Device for detecting an anomaly in the wearing of a respiratory mask

Abstract

A method for detecting an anomaly in the wearing of a respiratory mask fitted to the face of a user, comprising: - a) detection, by a microphone, of a sound produced, at different instants, by an expiration or inspiration of the user through the mask; - b) processing of the detected sound, by a processing unit, at each instant so as to extract therefrom at least one characteristic of the sound; - c) as a function of each characteristic extracted during step b), detection, by the processing unit, of an occurrence of an anomaly in the wearing of the mask.

Description

[0001] Description

[0002] DEVICE FOR DETECTING AN ANOMALY IN THE WEARING OF A RESPIRATORY MASK

[0003] TECHNICAL FIELD

[0004] The technical field of the invention is the monitoring of users wearing a respiratory mask.

[0005] PREVIOUS ART

[0006] The use of a respiratory mask is common in the treatment of respiratory pathologies, for example chronic obstructive pulmonary disease, obesity-hypoventilation syndrome, so-called "restrictive" lung diseases, or sleep apnea.

[0007] One possible function of such masks is to connect the patient wearing it to a respiratory assistance machine that delivers overpressure to the respiratory system by covering the nose, or even the nose and mouth. This allows, for example, non-invasive ventilation treatment, which consists of providing mechanical assistance to breathing by supplying the mask with pressurized air in a cyclical manner. This helps reduce the work of the respiratory muscles and improve gas exchange. In another type of treatment, the air is pressurized in a non-cyclical manner; this modality, called "continuous positive airway pressure," can be used to treat sleep apnea.

[0008] The effectiveness of the treatment depends on the mask being applied correctly to the face, especially during periods when the user's vigilance is insufficient, such as during sleep. Depending on the treatment, the mask used may be a full-face mask, covering the mouth and nose, or a nasal mask, covering only the nose.

[0009] WO2021243293, WO2010091462, WO2020118311 and WO2021245637 describe breathing masks for treating sleep apnea. In WO2021243293, the detection of sound is discussed, for leak detection purposes, in a spectral band below 1000 Hz.

[0010] It is understood that it is desirable to have an effective, discreet and easy-to-use device for monitoring that a respiratory mask is being used correctly. The invention meets this need.

[0011] STATEMENT OF THE INVENTION A first object of the invention is a method for detecting an anomaly in the wearing of a respiratory mask applied against the face of a user, comprising:

[0012] - a) detection, by a microphone, of a sound produced, at different times, by an expiration or an inspiration of the user through the mask;

[0013] - b) processing of the detected sound by a processing unit, at each instant so as to extract at least one characteristic;

[0014] - c) depending on each characteristic extracted during step b), detection, by the processing unit, of the occurrence of an anomaly in the wearing of the mask.

[0015] Step c) may include:

[0016] - cl) taking into account at least one previously stored criterion;

[0017] - c2) comparison of each characteristic extracted during step b) with the criterion or one of the criteria taken into account during sub-step cl).

[0018] Treatment may include:

[0019] - selection of at least one sound frequency band;

[0020] - determination of a characteristic in the selected frequency band.

[0021] According to one possibility:

[0022] - the characteristic is a spectral power;

[0023] - the criterion is a spectral power threshold, so that the fault is detected when the spectral power, in the selected frequency band, exceeds the spectral power threshold.

[0024] According to one possibility:

[0025] - the characteristic is a variation in spectral power;

[0026] - the criterion is a spectral power variation threshold, so that the fault is detected when the spectral power variation, in the selected frequency band, exceeds the spectral power variation threshold.

[0027] According to one possibility:

[0028] - the criterion is a threshold;

[0029] - sub-step cl) includes:

[0030] • taking into account times at which the characteristic crosses the threshold;

[0031] • estimation of a threshold crossing time period, according to which the characteristic crosses the threshold; during sub-step c2), the anomaly is detected when the threshold crossing time period is within a predetermined range. According to one possibility:

[0032] - the mask is a face mask, configured to cover the user's mouth and nose;

[0033] - the frequency band is greater than 100 Hz.

[0034] According to a possibility

[0035] - the mask is a nasal mask, configured to cover the user's nose without covering the mouth;

[0036] - the frequency band is between 300 Hz and 13000 Hz.

[0037] According to one possibility, the frequency band extends from 1000 Hz, or from 1100 Hz, up to 10000 Hz or 13000 Hz.

[0038] According to one possibility:

[0039] - the processing unit implements a supervised learning artificial intelligence algorithm;

[0040] - the supervised learning artificial intelligence algorithm is parameterized by a learning phase taking into account sounds detected in the absence and presence of an anomaly;

[0041] A second object is a device for monitoring the wearing of a respiratory mask by a user, the device comprising:

[0042] - a microphone, configured to record sounds of the user's breathing through the mask;

[0043] - a processing unit, programmed to receive the sounds recorded by the microphone, and to implement steps b) and c) of a method according to the first object of the invention.

[0044] The invention will be better understood by reading the description of the exemplary embodiments presented in the remainder of the description, in conjunction with the figures listed below.

[0045] The invention will be better understood by reading the description of the exemplary embodiments presented in the remainder of the description, in conjunction with the figures listed below.

[0046] FIGURES

[0047] Figure 1A represents an example of a device according to the invention.

[0048] Figure 1B shows the wearing of a face mask and possible air vents.

[0049] Figure 2 represents a frequency decomposition of sounds produced by a face mask worn by a user.

[0050] Figure 3A shows a nasal mask. Figure 3B shows a diagram of how to wear a nasal mask.

[0051] Figure 4 represents a frequency decomposition of sounds produced by a nasal mask worn by a user.

[0052] Figure 5 shows schematically the steps of implementing a device according to the invention.

[0053] PRESENTATION OF SPECIAL EMBODIMENTS

[0054] Figure 1A shows an example of a device according to the invention. The device comprises a microphone 10, connected to a processing unit 11. The microphone is configured to record sounds produced by a user wearing a face mask 20.

[0055] Figure 1B shows the face mask 20 worn by a user.

[0056] The processing unit 11 is programmed to process the sounds collected by the microphone 10. More precisely, these are sounds produced by the user's breathing through the mask 20, during his inspiration and / or his expiration.

[0057] The inventors observed that when the mask is not properly applied to the face, the sound resulting from the user's breathing through the mask varies. Thus, an analysis of the sound recorded by the microphone makes it possible to detect a possible defect in the wearing of the mask.

[0058] The respiratory mask comprises a bubble 21, generally flexible and transparent, delimited by a seal 22, for example a silicone-type seal or a foam. The seal is intended to be applied against the user's skin. When the mask is worn correctly, the seal forms a sealed barrier, or considered as such, preventing or limiting air circulation between the internal space, delimited by the bubble and the user's face, and the ambient air, outside the bubble.

[0059] It should be noted that there is also an intentional calibrated air outlet through the mask allowing the user to exhale freely.

[0060] However, the mask can be displaced from its correct position of use, resulting in air leaking from either side of the seal. Such leakage is undesirable, as it compromises the ability to establish the desired pressure regime inside the bubble, resulting in a decrease in treatment effectiveness.

[0061] The processing unit 11 is configured to: process the sounds recorded by the microphone 10, so as to extract characteristics therefrom; compare the extracted characteristics with a criterion, the criterion being representative of a defect in the wearing of the mask; depending on the comparison, determine the occurrence of an anomaly in the wearing of the mask.

[0062] The criterion representing the defect in wearing the mask is previously determined during a learning phase. It may be: a spectral power threshold, in a predetermined frequency band; a threshold of variation of the spectral power in a predetermined frequency band; an intensity threshold or a threshold of variation of the intensity.

[0063] The criterion may be a passage of a periodic threshold, reflecting the fact that the characteristic studied exceeds the threshold periodically, according to a period compatible with a respiratory period, typically of the order of a few seconds or a few tens of seconds.

[0064] According to one possibility, the sound processing is carried out by a supervised learning artificial intelligence algorithm, as described later in connection with Figure 5. The criterion is therefore "implicit", in the sense that it is taken into account in the parameterization of the algorithm. The output of the algorithm can be a detection or a non-detection of an anomaly.

[0065] Figure 2 represents a spectrogram of sounds recorded, in the audible domain, during a test in which a user used a face mask correctly during a first time interval Ati. An anomaly was then deliberately introduced, inducing an air leak, particularly during exhalations. The anomaly was maintained during a second time interval At2.

[0066] The x-axis corresponds to time. The y-axis corresponds to frequency (Hz). The gray level corresponds to spectral power.

[0067] In Figure 2, the periodicity of breathing is observed by an alternation between light bands (high spectral power), which correspond to expirations, and dark bands (low spectral power), which correspond to inspirations. The occurrence of an anomaly is reflected by a periodic increase in spectral power, particularly during expirations. Spectral power is increased in frequencies above 100 Hz.

[0068] According to this embodiment, a criterion forming a spectral power threshold can be established, for one or more frequencies greater than 100 Hz. When at least one of said frequencies, the measured spectral power exceeds the threshold, a fault is detected. The method is more robust by simultaneously considering different spectral bands.

[0069] According to one possibility, the processing unit is configured to detect the instants during which at least one characteristic extracted from the measured sound satisfies the criterion representative of the anomaly. The processing unit can estimate a time period during which the characteristic satisfies the criterion. When the time period is within a predetermined range, likely to correspond to a respiratory rhythm, the processing unit detects an anomaly. Taking the time period into account makes it possible to limit the occurrence of false detections. The predetermined range can be between 2s (respiratory rate of 30 cycles per minute) and 10s (respiratory rate of 6 cycles per minute).

[0070] Figure 3A shows an example of a nasal mask 20 connected to a device according to the invention. The nasal mask comprises a bubble 21 covering only the nose and leaving the user's mouth free.

[0071] Figure 3B shows a diagram of a nasal mask worn by a user. When using this type of mask, the mouth must be kept closed, otherwise a leak will occur through the mouth. An anomaly in wearing the mask may correspond to an opening of the mouth.

[0072] Figure 4 represents a spectrogram of sounds recorded during a test in which a user used a nasal mask correctly during a first time interval Ati. A first anomaly was then voluntarily introduced, during a second time interval At2, corresponding to a slight opening of the mouth. A second anomaly was then voluntarily introduced, during a third time interval Ata, corresponding to a greater opening of the mouth.

[0073] Figure 4 is similar to Figure 2: the x-axis corresponds to time, the y-axis corresponds to frequency, and the gray level corresponds to spectral power.

[0074] As in Figure 2, the sound intensity is modulated according to the respiratory rhythm, the spectral power during the expiration phases being higher than the spectral power during the inspiration phases. We observe that the anomalies result in a variation of the spectrogram, and more precisely an increase in the spectral power in a frequency range between 300 Hz and 10000 Hz. When the anomaly becomes more significant, we observe a marked increase in the spectral power between 1000 Hz and 8000 Hz. We observe that the increase in spectral power concerns both the expiration and inspiration phases. In Figure 4, the frame drawn in white during the Ata period delimits a frequency band of interest.

[0075] Figure 5 shows the main steps of the sound processing implemented by the processing unit. during a step 100, the processing unit 11 receives the sound detected by the microphone; during a step 110, the processing unit 11 performs an extraction of one or more characteristics of the detected sound; during a step 120, the processing unit 11 takes into account a criterion, previously determined during a learning phase 90. The learning phase consists of carrying out tests for a type of mask, with and without anomaly, so as to determine the criterion or criteria corresponding to an anomaly. during a step 130, the processing unit 11 detects the occurrence of an anomaly when at least one extracted characteristic, or when each extracted characteristic, corresponds to a criterion for the occurrence of an anomaly.If an anomaly is detected, an alarm signal can be generated to alert the user or a user monitoring team.

[0076] In one possibility, the criterion for the occurrence of an anomaly is determined by implementing a supervised learning artificial intelligence algorithm, for example a neural network. The algorithm is fed either by the sound or by previously extracted characteristics of the sound.

[0077] The output of the algorithm is the occurrence, or not, of an anomaly. The parameterization of the algorithm is carried out during the learning phase, taking into account sounds recorded respectively in the presence and absence of defects. According to such an embodiment, the criterion is implicitly taken into account in the algorithm. Steps 110 and 120 are merged into a single step 110 / 120, which corresponds to the implementation of the algorithm.

[0078] The spectrograms shown in Figures 2 and 4 show that a leak results in a modification of the spectrum between 100 Hz and 13000 Hz. Between 100 Hz and 1000 Hz, some fluctuations are detected in the absence of leaks. See for example the fluctuations observed during the Ati periods in Figures 2 and 4. Such fluctuations may correspond to everyday noises, corresponding to an activity carried out by the user, not related to wearing the mask. It is therefore preferable for the spectral band to extend from a wavelength greater than 1000 Hz, for example beyond 1100 Hz, or 1200 Hz, or 1300 Hz, or 1400 Hz, or 1500 Hz, or 2000 Hz, and below 13000 Hz. Being beyond 1000 Hz or 1100 Hz makes it possible to limit the impact of user activities on the detected sound.

[0079] The invention can be used for monitoring users, both in a medical environment and at home. It does not require bulky or expensive equipment, which makes it particularly suitable for home use.

Claims

CLAIMS Device for monitoring the wearing of a respiratory mask by a user, the device comprising: - a microphone (10), configured to record sounds of the user's exhalation or inspiration through the mask; - a processing unit (11), programmed to - a) receive the sounds recorded by the microphone, at different times; - b) process the detected sound, at each instant in order to extract at least one characteristic; - c) depending on each characteristic extracted during step b), detecting the occurrence of an anomaly in the wearing of the mask. Device according to claim 1, in which the processing unit is programmed so that step c) comprises: - cl) taking into account at least one previously stored criterion; - c2) comparing each characteristic extracted during step b) with the criterion to one of the criteria taken into account during sub-step cl). Device according to claim 2, in which the processing unit is programmed to: - select at least one sound frequency band; - determining a characteristic in the selected frequency band. Device according to claim 3, in which: - the characteristic is a spectral power; - the criterion is a spectral power threshold, so that the fault is detected when the spectral power, in the selected frequency band, exceeds the spectral power threshold. Device according to claim 3, in which: - the characteristic is a variation in spectral power; - the criterion is a spectral power variation threshold, so that the fault is detected when the spectral power variation, in the selected frequency band, exceeds the spectral power variation threshold. Device according to any one of claims 2 to 5, in which: - the criterion is a threshold; - sub-step cl) includes: • taking into account times at which the characteristic crosses the threshold; • estimation of a threshold crossing time period, according to which the characteristic crosses the threshold; - during sub-step c2), the anomaly is detected when the time period of crossing the threshold is within a predetermined range. Device according to any one of claims 3 to 6, in which: - the mask is a face mask, configured to cover the user's mouth and nose; - the frequency band is greater than 100 Hz. Device according to any one of claims 3 to 6, in which: - the mask is a nasal mask, configured to cover the user's nose without covering the mouth; - the frequency band is between 300 Hz and 13000 Hz. Device according to any one of the preceding claims, in which: - the processing unit implements a supervised learning artificial intelligence algorithm; - the supervised learning artificial intelligence algorithm is parameterized by a learning phase taking into account sounds detected in the absence and presence of an anomaly.

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