructed training flow curves 224.
  • the characterization of the particular flow curve 227 can be confirmed by a doctor or other user before being used as a training flow curve 224.
  • the airflow obstruction detection application 209 can also store the training flow curves 224 (and/or volume curves) of a user for disease monitoring and disease progression tracking purposes. For example, the airflow obstruction detection application 209 can analyze a particular flow curve 224 relative to a previously obtained flow curve 224 of a user already identified as having an airflow obstruction to provide an indication of how the airflow obstruction is improving or worsening.
  • FIG. 3 shown is a flowchart illustrating an example of how the airflow obstruction detection application 209 can operate according to various examples of the disclosure.
  • the flowchart of FIG. 3 can illustrate a method according to one example of the disclosure.
  • the automated time series feature extraction is a process of transforming raw data into numerical features which can be efficiently processed by a machine learning algorithm and while preserving the information in the raw data.
  • the automated time series feature extract can generate a more informative dataset that can be used for classification.
  • the feature extract dataset can be passed on to a model training stage wherein a machine learning algorithm can be executed to generate a machine learning model.
  • the machine learning algorithm can be executed to learn patterns and make predictions based on one or more targeted variables for the generation of the machine learning model.
  • the machine learning model can include one or more equations or algorithms learned from the feature extract dataset, selected parameters (e.g., model parameters, hyperparameters).
  • the airflow obstruction detection application 209 can proceed in parallel or in series to steps 305 and 306.
  • step 309 the neural network or other machine learning model utilized by the airflow obstruction detection application 209 can be further trained based upon the flow curve 227 obtained at step 301 and classification of the flow curve 227 performed at step 303.
  • the flow curves 227 obtained from spirometers 100 associated with patients, once classified by the airflow obstruction detection application 209 and/or verified by a doctor or other user, can be added to training flow curves 224 and used to further train the model utilized by the airflow obstruction detection application 209 to improve the accuracy of the model in classifying normal breathing patterns and airflow obstructed breathing patterns.
  • FIG. 4 shown is a chart illustrating experimental results according to one embodiment of the disclosure.
  • FIG. 5 shown is a flowchart that provides one example of the operation of a portion of the device application 115.
  • the flowchart of FIG. 5 provides merely an example of the many different types of functional arrangements that can be employed to implement the operation of the depicted portion of the device application 115.
  • the flowchart of FIG. 5 can be viewed as depicting an example of elements of a method implemented within the network environment 200.
  • the spirometer 100 can be store a trained machine learning model from the Airflow Obstruction Detection application 209.
  • the device application 115 can execute or use the trained machine learning model to determine or generate breathing classification (e.g. , a classification of a flow curve and/or volume curve from the patient).
  • the device application 1 15 can capture breathing data by cause the measuring of a breath of a patient.
  • the patient can have their mouth on the mouthpiece of the spirometer 100.
  • the patient can beath into the mouthpiece for a predetermined amount of time.
  • the measurements of the breathing can be captured as breathing data.
  • the device application 115 can identify an error with the breathing data captured from the patients. For example, the device application 115 can identify an error type based at least in part on the breathing data and device application 115 can determine a recommended patient instruction based at least in part on the error type. For example, the device application 115 can identify an error with incomplete inhalation based at least in part on the breathing data, a flow curve (and/or volume curve), and/or a machine learning model trained to identify error with breathing data.
  • the device application 115 can display a recommended patient instruction for filling the patient’s lungs by taking a deeper breath in a user interface via a display 109.
  • Other errors that can be identified can include a hesitation in blowing into the mouthpiece, a poor initial breathing blast, a cough during testing, and other suitable errors.
  • the device application 115 can display a recommended instruction for fixing the error and a prompt to initiate breathing again into the mouthpiece for capturing additional breathing data.
  • the device application 115 can determine a flow curve and/or volume curves based at least in part on the breathing data.
  • the breathing data can comprise the measured volume of air the patient exhales over time during a forced breath.
  • the device application 1 15 can by measuring the volume of air the patient exhales over time during the forced breath.
  • the device application 115 can essentially plot the rate of airflow (flow) against the total volume of air exhaled (volume), which can create a visual representation of how quickly air is expelled from the lungs.
  • the patient can be instructed to take a deep breath in and then forcefully exhale as much air as possible into the mouthpiece, while one or more sensors 103 of the spirometer 100 measures or records the changing volume and calculates the corresponding flow rate at each point in time, producing a characteristic curve on a display.
  • the flow curve (and/or a volume curve) can be a graphical representation of the airflow during a breath of a patient, showing how the rate of air flow changes over time during inhalation and exhalation.
  • the flow curve can provide a visual depiction of the patient's breathing mechanics and can be used to identify potential respiratory issues.
  • the device application 115 can identify an error with the flow curve (and/or volume curve) determine from the patient. For example, the device application 115 can identify an error type based at least in part on the flow curve and device application 115 can determine a recommended patient instruction based at least in part on the error type. For example, the device application 115 can identify an error with incomplete inhalation based at least in part on the breathing data, a flow curve, and/or a machine learning model trained to identify error with breathing data.
  • the device application 115 can display a recommended patient instruction for filling the patient’s lungs by taking a deeper breath.
  • Other errors that can be identified can include a hesitation in blowing into the mouthpiece, a poor initial breathing blast, a cough during testing, and other suitable errors.
  • the device application 115 can display a recommended instruction for fixing the error and a prompt to initiate breathing again into the mouthpiece for capturing additional breathing data.
  • the device application 115 can determine a breathing classification based at least in part on the flow curve (and/or volume curve) .
  • the device application 115 can execute a trained machine learning model for classifying flow curves and/or breathing data.
  • the device application 115 can input the flow curve, breathing data, patient data, and/or other suitable machine learning parameters.
  • the machining learning model can be executed like a software function or an executable file. After generating the breathing classification, the machine learning model can return the breathing classification to the device application 1 15.
  • the device application 1 15 can display the breathing classification to a display 109 associated with the spirometer via a user interface. Additionally, the device application 115 can transmit the breathing classification and associated data (e.g., breathing data, flow curves, volume curves, etc.) to the computing environment 203 (e.g., air flow obstruction detection application 209) for storage in the user data 217 (e.g., a user profile, a user account, etc.). In some examples, the air flow obstruction detection application 209 can verify the breathing classification because the air flow obstruction detection application 209 may have more accurate machine learning models. Then, the device application 115 can proceed to the end.
  • the computing environment 203 e.g., air flow obstruction detection application 209
  • the air flow obstruction detection application 209 can verify the breathing classification because the air flow obstruction detection application 209 may have more accurate machine learning models.
  • executable means a program file that is in a form that can ultimately be run by the processor.
  • executable programs can be a compiled program that can be translated into machine code in a format that can be loaded into a random-access portion of the memory and run by the processor, source code that can be expressed in proper format such as object code that is capable of being loaded into a random-access portion of the memory and executed by the processor, or source code that can be interpreted by another executable program to generate instructions in a random-access portion of the memory to be executed by the processor.
  • An executable program can be stored in any portion or component of the memory, including random-access memory (RAM), read-only memory (ROM), hard drive, solid-state drive, Universal Serial Bus (USB) flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components.
  • RAM random-access memory
  • ROM read-only memory
  • USB Universal Serial Bus
  • CD compact disc
  • DVD digital versatile disc
  • floppy disk magnetic tape, or other memory components.
  • the memory includes both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the memory can include random-access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, or other memory components, or a combination of any two or more of these memory components.
  • the RAM can include static random-access memory (SRAM), dynamic random-access memory (DRAM), or magnetic random-access memory (MRAM) and other such devices.
  • the ROM can include a programmable read-only memory (PROM), an erasable programmable readonly memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device.
  • each block can represent a module, segment, or portion of code that includes program instructions to implement the specified logical function(s).
  • the program instructions can be embodied in the form of source code that includes human- readable statements written in a programming language or machine code that includes numerical instructions recognizable by a suitable execution system such as a processor in a computer system.
  • the machine code can be converted from the source code through various processes. For example, the machine code can be generated from the source code with a compiler prior to execution of the corresponding application. As another example, the machine code can be generated from the source code concurrently with execution with an interpreter. Other approaches can also be used.
  • each block can represent a circuit or a number of interconnected circuits to implement the specified logical function or functions.
  • the flowchart shows a specific order of execution, it is understood that the order of execution can differ from that which is depicted. For example, the order of execution of two or more blocks can be scrambled relative to the order shown. Also, two or more blocks shown in succession can be executed concurrently or with partial concurrence. Further, in some embodiments, one or more of the blocks shown in the flowchart can be skipped or omitted. In addition, any number of counters, state variables, warning semaphores, or messages might be added to the logical flow described herein, for purposes of enhanced utility, accounting, performance measurement, or providing troubleshooting aids, etc. It is understood that all such variations are within the scope of the present disclosure.
  • any logic or application described herein that includes software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as a processor in a computer system or other system.
  • the logic can include statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system.
  • a "computer-readable medium" can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system.
  • a collection of distributed computer- readable media located across a plurality of computing devices may also be collectively considered as a single non-transitory computer-readable medium.
  • the computer-readable medium can include any one of many physical media such as magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium can be a random-access memory (RAM) including static random-access memory (SRAM) and dynamic random-access memory (DRAM), or magnetic random-access memory (MRAM).
  • RAM random-access memory
  • SRAM static random-access memory
  • DRAM dynamic random-access memory
  • MRAM magnetic random-access memory
  • the computer-readable medium can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device.
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • any logic or application described herein can be implemented and structured in a variety of ways.
  • one or more applications described can be implemented as modules or components of a single application.
  • one or more applications described herein can be executed in shared or separate computing devices or a combination thereof.
  • a plurality of the applications described herein can execute in the same computing device, or in multiple computing devices in the same computing environment 203.
  • Disjunctive language such as the phrase “at least one of X, Y, orZ,” unless specifically stated otherwise, is understood with the context as used in general to present that an item, term, etc., can be either X, Y, or Z, or any combination thereof (e.g., X; Y; Z; X or Y; X or Z; Y or Z; X, Y, or Z; etc.).
  • X Y
  • Z X or Y
  • Y or Z X or Z
  • ratios, concentrations, amounts, and other numerical data may be expressed herein in a range format. It is to be understood that such a range format is used for convenience and brevity, and thus, should be interpreted in a flexible manner to include not only the numerical values explicitly recited as the limits of the range, but also to include all the individual numerical values or sub-ranges encompassed within that range as if each numerical value and sub-range is explicitly recited.
  • a concentration range of “about 0.1 % to about 5%” should be interpreted to include not only the explicitly recited concentration of about 0.1 % to about 5 %, but also include individual concentrations (e.g., 1 %, 2%, 3%, and 4%) and the sub-ranges (e.g., 0.5%, 1.1 %, 2.2%, 3.3%, and 4.4%) within the indicated range.
  • the term “about” can include traditional rounding according to significant figures of numerical values.
  • the phrase “about ‘x’ to ‘y’” includes “about ‘x’ to about . y , radical
  • a system for measuring respiration comprising: a spirometer configured to obtain breathing data from a patient; at least one computing device executing an application, the application, when executed, causing the at least one computing device to at least: generate a flow curve (and/or a volume curve) based upon breathing data of the patient, the flow curve comprising a characterization of the normal breathing of the patient over a specified time period; and classify the flow curve as one of normal breathing or airflow obstructed breathing based upon at least one machine learning model trained using a training data set.
  • Clause 2 The system of clause 1 , wherein the training data set comprises a plurality of training flow curves, the plurality of training flow curves comprising flow or volume curves that are respectively classified as normal breathing or airflow obstructed breathing.
  • Clause 3 The system of clause 1 or 2, wherein the application generates an alert in response to classifying the flow curve at airflow obstructed breathing.
  • Clause 4 The system of any of clauses 1-3, wherein the application classifies the flow curve based at least in part upon a machine learning model that is trained using the training data set.
  • Clause 5 The system of any of clauses 1-4, wherein the machine learning model utilizes one-dimensional (1 D) time series classification tasks to detect the presence of airflow obstruction.
  • Clause 6 The system of any of clauses 1 -5, wherein the application further causes the at least one computing device to classify the flow curve as more airflow obstructed or less airflow obstructed than a previous flow curve associated with the patient.
  • Clause 7 The system of any of clauses 1 -6, wherein the application obtains a confirmation of a classification of the flow curve as one of normal breathing or airflow obstructed breathing.
  • Clause 8 The system of clause 7, wherein in response to obtaining the confirmation, the application adds the flow curve with the classification to the training data set.
  • Clause 9 - A method comprising: capturing, by a sensor of a spirometer, breathing data from a patient; generating, by a computing device of the spirometer, a flow curve (and/or volume curve) based at least in part on the breathing data of the patient, the flow curve comprising a characterization of airflow during breathing of the patient over a specified time period; and classifying, by the computing device, the flow curve as one of normal breathing or airflow obstructed breathing based upon at least one machine learning model trained using a training data set.
  • Clause 10 The method of clause 9, wherein the training data set comprises a plurality of training flow curves, the plurality of training flow curves comprising flow or volume curves that are respectively classified as normal breathing or airflow obstructed breathing.
  • Clause 11 The method of clause 9 or 10, wherein the application generates an alert in response to classifying the flow curve at airflow obstructed breathing.
  • Clause 12 The method of any of clauses 9-11 , wherein the application classifies the flow curve based at least in part upon a machine learning model that is trained using the training data set.
  • Clause 14 The method of clause 9, wherein the application further causes the at least one computing device to classify the flow curve as more airflow obstructed or less airflow obstructed than a previous flow curve associated with the patient.
  • Clause 15 The method of clause 9, wherein the application obtains a confirmation of a classification of the flow curve as one of normal breathing or airflow obstructed breathing.
  • Clause 16 The method of clause 15, wherein in response to obtaining the confirmation, the application adds the flow curve with the classification to the training data set.
  • a spirometer comprising: a mouthpiece; a sensor configured to measure a respiration of a patient breathing through the mouthpiece; a computing device comprising a processor and memory; an application, when executed by the processor, causes the computing device to at least: capture breathing data of a patient using the sensor; determine a flow curve (and/or a volume curve) of the breathing data, the flow curve comprising a characterization of air flow during breathing of the patient over a specified time period; and determine a breathing classification for the flow curve based at least in part on a machine learning model, the breathing classification being a normal breathing or airflow obstructed breathing.
  • Clause 20 The spirometer of any of clauses 17-19, wherein the application, when executed by the processor, causes the computing device to at least: generate an alert based at least in part on the determination of the breathing classification being the airflow obstructed breathing.

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Abstract

Divers exemples concernent la détection et la mesure de respiration dont le flux d'air est obstrué. Des exemples de la divulgation permettent de quantifier une maladie pulmonaire à l'aide d'une respiration régulière au lieu d'efforts d'expiration forcée à l'aide de spiromètres. La respiration régulière d'un patient peut être analysée sur une période de temps et un algorithme d'apprentissage automatique peut être utilisé pour classer la respiration du patient comme normale ou à obstruction du flux d'air.
PCT/US2025/012862 2024-01-24 2025-01-24 Détection d'obstruction de flux d'air à l'aide d'un modèle d'apprentissage automatique Pending WO2025160337A1 (fr)

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030216659A1 (en) * 2002-05-16 2003-11-20 David Brawner Portable electronic spirometer
US20050256420A1 (en) * 2004-05-12 2005-11-17 Norman Robert G System and method for classifying patient's breathing using artificial neural network
US20130317379A1 (en) * 2012-05-22 2013-11-28 Sparo Labs Spirometer system and methods of data analysis
US20170273597A1 (en) * 2016-03-24 2017-09-28 Eresearchtechnology, Inc. Methods and systems for collecting spirometry data
US20180140252A1 (en) * 2015-11-16 2018-05-24 Respirix, Inc. Devices and methods for monitoring physiologic parameters
WO2019166804A1 (fr) * 2018-02-27 2019-09-06 Medchip Solutions Limited Appareil de spirométrie
US20230380792A1 (en) * 2022-05-31 2023-11-30 AireHealth Inc. Method and apparatus for determining lung pathologies and severity from a respiratory recording and breath flow analysis using a convolution neural network (cnn)

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030216659A1 (en) * 2002-05-16 2003-11-20 David Brawner Portable electronic spirometer
US20050256420A1 (en) * 2004-05-12 2005-11-17 Norman Robert G System and method for classifying patient's breathing using artificial neural network
US20130317379A1 (en) * 2012-05-22 2013-11-28 Sparo Labs Spirometer system and methods of data analysis
US20180140252A1 (en) * 2015-11-16 2018-05-24 Respirix, Inc. Devices and methods for monitoring physiologic parameters
US20170273597A1 (en) * 2016-03-24 2017-09-28 Eresearchtechnology, Inc. Methods and systems for collecting spirometry data
WO2019166804A1 (fr) * 2018-02-27 2019-09-06 Medchip Solutions Limited Appareil de spirométrie
US20230380792A1 (en) * 2022-05-31 2023-11-30 AireHealth Inc. Method and apparatus for determining lung pathologies and severity from a respiratory recording and breath flow analysis using a convolution neural network (cnn)

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