Respiratory rate measurement

Abstract

1. A diagnostic device configured to measure a user's respiratory rate, the diagnostic 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: prompt a user via the user interface to provide a user input via one or more sensors associated with the device each time the user reaches a predetermined time point during an inhalation cycle; receive a plurality of user inputs via the one or more sensors, each user input corresponding to a respective time at which the user is at a predetermined time point during an inhalation cycle; generate a timestamp associated with each user input in response to receiving each user input; apply a respiratory rate model to data including the plurality of generated timestamps, the respiratory rate model calculating the user's respiratory rate based on the generated timestamps; and output the calculated respiratory rate.

Description

[Technical Field]

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

[0002] Spinal muscular atrophy (SMA) is associated with severe respiratory distress. Patients with SMA (PlwSMA) typically exhibit a rapid, shallow breathing pattern dominated by the diaphragm {Bourke 2014, PMID 24532751}. Furthermore, PlwSMA typically exhibits a reduced ability to breathe slowly and deeply. This diaphragmatic control results in the pectoral muscles being more susceptible to muscle atrophy than the abdominal muscles. Pectoral muscle thinning can also lead to thoracoabdominal asynchrony (with paradoxical breathing as its most severe form), in which diaphragmatic contractions are wasted, deforming the chest wall rather than expanding the lungs, resulting in highly inefficient breathing {LoMauro et al. 2014, PMID 24632504}. Chest muscle weakness can also result in inefficient coughing, bell-chest anatomy, hypoventilation, or atelectasis (collapse or closure of a portion of the lung). In PlwSMA, the use of non-invasive ventilators (pressure or volume controlled) for part of the day (less than 16 hours per day, called non-permanent ventilation) is very common {Mercuri et al. 2017, ISBN 9780128036853}. Non-invasive ventilators typically alter breathing patterns by reducing the amount of thoracoabdominal asynchrony, and typically define a minimum respiratory rate below which breathing is forced {Lissoni et al. 1998, PMID 9635553}.

[0003] The most commonly used clinical measure to assess lung function in PlwSMA is forced vital capacity (FVC), typically expressed as %FVC predicted, which accounts for age and gender differences {LoMauro et al. 2016, PMID 27820869}. Healthy individuals have a %FVC of 80–120 (100 is the mean by definition), whereas PlwSMA individuals may have a %FVC as low as 30 or even lower. While %FVC is the most important measure of respiratory function in clinical practice, its actual relevance to daily life must be questioned, and differences of less than 30 may be irrelevant. Another measure that is likely more relevant to daily life is peak expiratory flow (PCF), a measure that indicates cough strength and, therefore, the ability to clear the airways {Chatwin et al. 2018, PMID: 29501255}.

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

[0005] At a high level, the present invention provides an apparatus and computer-implemented method for determining a user's respiratory rate. More specifically, the user is prompted to provide inputs to the apparatus each time a predetermined point in the inhalation cycle is reached. From the series of inputs, the apparatus can calculate the respiratory rate.

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

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

[0008] Respiratory rate is often known to correlate with forced vital capacity and peak expiratory flow, both of which may also be useful when conducting clinical assessments. By measuring respiratory rate using a diagnostic device according to the first aspect of the present invention, it may be possible to effectively track the progression of various muscle disorders, such as SMA, in a subject through active testing of the subject. As described in detail later in this application, a diagnostic device according to the first aspect of the present invention may use the calculated respiratory rate to indicate and / or track the presence or progression of a muscle disorder, 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 a user via a user interface to breathe at a prescribed 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, with the user being 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, to breathe slowly and deeply, and / or to breathe quickly and shallowly.

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

[0011] The computer-readable instructions, when executed by the at least one processor, may cause the diagnostic device to prompt a user, e.g., via a user interface, as to the duration for which the user should provide user input for a given respiration rate test. The prompt may correspond, for example, to a countdown. The duration may be 10 seconds or more, or 30 seconds or less, e.g., 20 seconds.

[0012] In a preferred embodiment, the device is or includes a smartphone. This is advantageous because smartphones are virtually everyone's today. By performing a computer-implemented process as described on the smartphone, the user does not need to go to, for example, a hospital or other clinical site to measure the respiration rate. Other types of diagnostic devices, such as tablets, laptop computers, desktop computers, etc., may also be used. Alternatively, the diagnostic device may be a dedicated respiration rate measurement device.

[0013] The diagnostic device preferably further comprises a display component configured to display a user interface. Preferably, the display component is in the form of a screen, such as a touchscreen. In embodiments in which the display component comprises a touchscreen, the touchscreen preferably includes one or more sensors associated with the device. In these cases, the sensors may include resistive sensors, capacitive sensors, surface acoustic wave sensors, infrared grid sensors, infrared acrylic projection sensors, optical imaging sensors, piezoelectric sensors, and / or acoustic pulse recognition sensors. In most cases, the one or more sensors are capacitive sensors, as these are most commonly used in smartphones. Capacitive sensors function on the basis that when a person touches the screen, its electrostatic field is distorted and a change in capacitance is recorded.

[0014] The nature and operation of a respiration rate model applied to data including multiple timestamps to calculate respiration rate will now be discussed in more detail. The respiration rate model may be further configured to calculate a time difference between two of the multiple timestamps and calculate the respiration rate based on the inverse of the calculated time difference. In some cases, the earlier of the two timestamps may immediately precede the later of the two timestamps. In other words, the two timestamps may be consecutive timestamps. Alternatively, there may be n timestamps between the earlier of the two timestamps and the later of the two timestamps, and the respiration rate model is configured to calculate the respiration rate by multiplying the inverse of the time difference by (n+1). Note that the time difference between two consecutive timestamps may correspond to a respiration duration. In some examples, the respiration rate model may be configured to calculate an average respiration duration by summing multiple 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 respiration rate model may then calculate the average respiration rate by taking the inverse of the average respiration duration.

[0015] By inverting the time difference or breath duration as outlined above, the breath rate model can calculate the breath rate in breaths per second. In some cases, the breath rate model may be further configured to multiply the inverted time difference or breath duration (i.e., the reciprocal) by 60 to obtain the breath rate in breaths per minute.

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

[0017] The clinical interpretation model may be configured to compare the frequency ratio with a predetermined value and, based on the comparison, output an indication of the presence or absence of a muscle disorder, such as SMA. In particular, the clinical interpretation model may be configured to determine whether the frequency ratio is greater than a predetermined threshold and, if the frequency ratio is determined to be greater than the predetermined threshold, output an indication of the presence of a muscle disorder (e.g., that the user has PlwSMA), and / or, if the frequency ratio is determined to be equal to or less than the predetermined threshold, output an indication of the absence of a muscle disorder. For example, the predetermined threshold may be at least 0.5 and less than or equal to 0.8. In some examples, the predetermined threshold may be at least 0.65 and less than or equal to 0.75. In some examples, the predetermined threshold may be at least 0.68 and less than or equal to 0.73. For example, the predetermined threshold may be approximately 0.7, e.g., 0.71. That is, a frequency ratio greater than 0.71 may indicate that the user has PlwSMA, and a frequency ratio less than or equal to 0.71 may indicate that the user does not have PlwSMA. PlwSMA may slow the breathing rate by less than 29% for slow, deep breathing compared to normal breathing, whereas healthy individuals may slow the breathing rate by more than 29% for slow, deep breathing compared to normal breathing.

[0018] The predetermined point in the inhalation cycle may be the start of the inhalation cycle, i.e., when the user begins to breathe in. This allows the point at which the user begins to breathe to be more easily identified, thereby providing a more reliable breathing rate.

[0019] A second aspect of the present invention provides a computer-implemented method for measuring a subject's respiratory rate. The computer-implemented method includes the steps of prompting the subject, via a user interface, to provide input via one or more sensors associated with the device each time the subject reaches a predetermined time during an inhalation cycle; receiving a plurality of user inputs via the one or more sensors, each user input corresponding to a respective time the user is at a predetermined time during an inhalation cycle; generating a timestamp associated with each user input in response to receiving each user input; applying a respiratory rate model to the plurality of generated timestamps, the respiratory rate model configured to calculate the user's respiratory rate based on the generated timestamps; and outputting the calculated respiratory rate. Preferably, the computer-implemented method of the second aspect of the present invention is executed by a processor of a diagnostic device, such as the diagnostic device of the first aspect of the present invention. It will be understood that any feature described above with respect to the first aspect of the present invention applies equally well to the second aspect of the present invention, unless the context clearly dictates otherwise or unless such combinations of features are clearly 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 a muscle disorder 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 of the second aspect of the invention. A further aspect of the present invention provides a computer-readable storage medium having stored thereon the computer program of the third aspect of the invention.

[0021] The present invention includes combinations of the described embodiments and preferred features except where such combinations are expressly not permitted or explicitly avoided. [Brief explanation of the drawings]

[0022] Embodiments of the present invention will now be described with reference to the accompanying drawings. [Figure 1] FIG. 1 illustrates an example of an environment in which a diagnostic device for assessing a subject's respiratory rate is provided. [Figure 2] 1 is a flow diagram of a computer-implemented method for assessing a user's respiratory rate. [Figure 3] 1 is a flow diagram of a computer-implemented method for determining an indication of the presence or absence of a muscle disorder such as SMA. [Figure 4] Plot showing frequency ratios calculated for PlwSMA and healthy individuals. [Figure 5] FIG. 1 illustrates an example of a network architecture and data processing device that can be used to implement one or more exemplary aspects described herein. DETAILED DESCRIPTION OF THE INVENTION

[0023] Aspects and embodiments of the present invention will now be described with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art. All documents mentioned herein are incorporated by reference.

[0024] In the following description of various aspects, reference is made to the accompanying drawings, which form a part hereof, and which show, by way of illustration, various embodiments in which the aspects described herein may be practiced. It is to be understood that other aspects and / or embodiments may be utilized and structural and functional changes may be made without departing from the scope of the described aspects and embodiments.

[0025] The aspects described herein are capable of other embodiments and of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. Rather, the phrases and terms used herein should be given their broadest interpretation and meaning. The use of "including" and "comprising" and variations thereof is intended to encompass the items listed thereafter and their equivalents, as well as additional items and their equivalents. The use of the terms "mounted," "connected," "coupled," "disposed," "engaged," and similar terms is meant to include both direct and indirect mounting, connecting, coupling, disposing, and engaging.

[0026] The systems, methods, and devices described herein provide diagnostic devices and computer-implemented methods for assessing, measuring, or determining the respiratory rate of a patient, e.g., a patient suffering from a muscular disorder such as certain SMA. In some cases, the diagnostic device may be in the form of a mobile device, particularly a smartphone, with a specific software application installed. The software application may be configured to execute (or cause a processor of the mobile device to execute) a corresponding computer-implemented method.

[0027] In some cases, the diagnosis acquires or receives sensor data from one or more sensors associated with the mobile device when the subject uses the mobile device to interact with the software application. In some cases, the sensors may be within the mobile device. In some cases, the respiratory rate is derived, calculated, or extracted from the received or acquired sensor data. In some cases, an assessment of the severity and progression of symptoms of muscle disorders, particularly SMA, in the subject may be determined based on the extracted sensor features.

[0028] In embodiments of the present invention, the diagnostic device may prompt the subject to perform a diagnostic task. In some cases, the diagnostic task is anchored or modeled after established methods and standardized tests. In some cases, in response to the subject performing the diagnostic task, the diagnostic device acquires or receives sensor data via one or more sensors. In some cases, the sensors may be in a mobile device or in a wearable sensor worn by the subject. In some cases, sensor features associated with symptoms of myopathy, particularly SMA, are extracted from the received or acquired sensor data. In some cases, an assessment of the severity and progression of symptoms of myopathy, particularly SMA, in the subject is determined based on the extracted features of the sensor data.

[0029] Assessment of the severity and progression of myopathy, particularly SMA, symptoms using a diagnostic according to the present disclosure correlates well with assessment 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 clinic environment, thus offering advantages in cost, ease of subject monitoring, and convenience for subjects. This facilitates frequent subject monitoring and testing, particularly daily, resulting in a better understanding of disease stages and providing disease-related 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 myopathy, particularly SMA, in a subject, and can therefore be used for better disease management, including personalized treatment.

[0030] FIG. 1 is a diagram of an exemplary environment in which a diagnostic device 105 for assessing the respiratory rate of a muscle disorder, particularly SMA, in a subject 110 is provided. In some cases, the device 105 may be a smartphone, smartwatch, or other mobile computing device. The device 105 includes a display screen 160. In some cases, the display screen 160 may be a touchscreen. The device 105 includes at least one processor 115 and a memory 125 storing computer instructions for a symptom monitoring application 130 that, 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 a muscle disorder, particularly SMA, and / or determine an indication of the presence or absence of a muscle disorder, 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 sensors disposed within the device and sensors worn by the subject and configured to communicate with the device. 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 .

[0031] The device 105 extracts the respiration rate from the received first sensor data.

[0032] Device 105 determines the respiratory rate of subject 110 based on the extracted features. Optionally, device 105 transmits the extracted features to server 150 over network 180. Optionally, device 105 transmits first sensor data to server 150 over network 180. Server 150 includes at least one processor 155 and memory 161 storing computer instructions for a symptom assessment application 170, the computer instructions, when executed by server processor 155, causing processor 155 to determine the respiratory rate of the subject based on the extracted features received by server 150 from device 105. Optionally, symptom assessment application 170 may determine the respiratory rate of subject 110 based on the extracted features of the sensor data received from device 105 and a subject database 175 stored in memory 160. Multiple respiratory rate tests may be performed, with first sensor data collected and processed in each test, resulting in multiple respiratory rates being determined. Symptom assessment application 170 may further determine an indication of the presence or absence of a myopathy, such as SMA, from the determined one or more respiratory rates and may output the indication. In some cases, subject database 175 may include subject data and / or clinical data. In some cases, subject database 175 may include in-clinic and sensor-based measures of respiratory rate. In some cases, subject database 175 may be independent from server 150. In some cases, server 150 transmits the determined one or more respiratory rates and / or an indication of the presence or absence of a myopathy to device 105. In some cases, device 105 may output the respiratory rate and / or an indication. In some cases, device 105 may communicate information to subject 110 based on the evaluation. In some cases, the respiratory rate evaluation or an indication of the presence or absence of a myopathy may be communicated to a clinician, who may determine an individualized treatment for subject 110 based on the evaluation.

[0033] In some cases, the computer instructions of the symptom monitoring application 130, when executed by the at least one processor 115, cause the device 105 to determine the subject's 110 respiratory rate based on the subject's 110 active testing. The device 105 prompts the subject 110 to perform one or more tasks. A respiratory rate may be calculated for each task. In some cases, prompting the subject to perform the one or more diagnostic tasks includes prompting the subject 110 to take several deep breaths and tap a touchscreen (or equivalent sensor) at the beginning of each inhalation cycle, i.e., as they begin to inhale. In some cases, prompting the subject to perform the one or more diagnostic tasks may further include prompting the subject 110 to take several normal breaths and tap a touchscreen (or equivalent sensor) at the beginning of each inhalation cycle.

[0034] 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 times indicating (via the sensors) when the user inhaled. Device 105 extracts a respiratory rate from the received sensor data for each diagnostic task. Thus, a plurality of respiratory rates may be determined. Symptoms of muscle disorders, particularly SMA, in subject 110 may include symptoms that affect the respiratory rate of subject 110.

[0035] Thus, the device may further determine the presence or absence of a muscular disorder such as SMA by calculating, for example, a frequency ratio from one or more breathing rates, which may be a ratio between the breathing rate corresponding to when the user is encouraged to breathe deeply and the breathing rate corresponding to when the user is encouraged to breathe normally.

[0036] FIG. 2 illustrates an exemplary method for assessing a subject's respiratory rate based on active testing of the subject using the exemplary device 105 of FIG. 1. While FIG. 2 is described with reference to FIG. 1, it should be noted that the steps of the method of FIG. 2 may be performed by other systems. The computer-implemented method includes, at step 205, prompting the subject to provide user input on a user input interface displayed on the display 160 of the device 105 each time the subject reaches a predetermined point in their inhalation cycle (e.g., the beginning). The method includes receiving a plurality of sensor data (step 210) via one or more sensors, which may be in the form of capacitance sensors in a touchscreen of the display component 160, in response to the subject performing one or more tasks.

[0037] A respiration rate model is then applied to the data including the multiple timestamps in step 215. The features of the respiration rate model are described in detail elsewhere in this patent application and will not be repeated here for the sake of brevity.

[0038] In step 220, the respiration rate is output, for example by processor 115 generating instructions that, when executed by display component 160 of device 105, cause display component 160 to display the respiration rate. Alternatively, the calculated respiration rate may be transmitted to server 150, as outlined elsewhere herein.

[0039] As explained above, assessment of the severity and progression of symptoms of muscle disorders, particularly SMA, using diagnostics according to the present disclosure correlates well with clinical outcome-based assessments and may therefore replace clinical subject monitoring and testing.

[0040] FIG. 3 illustrates an exemplary method for determining an indication of the presence or absence of SMA in a subject based on active testing of the subject using the exemplary device 105 of FIG. 1. While FIG. 3 is described with reference to FIG. 1, it should be noted that the steps of the method of FIG. 3 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 determined according to the method described with reference to FIG. 2. In the test to determine the first respiratory rate in step 225, the user may be prompted to breathe normally. In the test to determine the second respiratory 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 respiratory rate to the first respiratory rate. Next, in step 240, the computer-implemented method includes determining whether the calculated frequency ratio is greater than a predetermined threshold. If it is determined that the calculated frequency ratio is greater than the predetermined threshold, in step 245, the computer-implemented method includes outputting an indication of the presence of SMA. If it is determined that the calculated frequency ratio is 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.

[0041] For example, the predetermined threshold may be 0.71. That is, a frequency ratio greater than 0.71 may indicate that the user has PlwSMA, and a frequency ratio of 0.71 or less may indicate that the user does not have PlwSMA. This may be explained with reference to FIG. 4, which is a plot showing frequency ratios calculated for PlwSMA and healthy individuals. The plot shows that a majority of PlwSMA individuals may have test results with a frequency ratio greater than 0.71, while a majority of healthy individuals may have test results with a frequency ratio of 0.71 or less.

[0042] FIG. 5 illustrates an example of a network architecture and data processing apparatus that may be used to implement one or more exemplary embodiments described herein, such as those described in FIGS. 1 and 2. Various network nodes 303, 305, 307, and 309 may be interconnected via a wide area network (WAN) 301, such as the Internet. Other networks, including private intranets, corporate networks, LANs, wireless networks, and personal networks (PANs), may also or instead be used. Network 301 is illustrative and may be replaced by fewer or additional computer networks. The local area network (LAN) may have one or more of any known LAN topologies and may use one or more of a variety of protocols, such as Ethernet. Devices 303, 305, 307, 309 and other devices (not shown) may be connected to one or more of the networks via twisted pair wire, coaxial cable, optical fiber, radio waves, or other communication media.

[0043] As used herein and shown in the drawings, the term "network" refers not only to a system in which remote storage devices are coupled together through one or more communication paths, but also to stand-alone devices that may be coupled to such a system from time to time with storage capabilities. Thus, the term "network" includes not only a "physical network" but also a "content network" consisting of data—belonging to a single entity—that resides across all physical networks.

[0044] The 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 databases and control software for implementing one or more exemplary embodiments described herein. The data server 303 may be connected to a web server 305 with which users interact and obtain requested data. Alternatively, the data server 303 may itself operate as a web server and be connected directly to the Internet. The data server 303 may be connected to the web server 305 via a network 301 (e.g., the Internet) through a direct or indirect connection, or through some other network. Users may interact with the data server 303 using remote computers 307, 309, for example, by using a web browser to connect to the data server 303 through one or more publicly accessible websites hosted by the web server 305. The client computers 307, 309 may be used in conjunction with the data server 303 to access data stored in the data server 303, or may be used for other purposes. For example, from client device 307, a user may access web server 305 using an internet browser as known in the art or by executing a software application that communicates with web server 305 and / or data server 303 over a computer network (such as the internet). In some cases, client computer 307 may be a smartphone, smartwatch, or other mobile computing device and may implement a diagnostic device such as device 105 shown in FIG. 1. In some cases, data server 303 may implement a server such as server 150 shown in FIG. 1.

[0045] The servers and applications may be combined on the same physical device, maintain separate virtual or logical addresses, or may reside on separate physical devices. Figure 1 shows only one example of a network architecture that may be used, and those skilled in the art will appreciate that the specific network architecture and data processing devices used may vary and are secondary to the functions they provide, as described further herein. For example, the services provided by web server 305 and data server 303 may be combined on a single server.

[0046] Each of the components 303, 305, 307, and 309 may 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 RAM 313, ROM 315, a network interface 317, an input / output interface 319 (e.g., a keyboard, a mouse, a display, a printer, etc.), and 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 directing the data server 303 to perform aspects described herein, and other application software 327 that provides secondary support and / or other functions that may or may not be used in combination with other aspects described herein. The control logic is sometimes referred to herein as data server software 325. The functionality of the data server software may refer to actions or decisions that are made automatically based on rules coded into the control logic, actions or decisions that are made manually by a user by providing input to the system, and / or a combination of automated processing based on user input (e.g., queries, data updates, etc.).

[0047] Additionally, memory 321 may store data used to perform one or more aspects described herein, including first database 329 and second database 331. In some cases, the first database may include a second database (e.g., as another table, report, etc.). That is, information may be stored in a single database or, alternatively, separated into different logical, virtual, or physical databases, depending on the system design. Devices 305, 307, and 309 may have architectures similar to or different from that described with respect to device 303. Those skilled in the art will understand that the functionality of data processing device 303 (or devices 305, 307, and 309) described herein may be distributed across multiple data processing devices, for example, to distribute processing load across multiple computers, to segregate transactions based on geographic location, user access level, quality of service (QoS), etc.

[0048] One or more aspects described herein may be embodied in computer-usable or readable data and / or computer-executable instructions, such as in one or more program modules executed by one or more computers or other devices described herein. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types when executed by a processor in a computer or other device. Modules may be written in source code programming languages ​​that are subsequently compiled for execution, or in scripting languages ​​such as, but not limited to, HTML or XML. Computer-executable instructions may be stored in computer-readable media such as hard disks, optical disks, removable storage media, solid-state memory, RAM, etc. As will be appreciated by those skilled in the art, the functionality of the program modules may be combined or distributed as desired in various embodiments. Furthermore, the functionality may be embodied, in whole or in part, in firmware or hardware equivalents, such as integrated circuits, field-programmable gate arrays (FPGAs), etc. Particular data structures may be used to more effectively implement one or more aspects, and such data structures are contemplated within the scope of the computer-executable instructions and computer-usable data described herein.

[0049] The features disclosed in the foregoing description, or in the following claims, or in the accompanying drawings, and expressed in a specific form or in terms of means for performing a disclosed function or a method or process for obtaining a disclosed result, may be utilized, individually or in any combination of such features, as appropriate, to realize the invention in its various forms.

[0050] While the present invention has been described in conjunction with the foregoing exemplary embodiments, many equivalent modifications and variations will be apparent to those skilled in the art given this disclosure. Accordingly, the exemplary embodiments of the invention described above are considered to be illustrative and not limiting. Various changes may be made to the described embodiments without departing from the spirit and scope of the invention.

[0051] For the avoidance of doubt, any theoretical explanations provided herein are provided for the purpose of enhancing the understanding of the reader, and the inventors do not wish to be bound by any of these theoretical explanations.

[0052] Any section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0053] Throughout this specification, including the claims which follow, unless the context requires otherwise, the words "comprise" and "include", as well as variations such as "comprises", "comprising", and "including", are understood to mean the inclusion of a stated integer or step or steps, but not the exclusion of any other integer or step or steps.

[0054] It should 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" with respect to numerical values ​​is arbitrary and means, for example, + / - 10%.

Claims

1. 1. A diagnostic device configured to measure a respiratory rate of a user, 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 respiratory rate test, the respiratory rate test including: 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 reaches a predetermined point in an inhalation cycle; receiving a plurality of user inputs via the one or more sensors, each user input corresponding to a respective time at which the user is at a predetermined point in time during an inhalation cycle; responsive to receiving each user input, generating a timestamp associated with each said user input; applying a respiration rate model to the data including the plurality of generated timestamps, the respiration rate model calculating the user's respiration rate based on the generated timestamps; outputting the calculated respiratory rate; A diagnostic device that performs the following.

2. The diagnostic device of claim 1 , comprising a smartphone having a display component configured to display the user interface.

3. The device of claim 2 , wherein the display component includes a touchscreen that includes the one or more sensors, and the user input is a screen touch detectable by the one or more sensors.

4. The apparatus of claim 3 , wherein the one or more sensors include a capacitance sensor.

5. 5. The diagnostic device of claim 1, wherein the respiration rate model is further configured to calculate a time difference between two timestamps among the plurality of timestamps and calculate the respiration rate based on the inverse of the time difference.

6. The apparatus of claim 5 , wherein the earlier of the two timestamps immediately precedes the later of the two timestamps.

7. 5. The apparatus of claim 1, wherein the respiratory model is further configured to: calculate a plurality of time differences, each time difference being the time difference between two consecutive timestamps; 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; and calculate the respiratory rate by taking the reciprocal of the average time difference.

8. The time difference is in seconds, 8. The apparatus of claim 1, wherein the respiration rate model is further configured to multiply the inverse of the time difference by 60 to obtain the respiration rate in breaths per minute.

9. A device according to any preceding claim, wherein the predetermined point in the inhalation cycle is the point at which the patient begins to inhale.

10. 10. The apparatus of claim 1, wherein the computer-readable instructions, when executed by the at least one processor, cause the diagnostic device to perform two breathing rate tests, the user being prompted to breathe at a different rate and / or depth in each breathing rate test.

11. 11. The apparatus of 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, the clinical interpretation model outputting an indication of the presence or absence of a myopathy.

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

13. 13. The apparatus of claim 12, wherein the two respiratory rates include a first respiratory rate and a second respiratory rate, the frequency ratio corresponding to a ratio of the second respiratory rate to the first respiratory rate, the first respiratory rate corresponding to a respiratory rate test in which the user is encouraged to breathe normally, and the second respiratory rate test corresponding to a test in which the user is encouraged to breathe deeply.

14. 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 myopathy based on the comparison.

15. 15. The apparatus of claim 14, wherein the clinical interpretation model is configured to determine whether the frequency ratio is greater than a predetermined threshold, and to output an indication of the presence of the myopathy if the frequency ratio is determined to be greater than the predetermined threshold, and to output an indication of the absence of the myopathy if the frequency ratio is determined to be equal to or less than the predetermined threshold.

16. 16. The apparatus of claim 15, wherein the predetermined threshold is at least 0.6 and not more than 0.

8.

17. 1. A computer-implemented method for measuring a patient's respiratory rate, comprising: prompting the subject via a user interface to provide input via one or more sensors each time the subject reaches a predetermined point in an inhalation cycle; receiving a plurality of user inputs via the one or more sensors; responsive to receiving each user input, generating a timestamp associated with each said user input; applying a respiration rate model to the data including the plurality of generated timestamps, the respiration rate model being configured to calculate the user's respiration rate based on the generated timestamps; outputting the calculated respiratory rate; 11. A computer-implemented method comprising:

18. 20. The computer-implemented method of claim 17, further comprising applying a clinical interpretation model to the calculated respiratory rate, the clinical interpretation model outputting an indication of the presence or absence of myopathy or an indication of progression of myopathy.

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

20. the steps of prompting the subject, receiving the user input, and generating the timestamp are performed by a processor of a diagnostic device, and the step of applying the respiration rate model is performed by a processor of a server, the diagnostic device being configured to transmit the generated timestamp to the server, the diagnostic device: 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 respiratory rate test, the respiratory rate test including: 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 reaches a predetermined point in an inhalation cycle; receiving a plurality of user inputs via the one or more sensors, each user input corresponding to a respective time at which the user is at a predetermined point in time during an inhalation cycle; in response to receiving each user input, generating a timestamp associated with each said user input; 19. The computer-implemented method of claim 17 or 18,

Images