Respiratory rate measurement

Abstract

The invention provides a diagnostic device configured to measure a respiratory rate of a user, the device comprising: at least one processor; a user interface; one or more sensors associated with the device; and a memory storing computer-readable instructions that, when executed by the at least one processor, cause the diagnostic device to perform a diagnostic operation when the user is at a predetermined point during an inspiration cycle, each time the user is at a predetermined point during the inspiration cycle. Prompting, via the user interface, the user to provide user input via the one or more sensors associated with the device; receiving, via the one or more sensors, a plurality of user inputs, each user input corresponding to a respective time at which the user is at a predetermined point during an inspiration cycle; in response to receiving each user input, generating a timestamp associated with the respective user input; applying a respiratory rate model to data comprising a plurality of generated timestamps, wherein the respiratory rate model calculates a respiratory rate of the user based on the generated timestamps; and outputting the calculated respiration rate.

Description

Technical Field

[0001] The present invention relates to a diagnostic device and a computer-implemented method for measuring the respiratory rate of a subject. Background Art

[0002] Spinal muscular atrophy (SMA) is associated with severe dyspnea. People with SMA (PlwSMA) typically exhibit a rapid shallow breathing pattern dominated by the diaphragm {Bourke 2014, PMID 24532751}. In addition, PlwSMA typically exhibits an impaired ability to breathe slowly and deeply. Diaphragm dominance is the result of having chest muscles that are more susceptible to muscle atrophy than abdominal muscles. Thinning of the chest muscles can even lead to chest-abdomen asynchrony (the most serious form is paradoxical breathing), in which the muscle contraction of the diaphragm is wasted in distorting the chest wall instead of inflating the lungs, resulting in inefficient breathing {LoMauro et al. 2014, PMID24632504}. Chest muscle weakness may also lead to inefficient coughing, bell-shaped chest anatomy, hypoventilation, or atelectasis (partial lung collapse or closure). It is common for PlwSMA to use a (pressure or volume controlled) non-invasive ventilator for part of the day (called non-permanent ventilation if <16 hours per day) {Mercuri et al. 2017, ISBN 9780128036853}. Non-invasive ventilators usually modify the breathing pattern by reducing chest-abdominal dyssynchrony and usually define a minimum respiratory rate below which they force a breath {Lissoni et al. 1998, PMID 9635553}.

[0003] The most commonly used clinical measure to assess lung function in PlwSMA is forced vital capacity (FVC), which is usually expressed as %FVC predicted to account for differences in age and sex {LoMauro et al. 2016, PMID 27820869}. Healthy people have a %FVC between 80 and 120 (by definition, 100 is the average), while PlwSMA may have a %FVC as low as 30 or even lower. %FVC is the most important measure of respiratory function in clinical practice, but its actual relevance to daily life is certainly questionable, and differences in values ​​<30 may be insignificant. Another measure that may be more relevant to daily life is peak cough flow (PCF), which indicates the intensity of the cough and therefore the ability to clear the airways {Chatwin et al. 2018, PMID: 29501255}.

[0004] In order to diagnose and track the progression of many diseases, such as spinal muscular atrophy (referred to herein as "SMA"), it is necessary to obtain reliable values ​​of the user's breathing rate in a manner that is non-invasive and can be performed by the subject at home, for example. Summary of the invention

[0005] The present invention provides, at a high level, a device and computer-implemented method for determining a user's breathing rate. More specifically, each time the user reaches a predetermined point in the inhalation cycle, the user is prompted to provide input to the device. From a series of inputs, the device is then able to calculate the breathing rate.

[0006] Therefore, a first aspect of the present invention provides a diagnostic device configured to measure the respiratory rate of a user, the device comprising: at least one processor; a user interface; one or more sensors associated with the device; and a memory storing computer-readable instructions which, when executed by the at least one processor, cause the diagnostic device to perform a respiratory rate test, the respiratory rate test causing the diagnostic device to: prompt the user via the user interface to provide user input via the one or more sensors associated with the device each time the user is at a predetermined point during the inhalation cycle; receive multiple user inputs via the one or more sensors, each user input corresponding to a corresponding time at which the user is at a predetermined point during the inhalation cycle; in response to receiving each user input, generate a timestamp associated with the corresponding user input; apply a respiratory rate model to data including a plurality of generated timestamps, wherein the respiratory rate model calculates the user's respiratory rate based on the generated timestamps; and output the calculated respiratory rate.

[0007] The combination of the steps of: prompting the user; receiving the plurality of user inputs; generating the timestamp; applying the breathing rate model; and, outputting the calculated breathing rate may correspond to performing a "breathing rate test." That is, the computer readable instructions, when executed by the at least one processor, may cause the device to perform a breathing rate test, which may include the above steps.

[0008] It is known that the respiratory rate is generally correlated with forced vital capacity and peak cough flow, both of which can also be used to perform clinical assessments. By measuring the respiratory rate using the diagnostic device according to the first aspect of the invention, the progression of various muscle disabilities such as SMA in the subject can be effectively tracked by actively testing the subject. As described in detail later in this application, the diagnostic device according to the first aspect of the invention can use the calculated respiratory rate to indicate and / or track the presence or progression of muscle disability (such as SMA) in a subject or user.

[0009] The computer readable instructions, when executed by the at least one processor, may cause the diagnostic device to prompt the user via the user interface to breathe at an indicated breathing rate and / or breathing depth. The computer readable instructions, when executed by the at least one processor, may cause the diagnostic device to perform multiple breathing rate tests, wherein the user is prompted to breathe at a different breathing rate and / or depth in each breathing rate test. For example, the computer readable instructions, when executed by the at least one processor, may cause the diagnostic device to prompt the user to breathe normally, breathe slowly and deeply, and / or breathe quickly and shallowly.

[0010] The breathing rate and / or depth may be indicated by a color displayed on the user interface. For example, the color green may prompt the user to breathe normally. Additionally or alternatively, the breathing rate and / or depth may be indicated by text displayed on the user interface. For example, the text displayed on the user interface may be "Breathe Normally".

[0011] The computer readable instructions, when executed by the at least one processor, may cause the diagnostic device to indicate to the user, for example via the user interface, the length of time that the user should provide user input for a given breathing rate test. For example, the indication may correspond to a countdown. The length of time may be 10 seconds or more, or 30 seconds or less, such as 20 seconds.

[0012] In a preferred embodiment, the device is or includes a smartphone. This is advantageous because almost everyone has a smartphone today. By implementing a computer-implemented process such as the one described on a smartphone, the user does not need to go to, for example, a hospital or other clinical setting in order to measure the respiratory rate. Other types of diagnostic devices may be used, such as tablet computers, laptop computers, desktop computers, etc. Alternatively, the diagnostic device may be a dedicated respiratory rate measurement device.

[0013] The diagnostic device preferably further comprises a display component configured to display the user interface. Preferably, the display component is in the form of a screen such as a touch screen. In embodiments where the display component comprises a touch screen, the touch screen preferably comprises the one or more sensors associated with the device. In those cases, the sensor may comprise a resistive sensor, a capacitive sensor, a surface acoustic wave sensor, an infrared grid sensor, an infrared acrylic projection sensor, an optical imaging sensor, a piezoelectric sensor, and / or an acoustic pulse recognition sensor. In most cases, the one or more sensors are capacitive sensors, as these are most commonly used in smartphones. Capacitive sensors work by distorting their electrostatic field when a person touches the screen, thereby registering changes in capacitance.

[0014] We now discuss in more detail the properties and operation of the breathing rate model, which is applied to data including the multiple timestamps in order to calculate the breathing rate. The breathing model can be configured to calculate the time difference between two timestamps in the multiple timestamps, and calculate the breathing rate based on the inverse of the calculated time difference. In some cases, the earlier timestamp of the two timestamps is immediately before the later timestamp of the two timestamps. In other words, the two timestamps can be consecutive timestamps. Alternatively, there can be n timestamps between the earlier timestamp of the two timestamps and the later timestamp of the two timestamps, and the breathing rate model is configured to calculate the breathing rate by multiplying the inverse of the time difference by (n+1). Note that the time difference between two consecutive timestamps can correspond to a breathing duration. In some examples, the breathing model can be configured to calculate an average breathing duration by summing a plurality of time differences, each time difference being the difference between two consecutive timestamps, and dividing the sum by n, where n is the number of time differences. The breathing rate model can then calculate an average breathing rate by taking the inverse of the average breathing duration.

[0015] By inverting the time difference or breathing duration, as described above, the breathing rate model can calculate the breathing rate in units of breaths per second. In some cases, the breathing rate model can be further configured to multiply the inverted time difference or breathing duration (i.e., the inverse) by sixty to obtain the breathing rate in units of breaths per minute.

[0016] We now discuss how to use the calculated breathing rate to indicate the presence or progression of muscle disability (such as SMA). The computer readable instructions, when executed by the at least one processor, can cause the diagnostic device to apply a clinical interpretation model to the calculated breathing rate. The clinical interpretation model can output an indication of the presence or absence of muscle disability (such as SMA) in the user, or an indication of the progression of muscle disability in the user. Applying the clinical interpretation model can include applying the clinical interpretation model to two or more of the calculated breathing rates, each of which is calculated in different breathing rate tests. The clinical interpretation model can be configured to calculate the frequency ratio of two or more of the calculated breathing rates. The indication of the presence or absence of the muscle disability can be determined based on the calculated frequency ratio. In some examples, the frequency ratio can correspond to the ratio of the second breathing rate calculated in the second breathing rate test to the first breathing rate calculated in the first breathing rate test. The first breathing rate test can correspond to a test in which the user is prompted to breathe normally. The second breathing rate test can correspond to a test in which the user is prompted to breathe deeply and / or breathe slowly. The first breathing rate test (the "normal breathing" test) may be performed before the second breathing rate test (the "deep breathing and / or slow breathing" test). Performing the normal breathing test before performing the deep breathing test and / or slow breathing test may mean that the user is more likely to breathe normally when prompted to breathe normally than if the tests were performed in reverse.

[0017] The clinical interpretation model can be configured to compare the frequency ratio with a predetermined value and output an indication of the presence or absence of the muscle disability (such as SMA) based on the comparison. Specifically, the clinical interpretation model is configured to determine whether the frequency ratio is greater than a predetermined threshold, and if it is determined that the frequency ratio is greater than the predetermined threshold, an indication of the presence of muscle disability is output (e.g., the user is PlwSMA) and / or if it is determined that the frequency ratio is less than or equal to the predetermined threshold, an indication of the absence of the muscle disability is output. For example, the predetermined threshold may be at least 0.5 and not greater than 0.8. In some examples, the predetermined threshold may be at least 0.65 and not greater than 0.75. In some examples, the predetermined threshold may be at least 0.68 and not greater than 0.73. For example, the predetermined threshold may be about 0.7, such as 0.71. That is, a frequency ratio of >0.71 may indicate that the user is PlwSMA, and a frequency ratio of ≤0.71 may indicate that the user is not PlwSMA. The respiratory rate of slow and deep breathing in PlwSMA may be <29% slower than normal breathing, while the respiratory rate of slow and deep breathing in healthy individuals may be ≥29% slower than normal breathing.

[0018] The predetermined point during the inhalation cycle may be the start of the inhalation cycle, ie when the user begins to breathe in. This allows a more reliable breathing rate to be obtained, as the user is more easily able to identify the point at which they begin to breathe.

[0019] The second aspect of the present invention provides a computer-implemented method for measuring the respiratory rate of a subject. The computer-implemented method includes: prompting the subject via a user interface to provide input via the one or more sensors associated with the device each time the subject is at a predetermined point during the inspiratory cycle; receiving multiple user inputs via the one or more sensors, each user input corresponding to a corresponding time, at which the user is at a predetermined point during the inspiratory cycle; generating a timestamp associated with the corresponding user input in response to receiving each user input; applying a respiratory rate model to multiple generated timestamps, wherein the respiratory rate model is configured to calculate the respiratory rate of the user based on the generated timestamps; and outputting the calculated respiratory rate. Preferably, the computer-implemented method according to the second aspect of the present invention is performed by a processor of a diagnostic device such as a diagnostic device according to the first aspect of the present invention. It should be understood that the optional features described above with respect to the first aspect of the present invention are also applicable to the second aspect of the present invention, unless the context clearly indicates otherwise, or whether such a combination of features is obviously technically incompatible. For example, the computer-implemented method may further include applying a clinical interpretation model to the calculated respiratory rate. The clinical interpretation model may output an indication of the presence or absence of muscle disability (such as SMA).

[0020] A third aspect of the present invention provides a computer program comprising instructions which, when executed by a processor of a computer (or other suitable data processing device), cause the processor to perform the computer-implemented method according to the second aspect of the present invention. Another aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program according to the third aspect of the present invention.

[0021] The present invention includes any combination of described aspects and preferred features unless such a combination is expressly impermissible or explicitly avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Embodiments of the present invention will now be described with reference to the accompanying drawings, in which:

[0023] - Figure 1 is a diagram of an exemplary environment in which a diagnostic apparatus for assessing a subject's respiratory rate is provided.

[0024] - Figure 2 is a flow chart of a computer-implemented method for assessing a user's breathing rate.

[0025] - Figure 3 is a flow chart of a computer-implemented method for determining an indication of the presence or absence of muscle disability, such as SMA.

[0026] - Figure 4 is a graph showing the frequency ratios calculated for PlwSMA and for healthy individuals.

[0027] - Figure 5 An example of a network architecture and data processing device that can be used to implement one or more illustrative aspects described herein is shown.

[0028] Detailed description with drawings

[0029] Aspects and embodiments of the present invention will now be discussed with reference to the accompanying drawings. Other aspects and embodiments will be apparent to those skilled in the art. All documents mentioned in this text are incorporated herein by reference.

[0030] In the following description of the various aspects, reference is made to the accompanying drawings which form a part hereof and in which are shown by way of illustration various embodiments in which the aspects described herein may be practiced. It should be understood that other aspects and / or embodiments may be utilized and structural and functional modifications may be made without departing from the scope of the described aspects and embodiments.

[0031] Aspects described herein can be used for other embodiments and can be practiced or executed in various ways. In addition, it should be understood that the wording and terminology used herein are for illustrative purposes and should not be considered as restrictive. On the contrary, the phrases and terms used in this article will be given their broadest interpretation and meaning. The use of "include" and "comprise" and its variants is meant to cover the projects and their equivalents listed thereafter and other projects and their equivalents. The use of the terms "install", "connect", "couple", "locate", "engage" and similar terms is intended to include direct and indirect installation, connection, coupling, positioning and engagement.

[0032] The systems, methods, and devices described herein provide a diagnostic device and computer-implemented methods for assessing, measuring, or determining the respiratory rate of a patient, such as a patient with muscle disability, such as a particular SMA. In some cases, the diagnostic device may be in the form of a mobile device, particularly a smartphone, on which a specific software application is installed. The software application may be configured to execute (or cause a processor of the mobile device to execute) a corresponding computer-implemented method.

[0033] In some cases, the diagnostic acquires or receives sensor data from one or more sensors associated with the mobile device as the subject interacts with the software application using the mobile device. In some cases, the sensor may be within the mobile device. In some cases, the respiratory rate is derived, calculated, or extracted from the received or obtained sensor data. In some cases, an assessment of the severity and progression of symptoms of muscle disability, particularly SMA, of the subject may be determined based on the extracted sensor features.

[0034] In an embodiment of the present invention, the diagnostic device can prompt the subject to perform a diagnostic task. In some cases, the diagnostic task is anchored in an established method and standardized test, or the diagnostic task is modeled after an established method and standardized test. In some cases, in response to the subject performing the diagnostic task, the diagnosis obtains or receives sensor data via one or more sensors. In some cases, the sensor may be in a mobile device or a wearable sensor worn by the subject. In some cases, sensor features associated with symptoms of muscle disability, particularly SMA, are extracted from the received or obtained sensor data. In some cases, an assessment of the severity and progression of symptoms of muscle disability, particularly SMA, of the subject is determined based on the extracted features of the sensor data.

[0035] Assessment of symptom severity and progression of muscle disability, particularly SMA, using diagnostics according to the present disclosure is well correlated with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing. Exemplary diagnostics according to the present disclosure can be used outside of a clinical setting, and therefore have advantages for subjects in terms of cost, ease of subject monitoring, and convenience. This facilitates frequent, particularly daily subject monitoring and testing, thereby providing a better understanding of the disease stage and providing disease insights useful to both the clinical and research communities. Exemplary diagnostics according to the present disclosure can provide earlier detection of even small changes in respiratory rate, which can indicate the presence or progression of muscle disability, particularly SMA, in a subject, and can therefore be used for better disease management including personalized therapy.

[0036] Figure 1is a diagram of an exemplary environment of a diagnostic device 105 for assessing respiratory rate of a subject 110 for muscle disability, particularly SMA, in which is provided. In some cases, the device 105 can be a smartphone, smart watch, or other mobile computing device. The device 105 includes a display screen 160. In some cases, the display screen 160 can be a touch screen. The device 105 includes at least one processor 115 and a memory 125 storing computer instructions for a symptom monitoring application 130, which when executed by the at least one processor 115 causes the device 105 to assess one or more respiratory rates of a patient, such as a patient with muscle disability, particularly SMA, and / or determine an indication of the presence or absence of muscle disability, such as SMA. The device 105 receives a plurality of sensor data via one or more sensors associated with the device 105. In some cases, the one or more sensors associated with the device are at least one of: a sensor disposed within the device or a sensor worn by the subject and configured to communicate with the device. In Figure 1 In FIG. 1 , the sensors associated with the device 105 include a first sensor 120 a disposed within a display screen 160 of the device 105 .

[0037] The device 105 extracts the breathing rate from the received first sensor data.

[0038] The device 105 determines the respiratory rate of the subject 110 based on the extracted features. In some cases, the device 105 sends the extracted features to the server 150 via the network 180. In some cases, the device 105 sends the first sensor data to the server 150 via the network 180. The server 150 includes at least one processor 155 and a memory 161 storing computer instructions for the symptom assessment application 170, which, when executed by the server processor 155, causes the processor 155 to determine the respiratory rate of the subject based on the extracted features received by the server 150 from the device 105. In some cases, the symptom assessment application 170 can determine the respiratory rate of the subject 110 based on the extracted features of the sensor data received from the device 105 and the subject database 175 stored in the memory 160. Multiple respiratory rate tests can be performed, wherein the first sensor data is collected and processed in each test so that multiple respiratory rates can be determined. The symptom assessment application 170 can further determine an indication of the presence or absence of muscle disability such as SMA based on the determined one or more respiratory rates and can output the indication. In some cases, the subject database 175 may include subject data and / or clinical data. In some cases, the subject database 175 may include intraclinical and sensor-based measurements of respiratory rate. In some cases, the subject database 175 may be independent of the server 150. In some cases, the server 150 sends the determined one or more respiratory rates and / or an indication of the presence or absence of muscle incapacity to the device 105. In some cases, the device 105 may output the respiratory rate and / or indication. In some cases, the device 105 may communicate information to the subject 110 based on the assessment. In some cases, the assessment of the respiratory rate or the indication of the presence or absence of muscle incapacity may be communicated to a clinician, who may determine a personalized therapy for the subject 110 based on the assessment.

[0039] In some cases, when the computer instructions for the symptom monitoring application 130 are executed by at least one processor 115, the device 105 determines the respiratory rate of the subject 110 based on the active test of the subject 110. The device 105 prompts the subject 110 to perform one or more tasks. The respiratory rate for each task can be calculated. In some cases, prompting the subject to perform one or more diagnostic tasks includes prompting the subject to take several deep breaths and tapping the touch screen (or equivalent sensor) at the beginning of each inhalation cycle (i.e., when they begin to inhale). Prompting the subject to perform one or more diagnostic tasks may further include prompting the subject to take several normal breaths and tapping the touch screen (or equivalent sensor) at the beginning of each inhalation cycle.

[0040] In response to subject 110 performing each of the one or more diagnostic tasks, diagnostic device 105 receives a plurality of sensor data via one or more sensors associated with device 105, the sensor data including a series of timestamps corresponding to the times at which the user indicated (via the sensors) when they were breathing. Device 105 extracts the breathing rate from the sensor data received for each diagnostic task. Thus, a plurality of breathing rates may be determined. Symptoms of muscle disability, particularly SMA, of subject 110 may include symptoms that affect the breathing rate of subject 110.

[0041] Thus, the device may further determine the presence or absence of muscle dysfunction such as SMA from one or more breathing rates, for example by calculating a frequency ratio. The frequency ratio may be a ratio of a breathing rate corresponding to when the user is prompted to take a deep breath to a breathing rate corresponding to when the user is prompted to breathe normally.

[0042] Figure 2 An exemplary method is shown for using Figure 1 The exemplary apparatus 105 assesses the breathing rate of a subject in a subject based on an active test of the subject. Figure 2 refer to Figure 1 To describe, it should be noted that Figure 2 The method steps may be performed by other systems. The computer-implemented method includes, in step 205, prompting the subject to provide a user input on a user input interface displayed on the display 160 of the device 105 each time the subject is at a predetermined point in the inspiratory cycle (such as the beginning). The method includes receiving a plurality of sensor data via one or more sensors (step 210), which may be in the form of capacitive sensors in the touch screen of the display component 160, in response to the subject performing one or more tasks.

[0043] Then, in step 215, the breathing rate model is applied to the data including the plurality of time stamps. The features of the breathing rate model have been explained in detail elsewhere in this patent application and are not repeated here for the sake of brevity.

[0044] In step 220, the breathing rate is output, for example by the processor 115 generating instructions which, when executed by the display component 160 of the apparatus 105, cause the display component 160 to display the breathing rate. Alternatively, the calculated breathing rate may be transmitted to the server 150 as outlined elsewhere in this application.

[0045] As described above, assessment of symptom severity and progression of muscle disability, particularly SMA, using diagnostics according to the present disclosure correlates well with assessments based on clinical outcomes and can therefore replace clinical subject monitoring and testing.

[0046] Figure 3 An exemplary method is shown for using Figure 1 The exemplary apparatus 105 determines an indication of the presence or absence of SMA in a subject based on active testing of the subject. Figure 3 refer to Figure 1 To describe, it should be noted that Figure 3 The method steps may be performed by other systems. The computer-implemented method includes determining a first respiratory rate and a second respiratory rate in steps 225 and 230, each respiratory rate according to a reference Figure 2 The method described above is determined. In the test for determining the first breathing rate in step 225, the user may be prompted to breathe normally. In the test for determining the second breathing rate in step 230, the user may be prompted to breathe deeply and slowly. In step 230, the computer-implemented method includes calculating a frequency ratio, which may correspond to the ratio of the second breathing rate to the first breathing rate. Then, in step 240, the computer-implemented method includes determining whether the calculated frequency ratio is greater than a predetermined threshold. If the calculated frequency ratio is determined to be greater than the predetermined threshold, then in step 245, the computer-implemented method includes outputting an indication of the presence of SMA. If the calculated frequency ratio is determined to be less than or equal to the predetermined threshold, then in step 250, the computer-implemented method includes outputting an indication of the absence of SMA.

[0047] For example, the reservation threshold may be 0.71. That is, a frequency ratio of >0.71 may indicate that the user is PlwSMA, and a frequency ratio of ≤0.71 may indicate that the user is not PlwSMA. This can be referred to Figure 4 To explain. Figure 4 is a graph showing frequency ratios calculated for PlwSMA and healthy individuals. The graph shows that most PlwSMA among PlwSMA may have test results of frequency ratios>0.71, while most healthy individuals among healthy individuals may have test results of frequency ratios≤0.71.

[0048] Figure 5 A method is shown that can be used to implement one or more illustrative aspects described herein (such as Figure 1 and Figure 2301 is an example of a network architecture and data processing device of the aspects described above. Various network nodes 303, 305, 307 and 309 can be interconnected via a wide area network (WAN) 301 (such as the Internet). Other networks can also or alternatively be used, including private intranets, corporate networks, LANs, wireless networks, personal networks (PANs), etc. Network 301 is for illustrative purposes and can be replaced with fewer or other computer networks. The local area network (LAN) can have one or more of any known LAN topology and can use one or more of a variety of different protocols, such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) can be connected to one or more networks via twisted pair, coaxial cable, optical fiber, radio waves or other communication media.

[0049] The term "network" as used herein and depicted in the accompanying drawings refers not only to a system in which remote storage devices are coupled together via one or more communication paths, but also to independent devices that may occasionally be coupled to a system having storage functions. Thus, the term "network" includes not only a "physical network" but also a "content network", which consists of data (attributed to a single entity) residing in all physical networks.

[0050] Components may include a data server 303, a web server 305, and client computers 307, 309. The data server 303 provides overall access, control, and management of the database and control software for performing one or more illustrative aspects described herein. The data server 303 may be connected to the web server 305, through which users interact and obtain data upon request. Alternatively, the data server 303 itself may act as a web server and be directly connected to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet), via a direct or indirect connection, or via some other network. A user may interact with the data server 303 using a remote computer 307, 309 (e.g., using a web browser) via one or more externally disclosed websites hosted by the web server 305 to connect to the data server 303. The client computers 307, 309 may be used together with the data server 303 to access data stored therein, or may be used for other purposes. For example, as is known in the art, a user may access the web server 305 from a client device 307 using an Internet browser or by executing a software application that communicates with the web server 305 and / or data server 303 over a computer network (such as the Internet). In some cases, the client computer 307 may be a smartphone, smart watch, or other mobile computing device, and may implement a diagnostic device such as Figure 1 In some cases, the data server 303 may implement a server such as Figure 1 The server 150 shown in FIG.

[0051] The server and application can be combined on the same physical computer and retain separate virtual or logical addresses, or they can reside on separate physical computers. Figure 1 Only one example of a network architecture that can be used is shown, and those skilled in the art will appreciate that the specific network architecture and data processing devices used can vary and assist in the functions they provide, as further described herein. For example, the services provided by the network server 305 and the data server 303 can be combined on a single server.

[0052] Each component 303, 305, 307, 309 can be any type of known computer, server or data processing device. The data server 303 may include, for example, a processor 311 that controls the overall operation of the rate server 303. The data server 303 may further include a RAM 313, a ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.) and a memory 321. The I / O 319 may include various interface units and drivers for reading, writing, displaying and / or printing data or files. The memory 321 may further store operating system software 323 for controlling the overall operation of the data processing device 303, control logic 325 for instructing the data server 303 to perform aspects described herein, and other application software 327 that provides assistance, support and / or other functions, which may or may not be used in conjunction with other aspects described herein. The control logic may also be referred to as data server software 325 in this article. The functionality of the data server software may refer to a combination of operations or decisions made automatically based on rules encoded into the control logic, operations made manually by a user providing input to the system, and / or automatic processing based on user input (e.g., queries, data updates, etc.).

[0053] The memory 321 may also store data for performing one or more aspects described herein, including a first database 329 and a second database 331. In some cases, the first database may include the second database (e.g., as a separate table, report, etc.). That is, depending on the system design, information may be stored in a single database or divided into different logical, virtual or physical databases. The devices 305, 307, 309 may have an architecture similar to or different from that described with respect to the device 303. Those skilled in the art will appreciate that the functionality of the data processing device 303 (or devices 305, 307, 309) as described herein may be distributed across multiple data processing devices, for example, to distribute processing loads between multiple computers to separate processing performed based on geographic location, user access level, quality of service (QoS), etc.

[0054] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions executed by one or more computers or other devices described herein, such as in one or more program modules. Typically, program modules include routines, programs, targets, components, data structures, etc., which perform specific tasks or implement specific abstract data types when executed by a processor in a computer or other device. Modules may be written in a source code programming language and then compiled to execute the module, or modules may be written in a scripting language (such as, but not limited to, HTML or XML). Computer-executable instructions may be stored on a computer-readable medium, such as a hard disk, an optical disk, a removable storage medium, a solid-state memory, a RAM, etc. As will be appreciated by those skilled in the art, the functions of the program modules may be combined or distributed as needed in various embodiments. In addition, the functions may be embodied in firmware or equivalent hardware (such as an integrated circuit, a field programmable gate array (FPGA), etc.) in whole or in part. Specific data structures may be used to more effectively implement one or more aspects, and such data structures are included within the scope of computer-executable instructions and computer-usable data described herein.

[0055] The features disclosed in the foregoing description, or in the following claims, in terms of the manner of expressing or implementing the disclosed functions in their specific forms, or in terms of the methods or processes for obtaining the disclosed results, may be appropriately used alone in their various forms, or in any combination to implement the present invention.

[0056] Although the present invention has been described in conjunction with the above exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art when this disclosure is given. Therefore, the above exemplary embodiments of the present invention are considered to be illustrative rather than restrictive. Various changes may be made to the described embodiments without departing from the spirit and scope of the present invention.

[0057] For the avoidance of any doubt, any theoretical explanations provided herein are intended to improve the reader's understanding. The inventors do not wish to be bound by any of these theoretical explanations.

[0058] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0059] Throughout the specification, including the following claims, unless the context requires otherwise, the words "comprise" and "include" and variations such as "comprises and comprising" and "including", will be understood to imply the inclusion of stated integers or steps or groups of integers or steps but not the exclusion of any other integers or steps or groups of integers or steps.

[0060] It must be noted that, as used in this specification and the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Ranges may be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by use of the antecedent "about", it will be understood that the particular value forms another embodiment. The term "about" in relation to a numerical value is optional and means, for example, + / - 10%.

Claims

1. A diagnostic device configured to measure a user's breathing rate, the device comprising: at least one processor; User interface; one or more sensors associated with the device; as well as a memory storing computer readable instructions that, when executed by the at least one processor, cause the diagnostic device to perform a respiratory rate test that causes the diagnostic device to: prompting the user via the user interface to provide user input via the one or more sensors associated with the device each time the user is at a predetermined point during an inhalation cycle; receiving, via the one or more sensors, a plurality of user inputs, each user input corresponding to a respective time at which the user is at a predetermined point during an inhalation cycle; In response to receiving each user input, generating a timestamp associated with the respective user input; applying a breathing rate model to data comprising a plurality of generated time stamps, wherein the breathing rate model calculates a breathing rate of the user based on the generated time stamps; and Outputs the calculated respiration rate.

2. The diagnostic device according to claim 1, wherein: The diagnostic device includes a smartphone including a display component configured to display the user interface.

3. The device according to claim 2, wherein: The display component includes a touch screen, the touch screen includes the one or more sensors, and wherein the user input is a touch of the screen detectable by the one or more sensors. The apparatus of claim 3 , wherein the one or more sensors comprise capacitive sensors.

5. The diagnostic device according to any one of claims 1 to 4, wherein: The breathing rate model is further configured to calculate a time difference between two time stamps of the plurality of time stamps, and calculate the breathing rate based on a reciprocal of the time difference.

6. The device according to claim 5, wherein: The earlier of the two timestamps immediately precedes the later of the two timestamps.

7. The device according to any one of claims 1 to 4, wherein: The breathing model is further configured to: calculate a plurality of time differences, each time difference being a time difference between two consecutive time stamps; calculate an average time difference by summing the plurality of time differences and dividing the sum by n or (n-1), where n is the number of time differences; The respiration rate is calculated by taking the inverse of the mean time difference.

8. The device according to any one of claims 1 to 7, wherein: The time difference is in seconds; and The breathing rate model is further configured to multiply the inverse time difference by 60 to obtain the breathing rate in breaths per minute.

9. The device according to any one of claims 1 to 8, wherein: The predetermined point during the inhalation cycle is the point when the patient begins to inhale.

10. A device according to any one of claims 1 to 9, wherein the computer readable instructions, when executed by the at least one processor, cause the diagnostic device to perform two breathing rate tests, wherein the user is prompted to breathe at a different breathing rate and / or depth in each breathing rate test.

11. A device according to claim 10, wherein the computer readable instructions, when executed by the at least one processor, cause the diagnostic device to apply a clinical interpretation model to the calculated respiratory rate, wherein the clinical interpretation model outputs an indication of the presence or absence of muscle disability.

12. The apparatus of claim 11, wherein the clinical interpretation model is configured to calculate a frequency ratio of the calculated respiratory rate and to determine the indication of the presence or absence of the muscle disability based on the calculated frequency ratio.

13. An apparatus according to claim 12, wherein the two breathing rates include a first breathing rate and a second breathing rate, and wherein the frequency ratio corresponds to a ratio of the second breathing rate to the first breathing rate, wherein the first breathing rate corresponds to a breathing rate test in which the user is prompted to breathe normally and the second breathing rate test corresponds to a test in which the user is prompted to take a deep breath.

14. The apparatus of claim 13, wherein the clinical interpretation model is configured to compare the frequency ratio with a predetermined value and to output the indication of the presence or absence of the muscle disability based on the comparison.

15. An apparatus according to claim 14, wherein the clinical interpretation model is configured to determine whether the frequency ratio is greater than a predetermined threshold, and if it is determined that the frequency ratio is greater than the predetermined threshold, output an indication of the presence of the muscle disability, and if it is determined that the frequency ratio is less than or equal to the predetermined threshold, output an indication of the absence of the muscle disability. The apparatus according to claim 15 , wherein the predetermined threshold is at least 0.6 and not greater than 0.

8.

17. A computer-implemented method of measuring a patient's respiratory rate, the method comprising: prompting the subject via a user interface to provide input via one or more sensors each time the subject is at a predetermined point during an inspiratory cycle; receiving a plurality of user inputs via the one or more sensors; In response to receiving each user input, generating a timestamp associated with the respective user input; applying a breathing rate model to the data including a plurality of generated time stamps, wherein the breathing rate model is configured to calculate a breathing rate of the user based on the generated time stamps; as well as Outputs the calculated respiration rate.

18. The computer-implemented method of claim 17, wherein the computer-implemented method further comprises the steps of: A clinical interpretation model is applied to the calculated respiration rate, wherein the clinical interpretation model outputs an indication of the presence or absence of muscle disability or an indication of the progression of muscle disability.

19. A computer-implemented method according to claim 17 or claim 18, wherein: The computer-implemented method is executed by a processor of a diagnostic device according to any one of claims 1 to 16.

20. The computer-implemented method of claim 17 or claim 18, wherein the steps of prompting the subject, receiving the user input, and generating the timestamp are performed by a processor of a diagnostic device, and wherein the step of applying the breathing rate model is performed by a processor of a server, wherein the diagnostic device is configured to transmit the generated timestamp to the server, and wherein the diagnostic device comprises: at least one processor; User interface; one or more sensors associated with the device; as well as a memory storing computer readable instructions that, when executed by the at least one processor, cause the diagnostic device to perform a respiratory rate test that causes the diagnostic device to: prompting the user via the user interface to provide user input via the one or more sensors associated with the device each time the user is at a predetermined point during an inhalation cycle; receiving, via the one or more sensors, a plurality of user inputs, each user input corresponding to a respective time at which the user is at a predetermined point during an inhalation cycle; In response to receiving each user input, a timestamp associated with the respective user input is generated.

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