System and method for determining the degree of respiratory effort exerted by a patient while breathing

Abstract

This invention is a respiratory monitoring device that uses two or more sensors to map a patient's respiratory movements to interpret them as respiratory effort and severity scores. The core components of this invention are contact-based sensors that measure relative movements of the chest, abdomen, and / or other key anatomical features; a processing unit that acquires data from all sensors; an algorithm that analyzes and compares data from each sensor to understand the relative movements and interpret them as clinically relevant information; and a display screen that shares this information with the clinician. These sensors are interconnected and connected to the information processing unit, which shares data with the screen to display a respiratory severity score based on analysis of respiratory effort using a thoracoabdominal asynchronous (TAA) or similar indicators measured by the sensor network and analyzed by the algorithm.

Description

[0001] Related Applications

[0002] This application is an International Application (PCT) of and claims priority to U.S. Provisional Patent Application No. 62 / 944,355, filed December 5, 2019, entitled “RESPIRATORY SEVERITY ASSESSMENT USING MOTION-BASED SENSING,” and U.S. Provisional Patent Application No. 63 / 094,056, filed October 20, 2020, entitled “SYSTEMS, DEVICES, AND METHODS FOR THORACOABDOMINAL ASYNCHRONY-BASED RESPIRATORY EFFORT ASSESSMENT IN PATIENTS,” both of which are incorporated herein in their entirety. BACKGROUND

[0003] Respiratory diseases are a leading cause of morbidity and mortality in children and adults worldwide. These diseases include respiratory distress syndrome (RDS), acute respiratory distress syndrome (ARDS), pediatric acute respiratory distress syndrome (PARDS), asthma, and upper and lower respiratory tract infections such as croup, bronchiolitis, and pneumonia. Respiratory distress is of great concern in pediatric intensive care unit (PICU) patients who are not admitted for respiratory diseases, as unrecognized respiratory failure is a leading cause of cardiopulmonary arrest in infants; and respiratory arrest is a leading cause of death in adults. Early identification and treatment are critical to reduce morbidity and mortality. Thus, respiratory monitoring for ensuring proper utilization of respiratory support is a key area of interest for general and ICU clinicians.

[0004] Traditionally, direct and indirect methods have been used to assess the respiratory effort exerted by a patient. The most direct assessment of respiratory effort is to calculate respiratory work, or the total energy expenditure associated with breathing, which can be calculated as the integral of the product of respiratory volume and pressure. Esophageal manometry, defined as the measurement of pressure through a balloon catheter placed in the esophagus of a patient, is considered the gold standard for the minimally-invasive, quantitative assessment of respiratory effort through respiratory work calculation; however, esophageal manometry has not been widely adopted in clinical practice due to poor clinician interpretability.

[0005] Less direct methods for measuring respiratory work rely on assessing conditions such as respiratory effort or respiratory distress or dyspnea when the patient is at rest, the patient's use of accessory respiratory muscles, and the measurement of abnormal movement of the patient's abdomen in a qualitative or semi-quantitative manner. One example of an existing clinical standard for objective clinical assessment of respiratory distress in children and infants is known as the Silverman Andersen Respiratory Severity Score (RSS). The RSS is a semi-quantitative assessment of five parameters related to respiratory work that has been pioneered for use in resource-poor settings. Based on a total severity rating of the five parameters, i.e., ratings of 0, 1, or 2, the RSS score ranges from 0 to 10. However, like many clinical assessment guidelines, this metric suffers from poor inter-observer variability that can only be corrected by continuous, extensive training to maintain assessment skills. Additionally, the assessment relies on the availability of medical professionals and direct observation, and does not allow for continuous monitoring, which can impact the ability to detect increased respiratory effort at the onset of respiratory effort and intervene in a timely manner.

[0006] Figure 1 A table is provided of currently available tools that indirectly assess the degree of effort a patient expends for breathing through mechanical, acoustic, and / or electrical sensing devices along with a description of their respective functionality and limitations. While each of these tools has the ability to measure one or more characteristics of respiratory effort, each tool has unique limitations related to accuracy, the ability to link the monitored data to respiratory effort, and commercial availability that limit the respective usefulness and accuracy of these tools, particularly in a clinical setting. BRIEF DESCRIPTION OF DRAWINGS

[0007] Figure 1 A table is provided of currently available tools that indirectly assess the degree of effort a patient expends for breathing through mechanical, acoustic, and / or electrical sensing devices along with a description of their respective functionality and limitations according to embodiments of the present invention;

[0008] Figure 2A A graph is provided showing a first chest signal aligned in time with a first abdominal signal according to embodiments of the present invention;

[0009] Figure 2B A graph is provided showing a second chest signal aligned in time with a second abdominal signal according to embodiments of the present invention;

[0010] Figure 2C A graph is provided showing a third chest signal aligned in time with a third abdominal signal according to embodiments of the present invention;

[0011] Figure 3AAn exemplary system according to embodiments of the application is presented that can be configured to perform one or more of the methods disclosed herein;

[0012] Figure 3B is a block diagram illustrating an exemplary computer system according to embodiments of the application;

[0013] Figure 4A is an illustration of an exemplary sensor array according to embodiments of the application comprising three sensor modules;

[0014] Figure 4B is an illustration of another exemplary sensor array according to embodiments of the application;

[0015] Figure 4C An exploded view of a sensor array according to embodiments of the application is provided;

[0016] Figure 4D is an illustration of a patient having a sensor array positioned thereon according to embodiments of the application;

[0017] Figure 5A is a flowchart illustrating exemplary steps of a process for determining a patient's respiratory rate according to embodiments of the application;

[0018] Figure 5B depicts a waveform plot that can represent received sensor data according to embodiments of the application;

[0019] Figure 6 is a flowchart illustrating exemplary steps of a process for determining a patient's TAA, a degree of respiratory distress exhibited by the patient, and / or a respiratory distress score for the patient according to embodiments of the application;

[0020] Figure 7 is a flowchart illustrating exemplary steps of another process for determining a patient's TAA, a degree of respiratory distress exhibited by the patient, and / or a respiratory distress score for the patient according to embodiments of the application;

[0021] Figure 8 A plot is provided in which a phase shift between filtered amplitudes of a Hilbert transform of data received from a sensor placed proximate to a patient's navel or abdomen according to embodiments of the application is 0°;

[0022] Figure 9 A plot is provided illustrating a motion capture test according to embodiments of the application;

[0023] Figure 10 is a flowchart illustrating exemplary steps of a process for gathering information about a patient's chest and portions thereof as they move over time when breathing according to embodiments of the application;

[0024] Figure 11 Examples of images according to embodiments of the present invention are provided, which may be received in the step of depicting different locations of a patient's chest cavity or thoracic cavity in the form of different markings in the form of dots drawn on the patient and labels erected on the patient;

[0025] Figure 12 Exemplary graphs are provided, illustrating three Lissajous curves showing the changes in abdominal movement with rib cage movement according to an embodiment of the present invention.

[0026] Figure 13 A bar graph showing the average peak-to-peak amplitude during normal breathing with severe respiratory distress and breathing after recovery from severe respiratory distress, according to an embodiment of the present invention, is provided.

[0027] Figure 14 This is a flowchart illustrating exemplary steps of a process for treating a patient with respiratory distress according to an embodiment of the present invention. Summary of the Invention

[0028] A system for monitoring a patient's respiratory system may include: a first sensor communicatively coupled to a processor and configured to be positioned on the patient's chest and capture chest movements; a second sensor communicatively coupled to the processor and configured to be positioned near the patient's xiphoid process and capture xiphoid movements; a third sensor communicatively coupled to the processor and configured to be positioned on the patient's abdomen and capture abdominal movements; and a power source for providing power to the first, second, and third sensors. The first, second, and / or third sensors may be, for example, accelerometers, force sensors, and / or strain gauges.

[0029] In some embodiments, the system further includes a controller communicatively coupled to at least one of the first sensor, the second sensor, and the third sensor, as well as the processor. The controller may be configured to, for example, extract motion measurements, acceleration measurements, force measurements, strain measurements, respiratory rate, and / or the degree of thoracoabdominal asynchrony (TAA) exhibited by the patient, and transmit the extracted motion measurements, acceleration measurements, force measurements, strain measurements, respiratory rate, and / or TAA degree to the processor.

[0030] Alternatively or concurrently, the system may include a first wire mechanically and electrically coupling the first and second sensors together, and a second wire mechanically and electrically coupling the second and third sensors together. In some cases, the lengths of the first and / or second wires may be adjusted, for example, via a retractable spool or, where expandable wires may be used.

[0031] In some embodiments, the system's processor may communicate with a memory storing an instruction set that, when executed by the processor, causes the processor to perform multiple steps, such as receiving a first sensor dataset from a first sensor located at a first position on the patient's skin; receiving a second sensor dataset from a second sensor located at a second position on the patient's skin; determining the phase difference between the first sensor dataset and the second sensor dataset and / or performing a cross-correlation analysis on the first sensor dataset and the second sensor dataset; determining the patient's level of respiratory effort based on the determined phase difference between the first sensor dataset and the second sensor dataset and / or the result of the cross-correlation analysis; and transmitting the level of respiratory effort to a display device. In some embodiments, the processor of the system may further perform the following operations: receiving a third sensor dataset from a third sensor located at a third position on the patient's skin, the third sensor communicating with the processor; determining the phase difference between at least one of the first sensor dataset and the third sensor dataset and / or the second sensor dataset and the third sensor dataset, and / or performing a cross-correlation analysis on the first sensor dataset and the third sensor dataset and / or the second sensor dataset and the third sensor dataset; and determining the degree of respiratory effort exhibited by the patient based on the determined phase difference between the first sensor dataset and the third sensor dataset and / or the second sensor dataset and the third sensor dataset, and / or based on the result of the cross-correlation analysis.

[0032] An exemplary method executed by a processor when using the present invention includes: receiving a first sensor dataset from a first sensor located at a first location on the patient's skin; receiving a second sensor dataset from a second sensor located at a second location on the patient's skin; determining a phase difference between the first sensor dataset and the second sensor dataset; determining the degree of respiratory effort exhibited by the patient based on the determined phase difference between the first sensor dataset and the second sensor dataset; and transmitting the degree of respiratory effort to a display device. In some embodiments, determining the degree of respiratory effort exhibited by the patient may include determining the degree of thoracoabdominal asynchrony (TAA) exhibited by the patient. The first location may be the patient's chest or near the patient's xiphoid process, and the second location may be near the patient's xiphoid process or abdomen.

[0033] Sometimes, the first sensor dataset and / or / (multiple) second sensor datasets may be preprocessed or filtered (e.g., bandpass filtered) before determining the phase difference. The first and second sensors may be, for example, accelerometers, and the first and second sensor datasets include acceleration measurements. Alternatively or alternatively, the first and second sensors may be force gauges, and the first and second sensor datasets include force measurements. Alternatively or alternatively, the first and second sensors may be strain sensors, and the first and second sensor datasets include strain measurements.

[0034] In some embodiments, an indication of the patient’s respiratory rate may be received, and the determination of the degree of respiratory effort exhibited by the patient may be further based on the respiratory rate.

[0035] In some embodiments, a third sensor dataset can be received from a third sensor located at a third position on the patient. The phase difference between the first sensor dataset and the third sensor dataset and / or the second sensor dataset and the third sensor dataset can then be determined, and the determination of the degree of respiratory effort exhibited by the patient can be further based on the determined phase difference between the first sensor dataset and the third sensor dataset and / or the second sensor dataset and the third sensor dataset.

[0036] In some embodiments, a cross-correlation analysis between the first sensor dataset and the second sensor dataset can be performed before determining the level of respiratory effort exhibited by the patient, wherein the level of respiratory effort exhibited by the patient can be further based on the results of the cross-correlation analysis.

[0037] In some embodiments, the first sensor dataset and the second sensor dataset may be signals collected over a period of time, and the results of cross-correlation calculations at specific times during that period may be mapped to the maximum theoretical cross-correlation value or maximum cross-correlation value calculated during that period before determining the degree of respiratory effort exhibited by the patient.

[0038] Alternatively or concurrently, a video recording of the patient's chest cavity over a period of time during which the patient may breathe can be received, allowing observation and / or measurement of movement of the patient's chest cavity or portions thereof. Sometimes, movement may be a relative movement of the patient's chest cavity during which the patient may breathe. In some embodiments, the video recording is a three-dimensional video recording. Optionally, in some cases, the patient's chest cavity skin may be marked with a first marker located at a first location on the patient's skin (e.g., the chest or xiphoid process) and a second marker located at a second location on the patient's skin (e.g., the xiphoid process or abdomen), but this is not always the case. Exemplary markers include dots or graphics drawn on the patient's skin, stickers, LEDs, and transmissive markers. The video can then be analyzed to determine the positional changes of the first and second markers over the time period, and a first waveform showing the positional changes of the first marker over the time period and a second waveform showing the positional changes of the second marker over the time period can be formed or generated. In some cases, the first and / or second waveforms may be sinusoidal. The phase difference between the first and second waveforms can be determined, and the determined phase difference can be used to further determine the degree of respiratory effort exhibited by the patient. Alternatively or alternatively, a first and second waveform can be used to perform a cross-correlation analysis, and the results of the cross-correlation analysis can be used to further determine the patient's demonstrated respiratory effort. The respiratory effort can then be transmitted to a display device as, for example, a respiratory effort score, a respiratory distress severity score, or other indicators of respiratory effort. In some cases, determining the patient's demonstrated respiratory effort may include determining the degree of thoracoabdominal asynchrony (TAA). Alternatively or alternatively, an indication of the patient's respiratory rate and a determination of the patient's demonstrated respiratory effort can be further based on that respiratory rate.

[0039] In some embodiments, a cross-correlation analysis between the first sensor dataset and the second sensor dataset may be performed before determining the patient's demonstrated respiratory effort, wherein the patient's demonstrated respiratory effort may be further based on the results of the cross-correlation analysis. In these embodiments, the first sensor dataset and the second sensor dataset may be signals collected over a period of time, and the results of cross-correlation calculations at specific times within that period may be mapped to the maximum theoretical cross-correlation value or maximum cross-correlation value calculated during that period before determining the patient's demonstrated respiratory effort. Detailed Implementation

[0040] The management of COVID-19-associated respiratory distress must consider all invasive and non-invasive ventilation options, as the resources consumed by prolonged ICU bed use and mechanical ventilation may not be readily available in a confined environment. Physicians must also balance the risk of ventilator-induced lung injury with the extubation challenges of prolonged ventilator use with the risk of poor outcomes from inappropriately delayed intubation. The decision to intubate or provide less invasive forms of respiratory support is often complicated by the degree of variability in the performance of patients with similar levels of respiratory function. Recent guidance on COVID-19 management suggests that non-invasive support, such as BiPAP, CPAP, or HFNC, may be provided to some patients, but close monitoring for signs of worsening respiratory effort is essential, such as signs of increased work of breathing in hypoxia, use of accessory muscle control, and tachypnea.

[0041] While esophageal manometry is widely recognized as the gold standard for deriving the work of breathing from respiratory pressure, its clinical applicability is limited due to its invasiveness and the limited interpretability of its output measurements. A clinical indicator considered a marker of respiratory effort (also referred to as “work of breathing” in this text) is thoracoabdominal asynchrony (TAA), i.e., the inconsistent movement of the rib cage and abdomen during respiration. In healthy patients, the chest wall and abdomen expand and contract synchronously during respiration; when a patient experiences respiratory distress, the asynchronous movement of the chest and abdomen becomes increasingly pronounced. In the worst-case scenario, the rib cage and abdomen move according to a 180° out-of-phase periodic function, a phenomenon known as “seesaw” breathing.

[0042] In addition to escalation guidance, having a feedback mechanism to guide de-escalation of respiratory support is crucial for the successful and efficient treatment of COVID-19 patients. Successful extubation is particularly important in COVID-19 management due to the risk of aerosolization during repeated intubation-extubation cycles. Monitoring real-time changes in TAA can play a vital role in guiding ventilatory support weaning. Recent extubation protocols for COVID-19 patients recommend monitoring signs such as TAA during spontaneous breathing trials (SBT) to ensure the success of SBT during the weaning process. This monitoring is particularly important for high-risk patients where weaning may be more challenging. Among these risk factors, obesity is a comorbidity affecting up to half of adult COVID patients. Obesity can limit ventilation by impairing diaphragmatic movement, weakening the immune response to viral infection, promoting pro-inflammatory states, and inducing oxidative stress that can adversely affect cardiovascular function. Importantly, elevated TAA has been shown in subjects with significant abdominal obesity, increasing the risk of hypoxic ventilation-perfusion mismatch and impaired gas exchange.

[0043] Clinical standards for TAA monitoring involve periodic visual observation by members of the respiratory care team. This subjective assessment practice can be affected by poor inter-observer variability. For COVID-19 and all acute respiratory illnesses, reliable, objective assessment tools for continuous monitoring of respiratory effort can allow for a more comprehensive understanding of a patient's real-time respiratory status and provide additional indications or contraindications for using different levels of ventilatory support.

[0044] Figures 2A-2C Graphs 210, 220, and 230 are provided, illustrating sinusoidal signals (sometimes referred to herein as "chest signals") from a sensor located on the patient's chest (labeled "C" on the graph), sinusoidal signals (sometimes referred to herein as "abdominal signals") from a sensor located on the patient's abdomen (labeled "A" on the graph), and a composite graph showing the first (chest) sinusoidal signal superimposed on the second (abdominal) signal so that, for example, the phase difference (Ø) between them can be observed or determined. The maximum amplitude of each oscillation of the chest and abdominal signals is marked with arrows. Furthermore, the chest and abdominal sinusoidal signals are aligned in the time domain such that they correspond to each other in time (e.g., have the same start and end times and advance at the same rate in time). More specifically, Figure 2A A graph 210 is provided, showing the temporal alignment of the first chest signal 240A with the first abdominal signal 245A. Graph 210 also provides a composite signal 250A, the superposition of the first chest signal 240A and the first abdominal signal 245A. The first chest signal 240A and the first abdominal signal 245A are highly correlated (i.e., highly cross-correlated), such that the phase difference (Ø) between them is approximately 0°. Because of the high correlation between the first chest signal 240A and the first abdominal signal 245A, patients associated with both the first chest signal 240A and the first abdominal signal 245A exhibit little or no TAA and show little or normal effort during respiration.

[0045] More specifically, Figure 2B Graph 220 is provided, showing the temporal alignment of the second chest signal 240B with the second abdominal signal 245B. Graph 210 also provides a composite signal 250B, the superposition of the second chest signal 240B and the second abdominal signal 245B. The correlation between the second chest signal 240B and the second abdominal signal 245B is low (i.e., low cross-correlation), resulting in a phase difference (Ø) of approximately 90° between them. Because there is no high correlation between the second chest signal 240B and the second abdominal signal 245B, patients associated with the second chest signal 240B and the second abdominal signal 245B exhibit some degree of TAA, show more effort during respiration, and may be experiencing some degree of respiratory distress.

[0046] More specifically, Figure 2C Graph 230 is provided, showing the temporal alignment of the third chest signal 240C with the third abdominal signal 245C. Graph 210 also provides a composite signal 250C, the superposition of the third chest signal 240C and the third abdominal signal 245C. The third chest signal 240C and the third abdominal signal 245C are uncorrelated (i.e., there is no cross-correlation), resulting in a phase difference (Ø) of approximately 180° between them. Because there is no correlation between the third chest signal 240C and the third abdominal signal 245C, patients associated with both the third chest signal 240C and the third abdominal signal 245C exhibit severe TAA and may exert extreme effort during respiration, and may be in severe respiratory distress.

[0047] In healthy patients, the chest wall and abdomen expand and contract synchronously during respiration with a high degree of cross-correlation and a phase difference of approximately 0°, such as Figure 2A The first composite signal 250A of the first curve 210 is shown. When the patient experiences respiratory distress, the asynchronous movements between the chest and abdomen become increasingly apparent, as shown in... Figure 2B As can be seen in graph 220, and more specifically in the second composite signal 250B, the frequencies of chest movements are 90° out of phase with the frequencies of abdominal movements. In the worst performance, chest and abdominal movements become completely asynchronous, or exhibit a low cross-correlation of approximately 180° out of phase with each other, as shown in... Figure 2C This can be seen in the third composite signal 250C of curve 230. This asynchronous breathing phenomenon (such as...) Figure 2C (As shown) This is sometimes referred to as "seesaw breathing".

[0048] Asynchronous breathing is a symptom of respiratory distress in all types of patients, regardless of factors such as age, body size, body mass index, waist circumference, chest circumference, and / or sex. However, in some cases, the level or extent of asynchronous breathing may depend on the patient's physiological characteristics and may not be caused by respiratory distress (e.g., patients with a higher BMI or a larger layer of fat near the abdomen may have blurred limits of movement in parts of the abdomen or chest, and in some cases may not exhibit the same significant asynchrony as individuals with a lower BMI or a smaller layer of fat). For example, in adult patients with a relatively large layer of adipose tissue on or around the abdomen, this layer of adipose tissue may exert some compression on the diaphragm, which may result in a degree of asynchronous breathing not caused by respiratory distress. However, when such patients are in or may be in this state of respiratory distress, the systems and processes described herein can adjust measurements and other analyses to correct for adipose tissue located on or around the abdomen.

[0049] Therefore, the determination of the severity of a patient’s asynchronous breathing can be absolute (e.g., measured relative to a known baseline or baseline group) or relative to the patient’s breathing pattern when healthy and his or her breathing pattern when ill, or absolute.

[0050] Figure 3A An exemplary system 300 is presented, which can be configured to perform one or more methods disclosed herein. In some cases, system 300 (or portions thereof) can collect data that can be used to assess a patient's respiratory effort and determine the patient's respiratory distress (e.g., respiratory distress score). System 300 includes: a sensor array 310 configured to measure chest and abdominal movements during respiration; and a controller 320 configured to receive data from sensor array 310, extract, for example, movement, TAA, and / or respiratory rate from the sensor array data, and provide the extracted data to a computer system 330, which in many cases includes a display interface to visualize the data for user viewing. In some embodiments, controller 320 may be a microcontroller. System 300 may also include a power supply 360 that can be electrically coupled to one or more components of system 300. Power supply 360 can be configured to provide power to one or more components of system 300. Exemplary power supplies include, but are not limited to, batteries and mechanisms for plugging into a wall power outlet and drawing power from a mains power source.

[0051] Sensor array 310 may include multiple (e.g., 2 to 10) sensors that can be configured to sense patient movement. Exemplary sensors included in sensor array 310 include, but are not limited to, accelerometers (e.g., 2D and / or 3D accelerometers), force gauges, and / or strain-based sensors (sometimes referred to as strain gauges). An exemplary accelerometer that may be included in sensor array 310 is an Invensense ICM-20602 6-axis gyroscope and / or an accelerometer with an acceleration sensitivity of ±2 g, ±4 g, ±8 g, or ±16 g. Exemplary strain-based sensors include a piezoresistive metal film disposed in a substrate such as a silicone or rubber elastomer substrate.

[0052] The controller 320 can be configured to sample data from the sensor array 310 at any preferred rate (e.g., 4 kHz or lower) that allows for the measurement of minute (e.g., 0.1 mm to 5 mm) patient movements. In some embodiments, the controller 320 can be used to sample data from the sensor array 310 in accordance with Article I. 2A sensor array 310 using the C-communication protocol collects sensor data, and the controller can receive acceleration data from each accelerometer at an exemplary frequency of, for example, 30.5 Hz. The controller 320 can then transmit the sampled accelerometer data to a PC for processing according to one or more procedures described herein. Components of system 300 can communicate via wired and / or wireless means, and in some embodiments, communication networks such as the Internet can be used.

[0053] In some embodiments, the sensors and / or controllers 320 of the sensor array 310 may be physically / electrically coupled to each other and / or coupled to other components of the system 300. Alternatively or additionally, one or more sensors and / or controllers 320 of the sensor array 310 may be wirelessly coupled to each other and / or coupled to other components of the system 300 via, for example, a wireless or near-field communication protocol (e.g., BLUETOOTH™). When the sensor and / or controller wires 320 of the sensor array 310 are configured for wireless communication, they may include wireless antennas and / or transceivers (not shown).

[0054] System 300 may further include: a database 340 configured to store data received by computer system 330; a display device 350 communicatively coupled to computer system 330; and a camera 360, which may be a video camera configured to capture video images of a patient breathing. In one embodiment, camera 360 is a high-speed camera configured to capture, for example, 1,500 to 3,000 frames per minute. Two or more components of system 300 may be communicatively coupled to each other via, for example, a network 305 (such as the Internet).

[0055] Figure 3BThis is a block diagram illustrating an exemplary computer system 370, which includes a bus 372 or other communication mechanism for transmitting information, and a processor 374 coupled to the bus 372 for processing information. The computer system 370 also includes main memory 376 (such as random access memory (RAM) or other dynamic storage device) coupled to the bus 372 for storing information and instructions to be executed by the processor 374. Main memory 376 can also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processor 374. The computer system 370 further includes read-only memory (ROM) 378 or other static storage device coupled to the bus 372 for storing static information and instructions for the processor 374. Storage device 380, such as a hard disk, a flash memory-based storage medium, or other storage medium that the processor 374 can read from, is provided and coupled to the bus 372 for storing information and instructions (e.g., operating system, applications, etc.).

[0056] Computer system 370 may be coupled to display 382 (such as a flat panel display) via bus 372 for displaying information to a computer user. Input device 384 (such as a keyboard including alphanumeric keys and other keys) may be coupled to bus 372 for transmitting information and command selections to processor 374. Another type of user input device is cursor control device 386 (such as a mouse, trackpad, or similar input device) for transmitting directional information and command selections to processor 374 and for controlling cursor movement on display 382. Other user interface devices such as microphones and speakers, not shown in detail, may be involved in the reception of user input and / or the presentation of output.

[0057] The processes described herein can be implemented by a processor 374 executing a series of appropriate computer-readable instructions contained in main memory 376. These instructions can be read into main memory 376 from another computer-readable medium, such as storage device 380, and execution of the series of instructions contained in main memory 376 causes the processor 374 to perform associated actions. In alternative embodiments, the invention can be implemented using a hardwired circuitry system or firmware-controlled processing unit instead of or in combination with the processor 374 and its associated computer software instructions. These computer-readable instructions can be rendered in any computer language.

[0058] Generally, all the above process descriptions are intended to cover any series of logical steps executed sequentially to achieve a given purpose, which is a hallmark of any computer-executable application. Unless otherwise stated, it should be understood that throughout this specification, the use of terms such as “processing,” “operation,” “calculation,” “determining,” “displaying,” “receiving,” and “transmitting” refers to the actions and processes of a suitably programmed computer system (such as computer system 370) or similar electronic computing device that manipulates and converts data represented as physical (electronic) quantities in its registers and memories into other data similarly represented as physical quantities within its memory or registers or other such information storage, transmission, or display devices.

[0059] Computer system 370 also includes a communication interface 388 coupled to bus 372. Communication interface 388 can provide a bidirectional data communication channel to a computer network, providing connectivity to the various computer systems discussed above, as well as connectivity between these computer systems. For example, communication interface 388 could be a local area network (LAN) card providing a data communication connection to a compatible LAN, which itself is communicatively coupled to the Internet via one or more Internet service provider networks. The precise details of this communication path are not important to the present invention. What is important is that computer system 370 can send and receive messages and data through communication interface 388 and communicate with hosts accessible via the Internet in this manner. It should be noted that components of system 370 can reside in a single device or in multiple physically and / or geographically distributed devices.

[0060] Figure 4A This is an illustration of an exemplary sensor array 310, which includes three sensor modules 420: a first sensor module 420A, which can be configured to be positioned on a patient's chest (and may be referred to herein as a "chest sensor"); a second sensor module 420B, which can be configured to be positioned on a patient's xiphoid process (and may be referred to herein as a "xiphoid process sensor"); and a third sensor module 420C, which can be configured to be positioned on a patient's abdomen (and may be referred to herein as an "abdominal sensor"), for example... Figure 4CAs shown. The first sensor 420A, the second sensor 420B, and the third sensor 420C can be, for example, accelerometers, strain gauges, and / or force gauges, which are physically and electrically coupled (in series and / or in parallel) to each other via multiple (e.g., 4, 8, 10) wires that may be included in one or more cables 440. Individual wires / cables 440 may be soldered to leads provided by the first sensor 420A, the second sensor 420B, and the third sensor 420C. Although shown as wired, the first sensor 420A, the second sensor 420B, and the third sensor 420C may be configured in some cases to transmit signals wirelessly or with different numbers of wires. The sensor array 310 may be coupled to the controller 320 via the wires 440. In some cases, the wires 440 may be long enough to accommodate placing the controller at a preferred distance (e.g., 10 feet or 15 feet) from the patient where the sensor array 310 is placed. In some embodiments, the size of the wire / cable 440 may be suitable for different body shapes (e.g., infants, children, teenagers, and adults). Additionally or alternatively, the length of the wire / cable 440 may be adjusted via a spool or retraction mechanism present in the housing of one or more of the sensors 420, which facilitates the extension and / or retraction of the wire / cable 440 to accommodate different body shapes. In some embodiments, the wire / cable 440 may be flexible and / or the attachment mechanism between the wire / cable 440 and the sensor is flexible.

[0061] Figure 4B This is an illustration of another exemplary sensor array 310, including three sensor modules 420 similar to those shown in 4A. Besides Figure 4A In addition to the components of the sensor array shown, Figure 4B The sensor array 310 also includes a wire extension mechanism 455, which can be configured to allow the length of the wire / cable 440 to be adjusted via, for example, contraction and / or expansion through a spool or elastic mechanism.

[0062] Figure 4C An exploded view of sensor array 310 is provided, wherein each sensor 420 includes a set of sensor circuitry and / or mechanical devices 425 configured to, for example, sense motion or acceleration, a removable adhesive patch 450 configured to adhere to a patient's skin, a clamp 460 that can be attached to, for example, an electrocardiogram (ECG) pad, and a housing 470 that houses the clamp 460 and the sensor circuitry and / or mechanical devices 425. The sensor circuitry and / or mechanical devices 425 may be, for example, a printed circuit board, which in some cases includes a MEMS IMU and supporting hardware, force sensor devices, stress gauges, and / or accelerometers. Figure 4DThe sensor array 310 also shows the length of the wire 440.

[0063] The spacing of the sensors 420 and / or sensor circuitry and / or mechanical devices 425 can be configured to align with anatomical measurements of the distance between the patient's chest and xiphoid process, and between the xiphoid process and the apex of the abdomen, and can have different lengths to accommodate different ages and body types, such as children aged 1 to 5 years, adolescents aged 13 to 15 years, or adults (18 to 90 years). In some cases, the length of one or more wires 440 can be adjustable to accommodate, for example, different body types / shapes. For example, the housing for one or more sensors 420 can include a mechanism (e.g., a spool) capable of retracting one or more wires 440 into the housing. Additionally or alternatively, components of the sensor array can be resilient or otherwise configured to expand or contract such that the positioning between the sensors 420 can adapt to an individual's physiology. In some embodiments, a first accelerometer 420 can be configured to be placed on the wearer's chest (e.g., at the midpoint between the patient's nipples), a second accelerometer 420 can be configured to be placed on the patient's xiphoid process, and a third accelerometer 420 can be configured to be placed on the patient's abdomen.

[0064] Figure 4D This is an illustration of a patient 480 on which the sensor array 310 is positioned. In some cases, if the user (e.g., a healthcare provider) attaches the first sensor 420A at the midpoint between the nipples, the second sensor 420B to the skin near the patient's xiphoid process, and the third sensor 420C to the abdomen (approximately 1 to 3 inches above the navel for pediatric patients or approximately 2 to 6 inches above the navel for adult patients), the sensor array 310 can be positioned on the patient 480.

[0065] Figure 5A This is a flowchart illustrating exemplary steps of a process 500 for determining a patient's respiratory rate. Process 500 can be performed by, for example, system 300 or any component thereof.

[0066] Initially, in step 505, sensor data can be received in the form of, for example, a waveform 530 having multiple peaks 540, such as... Figure 5B As shown. Typically, the received sensor data comes from an abdominal sensor such as the abdominal sensor 420C. Sensor data may be timestamped and / or divided into multiple time windows, examples of which are shown in [examples of time windows are provided]. Figure 5B The data is shown as a first time window 535a and a second time window 535b. In some embodiments, the sensor data may be filtered using, for example, a bandwidth filter.

[0067] The received sensor data can then be analyzed using, for example, a peak detection function to detect peaks in the sensor data (step 510). These peaks may correspond to the maximum expansion of the abdominal cavity, which occurs once per respiratory cycle and is therefore related to the patient's respiratory cycle. In some cases, peaks in the data can be characterized by threshold separation at multiple points and threshold significance relative to surrounding local maxima, where threshold separation means that each peak is separated by a certain number of points. For example, if a peak is identified at point x and the threshold separation is defined as 10 points, this means that the earliest other peak that can be identified is located at point x + 10. This prevents peaks from being sampled too frequently from the data. Separation can be equivalent to the distance input of the Python function disclosed herein. Threshold significance can provide an indicator of relative amplitude. Noise within a signal has a certain typical amplitude, and the signal content of interest (e.g., respiratory amplitude or number of breaths in a sample) may have common or typical amplitudes. Setting a significance threshold allows setting the degree of "significance" of a peak relative to other possible peaks to practically label it as a peak.

[0068] In step 515, the duration for separating each pair of consecutive peaks can be determined for multiple peaks and / or time windows. Then, an average time value for separating the peaks can be determined (step 520) and this average time value can be converted into a respiratory rate (step 525), wherein, for example, the average number of peaks within a given time window (e.g., 1 minute) corresponds to the number of breaths per minute (i.e., respiratory rate).

[0069] Figure 6 This is a flowchart illustrating exemplary steps of process 600 for determining a patient's TAA, the degree of respiratory distress exhibited by the patient, and / or the patient's respiratory distress score. These determinations can be performed on, for example, a periodic, on-demand, and / or continuous basis. Process 600 can be performed by, for example, system 300 or any of its components (such as sensor array 310).

[0070] Initially, the first sensor dataset and the second sensor dataset may be received by a processor or a computer, such as computer system 330 (step 605). In some embodiments, the first sensor dataset and the second sensor dataset are like... Figures 2A-2CThe waveforms shown are examples of waveforms. Sensor data can be received from, for example, a controller such as controller 320 and / or sensors such as first sensor 420A, second sensor 420B and / or third sensor 420C. Sensor data can correspond to, for example, acceleration data, force measurement results, strain measurement results and / or measured changes in diameter of, for example, the patient's chest cavity, sternum, xiphoid process region and / or abdomen, and can be acquired over time (e.g., 30s, 1 minute, 5 minutes, etc.). Sometimes, data corresponding to multiple measurement results can be received in step 605. For example, in step 605, data corresponding to a measurement result acquired at the patient's chest can be received from, for example, first sensor 420A, data corresponding to a measurement result acquired at the patient's xiphoid process can be received from, for example, second sensor 420B, and / or data corresponding to a measurement result acquired at the patient's abdomen can be received from, for example, third sensor 420C. In some embodiments, different types of data corresponding to measurement results acquired from a specific location (e.g., chest, xiphoid process and / or abdomen) can be received in step 605. For example, data corresponding to one or more acceleration, force, and / or strain measurements at specific locations on the patient's chest can be received, thereby allowing various types of measurements to be used to verify and / or establish a confidence level of accuracy determined using the received data.

[0071] The received sensor data can then be filtered, analyzed, and / or preprocessed (step 610). In some cases, the analysis and preprocessing in step 610 may include, for example, filtering the data using a Hilbert transform filter and / or performing phase shift analysis to determine the phase angle between the resulting functions (step 615).

[0072] The Hilbert transform filter is a mathematical function that can be used to convert a real signal into an analytic signal, which is defined as a signal with no negative frequency components. A continuous-time analytic signal can be expressed as Equation 1, as shown below:

[0073] Equation 1

[0074] in:

[0075] z(t) = Analytical representation

[0076] t = time

[0077] Z(ω) = the complex coefficients of the positive frequency signal, and sets its amplitude and phase;

[0078] ω = frequency

[0079] dω = the derivative of frequency

[0080] A real sine curve can be converted into a positive-frequency complex sine curve by generating a phase quadrature component as the imaginary part; this phase quadrature component is generated by shifting the original signal by 90°. The Hilbert transform filter has the effect of filtering out negative frequencies and creating a gain of 2 for positive frequencies.

[0081] The Hilbert transform can be mathematically explained by the fact that if two signals are perfectly synchronized, the resulting phase angle is close to 0°, while during anomalous motion, the phase angle is close to 180°.

[0082] The Hilbert transform can be mathematically explained by the calculations in equations 2A and 2B, where x(t) is a sinusoidal signal with unit amplitude, frequency ω0, positive frequency component X+, and negative frequency component X-, where:

[0083] Equation 2A

[0084] Equation 2B

[0085] Phase shift of -90° ( ) Applied to positive frequency components (X) + ) and a +90° phase shift ( ) Applied to negative frequency components (X) - ) are represented by equations 3A and 3B respectively.

[0086] Equation 3A

[0087] Equation 3B

[0088] Then, the original component and the shifter component are added together as a single signal (x(t) + jy(t)) to generate equations 4A and 4B, as shown below.

[0089] Equation 4A

[0090] Equation 4B

[0091] When processing discrete-time signals using software such as MATLAB and / or Python script libraries, the Hilbert transform is calculated by first computeding the Fourier transform of the signal. Then, the amplitudes of the negative frequency components of the signal are set to zero. Finally, a new signal is generated by computeding the inverse Fourier transform in the new frequency space.

[0092] Using the Hilbert transform allows for the definition of an approximately sinusoidal signal, such as a respiratory signal, with a single characteristic frequency. In some embodiments, determining the characteristic frequencies of data from two or more locations / sensors on a patient's body (e.g., the xiphoid process and umbilicus) allows for the determination of the phase shift between signals. This can be done after identifying a window of the most recently collected data from which the phase shift can be calculated and normalizing the data by subtracting the mean of the data points contained within the window and dividing by the standard deviation. Once normalized, the data can be transmitted through a Hilbert transform filter and the phase shift can be calculated. Figure 8 A graph 800 is provided, in which the signal received from a sensor (sometimes referred to herein as a third signal) placed near the patient's navel or abdomen is plotted in the complex plane. Figure 8 The data referred to as the umbilical signal (N) is compared with the data received from a sensor placed near the xiphoid process of the patient (sometimes referred to as the second signal in this paper) and... Figure 8 The phase shift Ø between the Hilbert transform filter amplitudes of the data referred to as the xiphoid process signal (X) is given by the formula, where the Y-axis corresponds to the imaginary number (labeled Im on graph 800) and the X-axis corresponds to the real number (labeled Re on graph 800). The umbilical signal (N) can be expressed as Equation 5, as shown below:

[0093] Navel signal = Ne -i(wt + ø) Equation 5

[0094] in:

[0095] N = Amplitude shift of the navel signal

[0096] e = Euler's number (approximately 2.71828)

[0097] ω = frequency

[0098] Ø = Phase shift

[0099] t = time

[0100] The xiphoid process signal can be represented by Equation 6, as shown below:

[0101] Xiphoid process signal = Xe -iwt Equation 6

[0102] in:

[0103] X = Amplitude shift of the xiphoid process signal

[0104] e = Euler's number (approximately 2.71828)

[0105] ω = frequency

[0106] t = time

[0107] Optionally, in step 620, in addition to and / or replacing the phase difference determination in step 615, a cross-correlation analysis of the two datasets (e.g., data from a second sensor and a third sensor) received in step 605 can be performed. The result of the cross-correlation analysis at a specific moment (also referred to herein as cross-correlation data) can be mapped to the maximum cross-correlation calculated during the data collection process (step 625). The data collection process can last for a period of time, such as 15 seconds, 30 seconds, 60 seconds, 5 minutes, 10 minutes, and / or one hour. In some cases, data collection can be continuous and / or periodic over longer time periods (e.g., 4 hours, 12 hours, 24 hours, 48 ​​hours, or 82 hours). In some embodiments, the cross-correlation analysis can be based on the time integration of two or more signals. For example, the cross-correlation determined at a given time can be mapped from 0% to 100% relative to the maximum cross-correlation calculated during the data collection process; such an output or mapping can be referred to as "relative cross-correlation". The phase shift analysis in step 615 and / or the cross-correlation analysis in step 620 can be performed over a time period in a manner similar to that used in performing respiratory rate calculations via process 500.

[0108] In some embodiments, the cross-correlation of two discrete functions f[n] and g[n] or datasets can be defined as shown in Equation 7, as follows:

[0109] Equation 7

[0110] in:

[0111] f = the signal corresponding to the first dataset

[0112] g = the signal corresponding to the second dataset

[0113] n = lag between functions

[0114] m = the maximum value of the signal corresponding to either the first or second dataset over a given time period.

[0115] For two approximately periodic discrete signals with noise distortion of equal period, the cross-correlation function of the two signals having a lag of n equal to the sum of the number of points in each signal from negative to positive might look approximately as follows: Figure 9 As shown, the maximum value will appear at zero lag time and the local maximum will appear at an offset equal to the period. Figure 9A graph 900 is provided illustrating a motion capture test, in which lag time, in seconds, is shown as a function of the cross-correlation of xiphoid and umbilical respiratory signals determined during various stages of respiratory distress. Curve 910 shows the cross-correlation as a function of lag time during normal breathing, curve 920 shows the cross-correlation as a function of lag time during severe respiratory distress, and curve 930 shows the cross-correlation as a function of lag time during recovery from severe respiratory distress. Figure 9 The results show that the correlation between the two signals is greatest under normal conditions; the correlation decreases during severe respiratory distress; and the correlation returns to near baseline after recovery from respiratory distress.

[0116] Optionally, in step 630, an indication of the patient's respiratory rate variability may be received. This indication may be determined using a first sensor dataset and a second sensor dataset and / or may be input from another device and / or by the attending caregiver.

[0117] Optionally, in step 635, additional information about the patient may be received and / or determined. Exemplary received additional information includes information related to the patient's physiological characteristics, such as body mass index (BMI), thickness of the patient's abdominal adipose tissue, the patient's weight, the patient's body shape, the patient's respiratory rate (e.g., respiratory rates per minute), mental status, blood oxygen saturation, and / or whether the patient is receiving supplemental oxygen or other respiratory support. Exemplary determined additional information includes respiratory rate (e.g., respiratory rates per minute), which can be used, for example, as described above regarding... Figure 5A The process described is 500, etc., to determine the respiratory rate s, which is a measure of the change in the length of time for each respiratory cycle.

[0118] In step 640, phase-shift analysis data, mapped cross-correlation data, and / or additional information received in step 635 can be used to determine the patient's demonstrated respiratory effort, which can be used to determine the patient's respiratory distress level (i.e., respiratory distress score) (step 645), wherein a decrease in cross-correlation and an increase in phase shift between two or more collected signals indicates an increase in thoracic-abdominal asynchrony (TAA). In some cases, the patient's demonstrated respiratory effort may be and / or may include the patient's demonstrated TAA level. The respiratory effort and / or respiratory distress level can then be provided to a display device, such as a computer monitor or other display device (step 650).

[0119] In some embodiments, not all steps of process 600 are performed to determine the degree of respiratory distress (step 640) and / or to determine the respiratory distress severity score (step 645). For example, in some embodiments, the determination of steps 640 and / or 645 is performed using only the phase difference from step 615, the results of the cross-correlation analysis from step 620, the mapping of the cross-correlation data from step 625, and the determination of the respiratory variability indication from step 625. Alternatively, the results of performing two or more steps of process 600 may be used to determine the degree of respiratory distress (step 640) and / or to determine the respiratory distress severity score (step 645). For example, the results of steps 615 and 620, the results of steps 615, 620 and 625, the results of steps 615, 620, 625 and 630, the results of steps 620, 625 and / or 630, and / or the results of steps 625 and 630 can be combined to determine the degree of respiratory distress (step 640) and / or to determine the severity score of respiratory distress (step 645).

[0120] Figure 7 This is a flowchart illustrating exemplary steps of another process 700 for determining a patient's TAA, the degree of respiratory distress exhibited by the patient, and / or the patient's respiratory distress score. These determinations can be performed on, for example, a periodic, on-demand, and / or continuous basis. Process 700 can be performed by, for example, system 300 or any of its components (such as sensor array 310).

[0121] Initially, in step 705, a first cross-correlation dataset, the patient's first respiratory effort level, and / or the patient's first respiratory distress severity score may be received via, for example, execution of process 600 or a portion thereof. In some embodiments, the information received in step 705 may be a baseline cross-correlation dataset, the patient's baseline respiratory effort, and / or the baseline respiratory distress severity score, which in some cases may be predetermined as part of, for example, a routine physical examination. These baselines can help determine the level of effort a patient exhibits when breathing under normal conditions (e.g., not during an acute illness). Using baselines in this way allows for the determination of respiratory effort that takes into account individual differences when determining whether a patient is in respiratory distress and / or quantifying the degree of respiratory distress or determining a patient's respiratory distress score. This may be helpful, for example, when a patient exhibits impaired breathing under normal conditions, such as in cases that may be diagnosed with a chronic respiratory system (e.g., asthma, chronic obstructive pulmonary disease (COPD), or lung cancer). Alternatively or alternatively, the information received in step 705 may be a cross-correlation dataset prior to the execution of process 700 (e.g., minutes, hours, days), the patient’s demonstrated respiratory effort, and / or a respiratory distress severity score determined for the patient.

[0122] In step 710, the third sensor dataset and the fourth sensor dataset may be received by a processor or a computer, such as computer system 330. In some embodiments, the third dataset and the fourth dataset come from different sensors located at different parts of the patient's body (e.g., chest and abdomen or xiphoid process and abdomen). In some embodiments, the third sensor dataset and the fourth sensor dataset are like... Figures 2A-2C The waveforms shown are similar to those waveforms, and sometimes the third and fourth sensor datasets can be similar to the first and second sensor datasets received in step 605.

[0123] The received sensor data can then be filtered, analyzed, and / or preprocessed (step 715). Except for performing filtering, analysis, and / or preprocessing on the third and fourth sensor datasets, step 715 can be performed similarly to step 610 described above. The phase difference between the third and fourth sensor datasets can then be determined (step 720). Step 720 can be performed similarly to step 615.

[0124] Then, a cross-correlation analysis of the third and fourth datasets can be performed (step 725), and the results of this cross-correlation analysis at a specific point in time (also referred to herein as cross-correlation data) can be mapped to the maximum cross-correlation calculated during the data collection process (step 730). In some embodiments, steps 725 and 730 can be performed in a manner similar to that of steps 620 and 625, respectively.

[0125] Optionally, in step 735, additional information about the patient may be received and / or determined. The additional information received in step 735 may be similar to the additional information received in step 635.

[0126] In step 740, the cross-correlation data of the mapping between the third and fourth datasets and / or the additional information received in step 735 may be used to determine the patient's second or subsequent respiratory effort level, which may be used to determine the patient's second or subsequent respiratory distress level (i.e., respiratory distress score) (step 745). In some cases, the patient's demonstrated respiratory effort level may be and / or may include the patient's demonstrated thoracoabdominal asynchrony (TAA).

[0127] In step 750, the cross-correlation data of the mappings of the third and fourth datasets can be compared with the cross-correlation data of the mappings of the third and fourth datasets to determine the differences between them. This difference can be used to adjust or limit (e.g., increase or improve) the second determined degree of respiratory distress determined in step 740 and / or the second respiratory score determined in step 745. Alternatively or additionally, step 750 can be performed prior to steps(s) 740 and / or 745, and the comparison can be used to determine the second degree of respiratory distress determined in step 740 and / or the second respiratory score determined in step 745.

[0128] Alternatively or alternatively, step 750 may include comparing the level of respiratory effort received in step 705 with the second level of respiratory effort determined in step 740. This difference may be used to adjust or limit (e.g., increase or improve) the second level of respiratory distress determined in step 740 and / or the second respiratory score determined in step 745. Alternatively or alternatively, step 750 may be performed prior to step(s) 740, and the comparison may be used to determine the second level of respiratory distress determined in step 740 and / or the second respiratory score determined in step 745.

[0129] Alternatively or alternatively, step 750 may include comparing the degree of respiratory distress score received in step 705 with a second respiratory distress score determined in step 745. This difference may be used to adjust or limit (e.g., increase or improve) the degree of second respiratory distress determined in step 740 and / or the second respiratory score determined in step 745. Alternatively or alternatively, step 750 may be performed prior to step(s) 745, and the comparison may be used to determine the degree of second respiratory distress determined in step 740 and / or the second respiratory distress score determined in step 745.

[0130] Optionally, in step 755, the comparison results from step 750 may be used to update and / or recalculate the second respiratory distress level determined in step 740 and / or the second respiratory distress score determined in step 745.

[0131] In step 760, the patient's second respiratory effort and / or second respiratory distress severity score and / or updated and / or recalculated second respiratory effort and / or second respiratory distress severity score may be transmitted to a display device.

[0132] Figure 10 This is a flowchart illustrating exemplary steps of a process 1000 for collecting information about the movement of a patient's chest cavity and its portions over time while breathing, using, for example, system 300 and / or its components, and for assessing whether the patient is in respiratory distress.

[0133] In step 1005, an image of the patient with multiple markers positioned on it can be received. The markers can mark or depict different locations of the patient's chest cavity. The position of the markers in the image received in step 1005 can represent the original location or origin of the markers, and vertical and horizontal movements can be measured relative to this original location or origin.

[0134] Figure 11 An example of image 1100 is provided, which may be received in step 1005, where different locations of the patient's chest cavity or thoracic cavity are depicted using different markings in the form of dots drawn on the patient and labels erected from the patient. More specifically, Figure 11 The diagram illustrates a first marker 1105 located in the upper chest region of a patient, a second marker 1110 located below the sternum, a third marker 1115 located at or near the navel, a fourth marker 1120 generally located in the first intercostal space (e.g., between the fifth and sixth ribs), a fifth marker 1125 generally located in the second intercostal space (e.g., between the sixth and seventh ribs), a sixth marker 1130 generally located in the third intercostal space (e.g., between the seventh and eighth ribs), and a seventh marker 1135 generally located in the fourth intercostal space (e.g., between the eighth and ninth ribs). In some embodiments, the first marker 1105, the second marker 1110, and / or the third marker 1115 may each include a first reference marker 1106, a second reference marker 1117, and / or a third reference marker 1116, which can be configured to help capture video of the patient's movement in, for example, X, Y, and / or Z(multiple) directions during breathing. In some cases, reference points may be in the form of a cross or a "+" symbol to, for example, help analyze a patient's video recordings to determine the patient's movement as he or she breathes. Figure 11Optional sub-markers are also shown. Movements of the patient's chest and abdomen can be observed and quantified via the first seven marks 1105-1135. For example, a camera such as a 360-degree camera can record the movement of the patient's chest during breathing, and this video can be received in step 1010. The video can be analyzed to, for example, determine the movement of the marks over time (step 1015). In some embodiments, multiple videos can be received in step 1010, and multiple videos can be analyzed / quantified via the first seven marks under different breathing conditions of the patient (e.g., unrestricted and restricted). For example, video recordings of the patient's breathing can be acquired when breathing is unrestricted (e.g., normal); when resistance is applied to the patient's chest and / or breathing via, for example, an elastic band and / or an exercise mask with fixed resistance, and there is no time to adapt to the resisted breathing; and / or when resistance is applied to the patient's chest and / or breathing via, for example, an elastic band and / or an exercise mask with fixed resistance, and the patient is allowed to adapt to the resisted breathing at time intervals (e.g., 3 to 8 minutes). These records can then be analyzed to determine how much the first seven markers 1105-1135 moved over time under different patient conditions.

[0135] Figure 12 An exemplary graph 1200 is provided, showing three Lisajous curves plotting abdominal movement in inches as a function of rib cage movement in inches, wherein a first Lisajous curve 1210 represents abdominal movement as a function of rib cage movement when the patient recovers from respiratory distress, a second Lisajous curve 1220 represents abdominal movement as a function of rib cage movement when the patient is in severe respiratory distress, and a third Lisajous curve 1230 represents abdominal movement as a function of rib cage movement when the patient is experiencing normal breathing. The first Lisajous curve 1210, the second Lisajous curve 1220, and the third Lisajous curve 1230 reflect the changes in the amplitude of movement for each of three breathing types (i.e., recovery from respiratory distress, severe respiratory distress, and normal breathing, respectively). In this example, for the sake of illustration, the range of breathing amplitude during recovery from respiratory distress (i.e., the second Lisajous curve 1220) is larger than that shown in normal breathing (i.e., the third Lisajous curve 1230) and recovery as illustrated by the first Lisajous curve 1210 (i.e., a smaller change in the amplitude of abdominal movement relative to the amplitude of rib cage movement). This demonstrates how a comparison of the amplitude of abdominal and rib cage movement in a patient can help quantitatively characterize the degree of respiratory distress they are experiencing.

[0136] Optionally, in step 1020, the cross-correlation of data from two or more markers can then be performed, for example, in a manner similar to the cross-correlation analysis in step 620.

[0137] Optionally, in step 1025, the change in amplitude of one or more of the markers over time can be determined. As an example, Figure 13 Chart 1300 provides bar charts showing the average peak-to-peak amplitude of the first seven markers 1105-1135 during normal breathing (unfilled (or white) bars), breathing during severe respiratory distress (shown as bars with horizontal filled lines), and breathing after recovery from severe respiratory distress (shown as bars with diagonal filled lines). Chart 1300 also provides an indication of the error range for each breathing type in the form of error bars. In this case, the error bars represent 95% confidence intervals.

[0138] In step 1030, it can be determined whether the patient is in respiratory distress (i.e., breathing is similar to restricted breathing) and an indication of whether the patient is in respiratory distress can be provided to users such as clinicians, doctors or nurses (step 1035).

[0139] Recent proposed treatment algorithms for patients with hypoxia due to COVID-19 suggest monitoring signs, including TAA, when considering escalating respiratory support from HFNC to mechanical ventilation. This is because some patients whose respiratory rate and thoracoabdominal asynchrony cannot be rapidly relieved by HFNC are at potential high risk of HFNC failure. Multiple studies have shown that while HFNC and noninvasive ventilation (NIV) may be sufficient to manage respiratory failure in COVID-19 if used early enough, the data are far from conclusive—more robust, evidence-based indications are needed for choosing between different forms of NIV and between NIV and invasive ventilation.

[0140] Figure 14 This is a flowchart illustrating exemplary steps of a process 1400 for treating a patient with respiratory distress using, for example, system 300 and / or its components (such as sensor array 310).

[0141] In step 1405, the patient's sensor data set can be received. The sensor data can be similar to the sensor data received in step 605, as described above. Figure 6As explained. In some embodiments, the first and second datasets come from different sensors located at different sites on the patient's body (e.g., chest and abdomen or xiphoid process and abdomen). In some embodiments, sensor data can be received when the patient arrives at a treatment facility (e.g., an emergency center hospital) and / or when monitoring the patient's respiratory distress at home. Prior to step 1405, a sensor array, such as sensor array 310, can be placed on the patient's chest, xiphoid process, and abdomen to obtain data on how the chest, xiphoid process, and abdomen move when the patient breathes. In step 1410, it can be determined, for example, by performing process 600 or a portion thereof, whether the patient is experiencing respiratory distress. The determination of step 1410 can then be provided to a clinician or the patient's caregiver. For the purposes of discussing process 1400, the range of respiratory distress determination is none, mild, moderate, or severe distress; however, those skilled in the art will understand that an indication of respiratory distress can be made and provided to a clinician in any suitable form (e.g., numerical rating or graph).

[0142] When the patient is no longer experiencing respiratory distress, he or she can be discharged from the treatment facility (step 1485). When it is determined that the patient is experiencing mild respiratory distress, treatment such as salbutamol can be administered (step 1415), and the patient can continue to be monitored to determine if the treatment is effective. In step 1420, another sensor dataset can be received, and it can be determined whether the patient is still experiencing respiratory distress after treatment (step 1425). If the patient is no longer experiencing respiratory distress, or if the respiratory distress is considered manageable in an outpatient setting, such as in patients recovering from a respiratory illness and / or patients with chronic conditions such as chronic obstructive pulmonary disease (COPD), he or she can be discharged from the treatment facility (step 1485). When the patient is still experiencing respiratory distress, he or she can be admitted to a treatment facility (e.g., a hospital) for further treatment of his or her respiratory distress (step 1430).

[0143] When it is determined in step 1410 that the patient's respiratory distress is severe, the patient can be admitted to a treatment facility (e.g., a hospital) for further treatment of his or her respiratory distress (step 1430). After admission to a treatment facility via determination in 1410 or 1425, additional sensor data can be received (step 1435), thereby determining the level of respiratory distress (step 1440), and the respiratory distress determination in step 1440 can be used to determine whether the patient should be placed in the intensive care unit (for severe respiratory distress) or on a hospital floor (for moderate respiratory distress). In some embodiments, steps 1435 and 1440 may not be performed, and the respiratory distress determination in step 1410 or 1425 can be used to determine whether the patient should be placed in the intensive care unit or on a hospital floor.

[0144] In step 1445, the patient may be placed in the Intensive Care Unit (ICU) for further treatment, such as salbutamol, HFNC, NIPPV, IPPV, and / or sedation and ventilation, depending on the severity of respiratory distress and the patient's responsiveness to treatment (step 1450). To determine the patient's responsiveness to treatment, the patient may be monitored, and additional sensor data may be received on a continuous, periodic, and / or on-demand basis (step 1455). The sensor data received in step 1455 may be used to determine whether the patient's respiratory distress has changed (step 1460). If the patient's respiratory condition does not improve or worsens, step 1450 may be repeated, with gradually more aggressive and invasive treatments. When the patient's respiratory condition improves and / or when the patient's respiratory distress is moderate rather than severe, the patient may be transferred to a treatment facility / hospital floor (step 1465), where he or she may receive treatment such as salbutamol, oxygen, and / or HFNC (1470). While in a treatment facility / hospital, the patient can be monitored and sensor data received on a continuous, periodic, and / or on-demand basis (step 1475), and the patient can be discharged from the treatment facility when respiratory distress is resolved. If the patient's respiratory distress is not resolved (e.g., respiratory distress is the same as or worse than previously determined respiratory distress indicators), treatment 1470 can continue, and if the patient's respiratory distress worsens to a severe level, he or she can be transferred to the intensive care unit (step 1445) and / or step 1435 can be repeated.

[0145] In some embodiments, when monitoring a patient's respiratory distress using process 1400, a sensor array, such as sensor array 310, can be placed on the patient prior to step 1405. Figure 4C As shown, the patient can wear the sensor array continuously for a period of time while receiving treatment at the treatment facility. In this way, consistency of measurement results can be achieved over time, as different sensors and / or different sensor placements will not affect any determination of respiratory distress. Alternatively or alternatively, process 600 can be used to determine whether the patient is in respiratory distress in steps 1410, 1425, 1440, 1460, and / or 1480, such that the output is a respiratory distress severity score and / or an indication of the severity of the patient's TAA.

[0146] In one use case, the procedure described herein can be used to assess and manage acute infantile bronchiolitis, the most common cause of hospitalization in the first year of life. Currently, the standard of care for monitoring hospitalized infants with conditions such as bronchiolitis involves a series of routine and repetitive assessments by a trained clinician, monitoring respiratory rate, oxygen saturation, and signs of increased work of breathing, including thoracoabdominal asynchrony, nasal flaring, and accessory muscle use. Typically, continuous monitoring of the infant is necessary to detect respiratory deterioration, which would otherwise go undetected by intermittent clinical assessments and could progress to a more severe condition. Such continuous monitoring by trained clinicians is laborious and costly in terms of cost and resource usage (e.g., clinical staff). Furthermore, direct observation and assessment of the infant are susceptible to errors, for example, caused by mutual observability and relativistic assessment (as opposed to absolute diagnosis or assessment).

[0147] The oxygen and ventilation support used in the treatment of bronchiolitis ranges from minimally invasive to maximally invasive, from supplemental oxygenation (via nasal cannula or mask) to high-flow nasal cannula (HFNC) to continuous positive airway pressure (CPAP) and invasive mechanical ventilation in the most severe cases. Monitoring respiratory effort through the systems and processes described herein will provide clinicians with the ability to continuously monitor patients with bronchiolitis without requiring constant and direct observation and assessment. This has several advantages when compared to current standards of care, including, but not limited to, the ability to passively, continuously, and persistently monitor a patient's effort during respiration so that changes (improvement or failure) can be accurately measured over time and treatment plans can be adjusted accordingly. For example, information provided by the systems and processes described herein (e.g., respiratory distress severity scores, respiratory effort levels, etc.) can help clinicians make decisions about a patient's condition or the severity of respiratory distress and / or about escalation and de-escalation of respiratory and / or ventilation support in this situation.

[0148] In a typical use case in the management and / or treatment of bronchiolitis, a patient with respiratory distress symptoms is admitted to a treatment facility (e.g., an emergency care clinic, hospital, or hospital emergency department) where array 310 can be placed on the patient to receive sensor data (step 1405 of process 1400). When the degree of respiratory distress is mild (or uncertain) (step 1410), treatment, such as supplemental oxygen, can be administered (step 1415). Sensor data can be received again (step 1420), and if the patient is still experiencing respiratory distress (step 1425), he or she may be admitted to a general hospital ward for suspected bronchiolitis (e.g., step 1430). Alternatively, if a patient is observed to have significant respiratory distress upon admission to the treatment facility, the patient can be directly admitted to a general hospital ward (i.e., process 1400 can begin at step 1415 (e.g., when no sensor data is being collected, as respiratory distress is readily observable) and / or process 1400 can begin at step 1430). The patient's respiratory distress can be continuously monitored via, for example, performing process 600 (steps 1425 and 1440). If severe respiratory distress is detected and / or if moderate respiratory failure is present, an alarm can be issued to alert the nursing team to the patient's condition. The clinician can then observe the patient to assess his or her condition and, if necessary, adjust the treatment provided to the patient (steps 1450 or 1470) (e.g., escalation of the patient's oxygen requirements and / or respiratory therapy (e.g., escalation to non-invasive ventilation such as CPAP)). Intensive care unit admission can occur at this stage, and the patient can continue to be continuously monitored using the system described herein (steps 1445 to 1460).

[0149] If the clinical team subsequently receives an alert from the system described herein indicating severe respiratory distress and / or failure (steps 1440 or 1460), mechanical ventilation may be considered, especially in settings with other indications for intubation, such as poor mental status, severe hypoxemia, or hypercapnia. Alternatively, if the system indicates an improvement in respiratory status (e.g., step 1460), the patient can be weaned off oxygen support therapy and may be transferred from the ICU to a floor of the hospital (step 1465). The patient can then be monitored (steps 1475–1480) until discharge, which only occurs when sensor data and physical examination indicate minimal work of breathing without support therapy. This will translate into a near-normal respiratory severity score.

[0150] In another use case, the systems, devices, and procedures described herein can be used for the diagnosis and management (or treatment) of COVID-19 (or non-COVID-19) acute respiratory distress syndrome (ARDS). Currently, conflicting evidence exists regarding the role of high-flow nasal intubation (HFNC) and non-invasive ventilation (NIV) in the early management of COVID-19 respiratory distress. Some studies have found no evidence that delayed intubation followed by HFNC or NIV increases mortality, suggesting that this modality can be used to successfully manage the disease in less severe cases while avoiding potential damage associated with invasive ventilation. Other studies have found that failure to intubate early leads to increased mortality due to patient-inducible lung injury (P-SILI) resulting from rapid deterioration and excessive spontaneous ventilation. When these factors are considered in conjunction with the aerosolization risk of HFNC and NIV viruses, thus exposing bedside healthcare providers, early intubation may be considered a preferred approach for managing respiratory diseases or infections such as SARS, MERS, and SARS-CoV-2 (i.e., COVID-19) respiratory distress. Continuous monitoring of respiratory effort using the systems, devices, and processes described herein can provide valuable indications of respiratory distress and / or the degree of respiratory effort exerted by the patient, which can help guide early oxygenation therapy and intubation strategies in COVID-19 patients. In this use case, the systems and / or devices described herein can be placed on a patient with suspected or confirmed respiratory infection who presents with moderate respiratory distress upon admission, observable signs of increased work of breathing and / or hypoxemia upon physical examination (step 1430, which may be performed with or without sensor data received at steps 1405 and / or 1420). In the absence of significant dyspnea or severe respiratory distress (step 1440), the patient can initially be treated with brief (less than 24 hours) HFNC or noninvasive ventilation (NIV) (step 1470) while being monitored (continuously, periodically, and / or on demand) using the systems / devices described herein to determine the degree of effort exerted by the patient during breathing, the degree of respiratory distress, and / or a respiratory distress score over time (step 1480). The systems and / or devices described herein can be used to determine the success of a NIV trial by, for example, comparing respiratory distress scores and / or respiratory effort over time to quantify improvement or further failure (step 1480). If the patient's condition is stable and / or improves (e.g., an increase in respiratory distress score or a reduction in respiratory effort), treatment can be continued and / or degraded. If the patient's condition deteriorates and / or further decompensation is indicated, escalation to more aggressive and / or more invasive treatments (e.g., mechanical ventilation) and / or admission to the ICU may be necessary (step 1480).

[0151] The standard of care is to begin weaning patients off mechanical ventilation as soon as possible, starting 24 hours after intubation, provided the patient is at least able to breathe spontaneously. Ventilator modes that allow spontaneous breathing, whether assisted or unassisted, can facilitate this process. However, weaning a patient off a ventilator can pose risks, which may occur when high respiratory effort leads to uncontrolled transpulmonary pressure and puts the patient at risk of P-SILI and weaning failure. The described system can be used to ensure adequate minimization of effort during spontaneous breathing in mechanical ventilation by monitoring the patient's effort during respiration, thereby allowing for appropriate adjustments and / or countermeasures to, for example, the ventilation device to reduce risks to the patient. For example, if the clinical care team determines that a patient's respiratory effort is higher than expected (e.g., a respiratory effort score higher than expected or a threshold), the ventilator mode can be adjusted to controlled ventilation, where the patient's breathing is entirely controlled by the ventilator, which can be used to reduce the amount of effort the patient expends during respiration. On the other hand, increasing evidence suggests that insufficient patient effort during mechanical ventilation is associated with atrophic diaphragmatic injury due to muscle inactivity. For this reason, the system can also be used to ensure that the patient is making an adequately increased respiratory effort (e.g., a respiratory effort score above the expected or threshold value). If the patient is not making an adequate respiratory effort (a respiratory effort score below the expected or threshold value), the care team can adjust the ventilator settings to allow for greater spontaneous breathing and less assisted breathing in response to this inadequacy.

Claims

1. A system for monitoring respiration of a patient, comprising: a processor in communication with a first sensor positioned at a first location on a patient's epidermis and a second sensor positioned at a second location on the patient's epidermis, the patient being under respiratory distress syndrome monitoring, and a memory having stored therein a set of instructions that, when executed by the processor, will cause the processor to: receive a first sensor data set from the first sensor; receive a second sensor data set from the second sensor; determine a phase difference between the first sensor data set and the second sensor data set; determine a degree of respiratory effort exhibited by the patient over a period of time based on the determined phase difference between the first sensor data set and the second sensor data set; determine a change in respiratory distress syndrome of the patient over the period of time in response to the determined degree of respiratory effort exhibited by the patient over the period of time; and communicate the change in respiratory distress syndrome of the patient to a display device. The determination of the degree of respiratory effort exhibited by the patient includes a determination of a degree of thoracoabdominal asynchrony exhibited by the patient.

2. The system of claim 1, wherein, 3. The system of claim 1, further comprising: preprocessing at least the first sensor data set and the second sensor data set prior to determining the phase difference.

4. The system of claim 1, further comprising: filtering at least the first sensor data set and the second sensor data set prior to determining the phase difference. The first sensor and the second sensor are accelerometers, and the first sensor data set and the second sensor data set include acceleration measurements.

5. The system of claim 1, wherein, The first sensor and the second sensor are force gauges, and the first sensor data set and the second sensor data set include force measurements.

6. The system of any one of claims 1 to 5, wherein, The first sensor and the second sensor are strain sensors, and the first sensor data set and the second sensor data set include strain measurements.

7. The system of any one of claims 1 to 5, wherein, The first location is the patient's chest.

8. The system of any one of claims 1 to 5, wherein, The first location is proximate to the patient's xiphoid process.

9. The system of any one of claims 1 to 5, wherein, The second location is proximate to the patient's xiphoid process.

10. The system of any one of claims 1 to 5, wherein, The second location is the patient's abdomen.

11. The system of any one of claims 1 to 5, wherein, 12. The system of any one of claims 1 to 5, further comprising: receiving an indication of a respiratory rate of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the respiratory rate.

13. The system of any one of claims 1 to 5, further comprising: receiving a third sensor data set from a third sensor positioned at a third location on the patient's epidermis, the third sensor being in communication with the processor; ​ determining a phase difference between the first sensor data set and at least one of the third sensor data set and the second sensor data set, wherein determining the degree of respiratory effort exhibited by the patient is further based on the determined phase difference between the first sensor data set and the third sensor data set and the second sensor data set and the third sensor data set.

14. The system of any one of claims 1 to 5, further comprising: performing a cross-correlation analysis between the first sensor data set and the second sensor data set by the processor prior to determining the degree of respiratory effort exhibited by the patient, wherein the degree of respiratory effort exhibited by the patient is further based on a result of the cross-correlation analysis.

15. The system of claim 14, wherein, the first sensor data set and the second sensor data set are signals collected over the period of time, the system further comprising: mapping a result of the cross-correlation calculation at a particular time during the period of time to a maximum cross-correlation value calculated during the period of time prior to determining the degree of respiratory effort exhibited by the patient.

16. The system of any one of claims 1 to 5, further comprising: receiving an indication of a respiratory rate variability of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the respiratory rate variability.

17. A system for monitoring respiration of a patient, comprising: a processor in communication with a first sensor positioned at a first location on a patient's epidermis and a second sensor positioned at a second location on the patient's epidermis, the patient being under monitoring for respiratory distress syndrome, and a memory having stored therein a set of instructions which, when executed by the processor, will cause the processor to perform the following operations: receiving a first sensor data set from the first sensor; receiving a second sensor data set from the second sensor; performing a cross-correlation analysis between the first sensor data set and the second sensor data set; determining a degree of respiratory effort exhibited by the patient over a period of time based on a result of the cross-correlation analysis; determining a change in respiratory distress syndrome of the patient over the period of time in response to the determined degree of respiratory effort exhibited by the patient over the period of time; and communicating the change in respiratory distress syndrome of the patient to a display device. the determination of the degree of respiratory effort exhibited by the patient includes determining a degree of chest-abdomen asynchrony exhibited by the patient.

18. The system of claim 17, wherein, 19. The system of claim 17, further comprising: pre-processing at least the first sensor data set and the second sensor data set prior to performing the cross-correlation analysis.

20. The system of any one of claims 17 to 19, further comprising: filtering at least the first sensor data set and the second sensor data set prior to performing the cross-correlation analysis. the first sensor and the second sensor are accelerometers, and the first sensor data set and the second sensor data set comprise acceleration measurements.

21. The system of any one of claims 17 to 19, wherein, ​ 22. The system of any one of claims 17 to 19, wherein, The first sensor and the second sensor are force gauges, and the first sensor dataset and the second sensor dataset comprise force measurements.

23. The system of any one of claims 17 to 19, wherein, The first sensor and the second sensor are strain sensors, and the first sensor dataset and the second sensor dataset comprise strain measurements.

24. The system of any one of claims 17 to 19, wherein, The first location is the patient's chest.

25. The system of any one of claims 17 to 19, wherein, The first location is proximate to the patient's xiphoid process.

26. The system of any one of claims 17 to 19, wherein, The second location is proximate to the patient's xiphoid process.

27. The system of any one of claims 17 to 19, wherein, The second location is the patient's abdomen.

28. The system of any of claims 17 to 19, further comprising: receiving an indication of a respiratory rate of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the respiratory rate.

29. The system of any of claims 17 to 19, further comprising: determining, by the processor, a phase difference between the first sensor dataset and the second sensor dataset prior to determining the degree of respiratory effort exhibited by the patient, wherein the degree of respiratory effort exhibited by the patient is further based on the determined phase difference.

30. The system of claim 29, wherein, The first sensor dataset and the second sensor dataset are signals collected over the period of time, the system further comprising: mapping, prior to determining the degree of respiratory effort exhibited by the patient, a result of the cross-correlation calculation at a particular time during the period of time to a maximum cross-correlation value calculated during the period of time.

31. The system of any of claims 17 to 19, further comprising: receiving a third sensor dataset from a third sensor positioned at a third location on the patient's epidermis, the third sensor in communication with the processor; performing, by the processor, a cross-correlation analysis between the first sensor dataset and the third sensor dataset or the second sensor dataset and the third sensor dataset, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on a result of the cross-correlation analysis between the first sensor dataset and the third sensor dataset or the second sensor dataset and the third sensor dataset.

32. The system of any of claims 17 to 19, further comprising: receiving an indication of a respiratory rate variability of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the respiratory rate variability.

33. A system for monitoring respiration of a patient, comprising: a processor and a memory having stored therein a set of instructions, which when executed by the processor, will cause the processor to perform the following operations: receiving a video recording of a chest of the patient over a period of time as the patient is breathing, the patient's epidermis of the chest being marked with a first marker positioned at a first location on the patient's epidermis and a second marker positioned at a second location on the patient's epidermis, the patient being under monitoring for respiratory distress syndrome; analyzing changes in position of the first marker and the second marker over the period of time; forming a first waveform, the first waveform showing the changes in position of the first marker over the period of time; a second waveform is formed, the second waveform showing changes in position of the second marker over the period of time; a phase difference between the first waveform and the second waveform is determined; a degree of respiratory effort exhibited by the patient over the period of time is determined based on the determined phase difference; a change in respiratory distress syndrome of the patient over the period of time is determined in response to the determined degree of respiratory effort exhibited by the patient over the period of time; and the change in respiratory distress syndrome of the patient is communicated to a display device.

34. The system of claim 33, wherein, The determination of the degree of respiratory effort exhibited by the patient includes a determination of a degree of chest-abdominal asynchrony exhibited by the patient.

35. The system of claim 33, wherein, The first location is a chest of the patient.

36. The system of claim 33, wherein, The first location is proximate to an xiphoid process of the patient.

37. The system of any one of claims 33 to 36, wherein, The second location is proximate to the xiphoid process of the patient.

38. The system of any one of claims 33 to 36, wherein, The second location is an abdomen of the patient.

39. The system of any of claims 33 to 36, further comprising: receiving an indication of a respiratory rate of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the respiratory rate.

40. The system of any one of claims 33 to 36, wherein, At least one of the first marker and the second marker is a sensor.

41. The system of any one of claims 33 to 36, wherein, At least one of the first marker and the second marker is a light.

42. The system of any one of claims 33 to 36, wherein, The video recording is a three-dimensional video recording.

43. The system of any one of claims 33 to 36, wherein, The video recording captures relative movement of a chest of the patient as the patient breathes.

44. The system of any of claims 33 to 36, further comprising: performing, by the processor, a cross-correlation analysis between a first set of sensor data and a second set of sensor data prior to determining the degree of respiratory effort exhibited by the patient, wherein the degree of respiratory effort exhibited by the patient is further based on a result of the cross-correlation analysis.

45. The system of claim 44, wherein, The first set of sensor data and the second set of sensor data are signals collected over the period of time, the system further comprising: mapping, prior to determining the degree of respiratory effort exhibited by the patient, a result of a cross-correlation calculation at a particular time during the period of time to a maximum cross-correlation value calculated during the period of time.

46. The system of any of claims 33 to 36, further comprising: receiving an indication of a variability of a respiratory rate of the patient, wherein the determination of the degree of respiratory effort exhibited by the patient is further based on the variability of the respiratory rate.

47. A system for monitoring respiration of a patient, the system comprising: a first sensor configured to be positioned on a chest of a patient and to capture movement of the chest of the patient, the first sensor communicatively coupled to a processor; a second sensor configured to be positioned proximate to a xiphoid process of the patient and to capture movement of the xiphoid process of the patient, the second sensor communicatively coupled to the processor; a third sensor configured to be positioned on an abdomen of a patient and to capture movement of the abdomen of the patient, the third sensor communicatively coupled to the processor; a power source configured to provide power to the first sensor, the second sensor, and the third sensor; and a processor in communication with a memory having stored therein sets of instructions that, when executed by the processor, will cause the processor to perform the following operations: receive a first sensor data set from a first sensor positioned at a first location on the patient's epidermis, the patient being under respiratory distress syndrome monitoring, the first sensor being in communication with the processor; receive a second sensor data set from a second sensor positioned at a second location on the patient's epidermis, the second sensor being in communication with the processor; determine a phase difference between the first sensor data set and the second sensor data set; determine a degree of respiratory effort exhibited by the patient over a period of time based on the determined phase difference between the first sensor data set and the second sensor data set; determine a change in respiratory distress syndrome of the patient over the period of time in response to the determined degree of respiratory effort exhibited by the patient over the period of time; and communicate the change in respiratory distress syndrome of the patient to a display device.

48. The system of claim 47, wherein, The first sensor, the second sensor, and the third sensor are accelerometers.

49. The system of claim 47, wherein, The first sensor, the second sensor, and the third sensor are force sensors.

50. The system of claim 47, wherein, The first sensor, the second sensor, and the third sensor are strain gauges.

51. The system of claim 47, further comprising: a controller communicatively coupled to at least one of the first sensor, the second sensor, and the third sensor and the processor, the controller being configured to extract and communicate at least one of a movement measurement, an acceleration measurement, a force measurement, a strain measurement, a respiratory rate, and a degree of thoracoabdominal asynchrony exhibited by the patient to the processor.

52. The system of claim 47, further comprising: a first lead mechanically and electrically coupling the first sensor and the second sensor together; and a second lead mechanically and electrically coupling the second sensor and the third sensor together. A length of at least one of the first lead and the second lead is adjustable.

53. The system of claim 52, wherein, the processor is in communication with a memory having stored thereon a set of instructions that, when executed by the processor, cause the processor to:

54. The system of claim 47, wherein, receive a first sensor data set from the first sensor positioned at a first location on the patient's epidermis, the first sensor being in communication with the processor; receive a second sensor data set from the second sensor positioned at a second location on the patient's epidermis, the second sensor being in communication with the processor; determine a phase difference between the first sensor data set and the second sensor data set; determine a degree of respiratory effort exhibited by the patient based on the determined phase difference between the first sensor data set and the second sensor data set; and communicate the degree of respiratory effort to a display device. ​ ​ 55. The system of claim 54, wherein, The processor is in communication with a memory having stored thereon a set of instructions that, when executed by the processor, further cause the processor to: receive a third sensor data set from a third sensor positioned at a third location on the patient's epidermis, the third sensor being in communication with the processor; determine a phase difference between the first sensor data set and at least one of the third sensor data set and the second sensor data set and the third sensor data set; and determine a degree of respiratory effort exhibited by the patient based on the determined phase difference between the first sensor data set and the third sensor data set and the second sensor data set and the third sensor data set.

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