Patent Publication Number: US-2021169375-A1

Title: Sensors and Method for Defining Breathing Signatures for Identifying Respiratory Disease

Description:
CLAIM TO PRIORITY 
     The present application claims priority to U.S. Provisional Patent Applications 62/945,866 and 62/945,711, both filed 9 Dec. 2019. The entire contents of the aforementioned provisional patent applications are incorporated herein by reference. 
    
    
     FIELD 
     The present application relates to the field of lung function monitoring devices, such as are useful in monitoring and treating asthma, chronic obstructive pulmonary disease (COPD), and other pulmonary disorders of humans and other mammals. 
     BACKGROUND 
     Classical lung function testing in humans includes testing for the lung function parameters forced vital capacity (FVC), which is defined as the quantity of air that can be exhaled forcibly after taking a full inhalation, and forced expiratory volume in one second (FEV1), a measure of the volume of air exhaled in one second following a deep inhalation. FEV1 and FVC are of interest in monitoring COPD and asthma because obstructions or other limitations in expiratory airflow caused by these conditions, and measurable as changes in FVC and FEV1, make breathing difficult, cause audible wheezing, and produce other symptoms. FEV1 and FVC measurements of patients suffering from other diseases, such as pneumoconiosis or silicosis, or recovering from pneumonia, may also be useful to physicians. Patients may therefore benefit from home lung function parameter monitoring, particularly if recovering from an attack or recent hospitalization; outpatient monitoring would be useful to assure functional improvement and avert recurrence necessitating repeat hospitalization. Similarly monitoring patients with chronic conditions, or monitoring while they undertake their daily activities, or exercise, to guide these activities or modulate adjunctive pharmacology would be useful. 
     Asthma signs and symptoms, including FVC and FEV1, vary from day to day or from week to week because asthma may be triggered by environmental conditions including pollens, medications, foods, environmental airborne contaminants and fumes, or breathing cold air, as well as exercise and common viruses and bacteria. Asthma is often treated with medications including short-acting beta agonists and longer-acting “controller” medications; patients having frequent variations in symptoms, including frequent asthma attacks, may need adjustment in prescribed medications as well as identification and avoidance of environmental triggers. 
     It is known that the human chest and abdominal walls typically move during breathing. 
     SUMMARY 
     In an embodiment, a lung function analysis system has multiple motion sensing devices, each motion sensing device includes at least one accelerometer, at least one gyro, a battery, a processor, and a wireless transmitter, the processor reads motion data from the accelerometer and gyro and transmit it over the wireless transmitter. The system also has a data collection device adapted to receive motion data from motion sensing devices and records the motion data in a database; and a workstation with a lung function data analysis routine that analyses the motion data to provide information useful in treating pulmonary disease. 
     In an embodiment, a method of obtaining information useful in treating pulmonary disease in a subject includes placing multiple motion sensing devices on the subject, each motion sensing device with at least one accelerometer, at least one gyro, a battery, a processor, and a wireless transmitter, the processor configured to read motion data from the accelerometer and gyro and to transmit it over the wireless transmitter. The method also includes collecting the motion data from the plurality of motion sensing devices with a digital radio; and analyzing the motion data to determine the information useful in treating pulmonary disease. 
     In another embodiment, a lung function analysis system includes a chest movement monitoring system comprising remote-sensing sensors and a processor, the processor configured to read sensor data from the remote sensing sensors and to determine chest and abdominal movements of a subject; and a workstation configured with a lung function data analysis routine adapted to analyze chest and abdominal movement data from the database to provide information useful in treating pulmonary disease. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  is a block diagram of one example system for recording chest and abdominal movement data from which lung function data is derived, in embodiments. 
         FIG. 1A  is a block diagram of one example system for recording chest and abdominal movement using remote sensing using sensors deployed around a subject&#39;s body, in embodiments. 
         FIG. 2  is a block diagram of a biostamp data-collection stick-on device, in embodiments this may be the Biostamp nPoint® (Trademark of MC10, Inc, Lexington, Mass.) device. 
         FIG. 3  is an illustration of example data collection device placements on experimental subjects. 
         FIGS. 4, 5, 6, and 7  are plots of motion data versus time for data collection devices placed on right back, right hypochondrium, sternum X, and sternum Y, respectively. 
         FIG. 8  is a block diagram of an analysis workstation adapted to process collected chest movement data. 
         FIG. 9  is flowchart illustrating analysis of collected chest movement data. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Data Acquisition System 
     A system  100  for collecting chest and/or abdominal wall movement information includes one or more “biostamp” sensor devices  102 ,  103 ,  104 , ( FIG. 1 ) such as, but not limited to, the Biostamp nPoint manufactured by MC10, Inc., 10 Maguire Rd., Building 3, Floor 1, Lexington, Mass. 02421. Alternative sensor devices capable of being attached to a subject that contain accelerometers and gyroscopic sensors (gyros) for sensing motion of the subject are referred to herein as biostamp sensor device. Each biostamp sensor device is a portable, lightweight, motion-sensing device configured to be attached to skin of a subject  110  using an adhesive. Each biostamp sensor device  200  includes three-axis accelerometers  202  and three-axis gyros  204  adapted to sense motion, a single-channel analog voltage sensor  205  with a surface electrode  207  for sensing electromyographic signals, the analog voltage sensor  205  and electrode  207  together forming an electromyographic channel, a processor  206 , a memory  208 , a digital radio transceiver  210 , a battery  212 , and an inductive battery charger  214  adapted to charge the battery  212  when energized by AC magnetic fields from an external inductor  216  such as an inductor within charging station  108 . In a particular embodiment the digital radio transceiver  210  is a Bluetooth radio configured to communicate wirelessly with modern cell phones  106  ( FIG. 1 ) or other digital radio transceivers  218  that may or may not use Bluetooth) such as a digital radio of charging station  108 . In an embodiment, three-axis gyros  204  are three-axis rate gyros. 
     The processor  206  of each biostamp sensor device is configured with firmware (not shown) in memory  208  to record motion data  115  derived from accelerometers  202 , voltage sensor  205 , and gyros  204  and store this data in memory  208  until that recorded motion data can be confirmed as having been transmitted by digital radio transceiver  210  to a biostamp-associated app  114  in cell phone  106  or to charging station  108 . Once received in cell phone  106 , motion data  115  is accumulated and periodically retransmitted by cell phone  106 . 
     In an alternative embodiment, the sensor devices being stretchable and further including a stretch sensor adapted to measure stretch of the sensor device. In this embodiment the processor  206  is configured to read the strain sensor and the motion data includes stretch data read from the strain sensor. 
     In embodiments, AC-powered charging station  108  is adapted to provide AC magnetic fields to charge biostamp sensor devices  102 ,  103 ,  104  when the biostamp sensor devices are not adhered to subject  110 , to charge cell phone  106  when the cell phone is not being carried by subject  110 , has a digital radio transceiver  218  adapted to communicate with biostamp sensor devices  102 ,  103 ,  104  that are docked with and charging in charging station  108 , and a removable memory device  112 . In an embodiment, charging station  108  is configured to use its digital radio transceiver  218  to copy motion data from biostamp sensor devices  102 ,  103 ,  104  into memory device  112  while the devices  102 ,  103 ,  104  are being charged. 
     Each biostamp device is configured with a unique identifier (not shown) that is transmitted with collected motion data so that data collected by sensor devices  102 ,  104 , can be identified as motion data collected by particular sensor devices. 
     In an embodiment, biostamp-associated app  114  in cell phone  106  serving as a data collection device, the cell phone records motion data received from sensor devices  102 ,  103 ,  104 , then uploads this motion data  115  through either a cellular data network wireless connection or an IEEE 802-11 “WiFi” wireless connection through the internet  116  onto a server  118  that stores the recorded motion data in a database  120 . A physician may then access motion data from database  120  over internet  116  from a workstation  122  having a lung function data analysis routine  124  in memory  126 ; lung function data analysis routine  124  being configured to extract lung function information from motion data recorded in database  120 . 
     In an alternative embodiment, charging station  108  serves as a data collection device receiving data from each biostamp device and records it in a database within memory device  112 , the physician may then access the motion data from memory device  112  by moving memory device  112  into a connector of workstation  122 , and execute lung function data analysis routine  124  on that collected motion data in the database of memory device  112 . 
     In an alternative embodiment, instead of attaching stick-on sensors to the subject, “around body” or remote-sensing sensors are used to track and record body motions of subject  160  ( FIG. 1A ) from which chest and abdominal movements are determined. In an embodiment, at least two electronic cameras  156 ,  158  are positioned to record stereo image pairs of chest and abdomen of subject  160 , the stereo image pairs being referenced herein as stereo images; in a particular embodiment markers  152 ,  153  are positioned on chest and markers  154  on abdomen of subject  160  so processor  162 , using three-dimensional tracking routines in memory  164 , can track chest and abdominal movements of subject  160 , providing movement data corresponding to data from the 3-dimensional accelerometers and 3-dimensional gyros of the biostamp sensor devices  102 ,  103 ,  104 . In a particular embodiment, markers may be drawn on skin of the subject, or imprinted on an elastic garment worn by the subject  160 . In another particular embodiment with improved three-dimensional tracing routines, no markers are necessary. In an alternative embodiment, a single camera is used with markers, the camera being moved between breaths and successive images are used to record the stereo image pairs. 
     In embodiments, the stereo images are used to extract three dimensional models of the chest and abdomen of the subject at multiple points during the subject&#39;s breathing cycle, differences between the three-dimensional models being used to determine changes in chest and abdomen volume during the breathing cycle and to thereby determine airflow. 
     In an alternative embodiment, the biostamp sensors described with reference to  FIG. 1  are combined with the remote sensors of  FIG. 1A , with movement data from both the 3-dimensional accelerometers and 3-dimensional rate gyros of the biostamp devices and the movement data from the three-dimensional tracking routines of processor  162  being combined into overall chest movement data that may be uploaded to movement database  120  for processing by signature extraction and comparison module  421  ( FIG. 8 ). 
     In another remote-sensing embodiment, cameras  156 ,  158  provide stereo pairs of images of chest and abdomen of subject  160  and processor  162  uses three-dimensional surface model extraction routines in memory  164  to construct a three-dimensional surface model of subject  160 . In a particular embodiment three dimensional models are constructed from stereo image pairs after the subject&#39;s inhaling but prior to exhaling, after one second of exhaling, and after complete exhalation by the subject; volume changes during exhalation provide a direct measure of FEV1 and FVC that can be correlated directly to spirometry results. 
     In another remote-sensing embodiment, a laser beam  168  is repeatedly scanned across subject  160  by scanning laser  166 , intersection  170  of the laser beam with the subject is imaged by an electronic video camera  158 . Processor  162 , knowing positions of camera  158  and laser  166  and the angle of beam  168  at each frame of video captured by camera  158 , can determine position of the intersection  170  in three dimensions and determine movements of chest and abdomen of subject  160  effectively tracking motion of subject with lidar. 
     In another remote-sensing embodiment, ultrasonic transducers  172  are positioned to track position of chest and abdomen of subject  160  by ultrasonic echolocation and tracking of subject  160 . 
     In another remote-sensing embodiment, a radar mapping system using millimeter-wavelength electromagnetic radiation is used to image skin surface of subject  160  through any clothing worn by subject  160 , in a manner resembling millimeter-wavelength scanning systems used at airports to inspect travelers; images derived from the radar mapping system are processed by processor  162  to determine movements of chest and abdomen of subject  160 . 
     In all remote-sensing embodiments herein described, processor  162  is configured to upload motion data  115  through a computer network that may be the internet  116  onto server  118  and stored in database  120 , where the data may be accessed as describe with respect to the embodiment of  FIG. 1 . 
     Lung Function Measurements 
     When biostamp sensor devices  102 ,  103 ,  104  are used for sensing lung function, one device  102  is attached to a chest wall of subject  110  over the right pectoral region, a second device  104  attached to an abdominal wall of subject  110  at the right hypochondrium, and a third device  103  is attached to subject  110  at the sternum. In embodiments, multiple devices are attached to subject  110  including one on the subject&#39;s back, as well as the sternal, back, right pectoral, and right hypochondrium body regions of subject  110  as shown in  FIG. 3 . 
     With biostamp sensor devices, signatures may be derived from recorded accelerations in 3 dimensions (X, Y, and Z) and angular movements (pitch, yaw, and roll) by integrating accelerations to get velocity and integrating again to get approximate chest positions. Signatures of normal breathing, exaggerated breathing, and breathing by those suffering from various lung diseases are correlated to FEV1 and FVC, determined via spirometry or other standardized pulmonary function method, in experimental subjects to determine a calibration from which FEV1 and FVC can be estimated in additional subjects to obviate need for spirometry in the additional subjects. 
     In an alternative embodiment, an additional sensor device may be positioned on the subject&#39;s chest wall in a location where electrode  207  and voltage sensor  205  can detect electromyographic signals from intercostal muscles, data from the electromyographic signals is included as electromyographic data in the motion data transmitted by each sensor device and recorded in the database. 
     The metric that this project focused on detecting is Forced Vital Capacity (FVC). A reference value can be computed for FVC and Forced Expiratory Volume in one second (FEV1) based on a subject&#39;s age and sex. A subject&#39;s actual FVC and FEV1 are often measured by physicians, and an FEV1 to FVC ratio computed, to classify lung function impairment as follows in table 1: 
     
       
         
           
               
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 SPIROMETRY TEST 
                 NORMAL 
                 ABNORMAL 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                 FVC and FEV1 
                 Equal to or greater 
                 Mild 
                 70-79% 
               
               
                   
                 than 80% 
                 Moderate 
                 60-69% 
               
               
                   
                   
                 Severe 
                 less than 60% 
               
               
                 FEV1/WC 
                 Equal to or greater 
                 Mild 
                 60-69% 
               
               
                   
                 than 70% 
                 Moderate 
                 50-59% 
               
               
                   
                   
                 Severe 
                 less than 50% 
               
               
                   
               
            
           
         
       
     
     Once measured, subjects having abnormally low FVC or low FEV1/FVC ratios may be treated with inhaled and/or systemic medications to improve their lung function; for example, but not limitation, asthma, where the FEV1/FVC ratio is often particularly low during “attacks”, may be treated with inhaled or systemic steroids, beta agonists or anticholinergics—e.g. ipratropium and tiotropium, and treatment is often monitored by observing changes in FEV1/FVC. 
     This study defines and detects a digital ‘signature’ of breathing which correlates with FVC, utilizing multiple multimodal biosensors on various locations on the body to measure movement during breathing. Oftentimes, health professionals are interested in FEV1/FVC instead of just FVC. When compared to the reference value, a lower measured value corresponds to a more severe lung abnormality. As reference, FVC ranges for normal and abnormal lung performance are displayed in Table 2 where obstruction and restriction represent broad classifications of lung disease. 
     
       
         
           
               
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                   
                   
                   
                 Mixed/ 
               
               
                   
                 Normal 
                 Obstruction 
                 Restriction 
                 combined 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 FEV 1   
                 &gt;80% 
                 &gt; or &lt;80% 
                 &lt;80% 
                 &lt;80% 
               
               
                   
                 predicted 
                 predicted 
                 predicted 
                 predicted 
               
               
                 FVC 
                 &gt;80% 
                 &gt;80% 
                 &lt;80% 
                 &lt;80% 
               
               
                   
                 predicted 
                 predicted 
                 predicted 
                 predicted 
               
               
                 FEV 1 /FVC or 
                 &gt;70% 
                 &lt;70% 
                 &gt;70% 
                 &lt;70% 
               
               
                 FEV 1 /VC 
               
               
                 (depending on 
               
               
                 which is higher, 
               
               
                 FVC or VC) 
               
               
                   
               
            
           
         
       
     
     Results suggest that there is a correlation between the kinetic data and FVC. In  FIGS. 4-7 , each deep exhalation the subject performed is visible as each of the spikes, and the amplitude of each spike loosely correlated with FVC for all subjects tested. 
     Using biostamp, or equivalent, sensor devices around the thorax of a patient may be a viable method of measuring lung performance data, with each breath easily visible in data (See  FIG. 4-7 )—each spike is one breath, and large breaths such as an extremely forceful exhale, as is required for accurately measuring FVC and FEV1, showing pronounced spikes in motion data. Many different locations on the thorax result in visible results, meaning that different sensor placements may optimize measurement accuracy for each particular disorder studied in this way. 
     Trials with subjects with specific lung disorders are used to generate a training database, this training database is then used to train analysis and classifier routines for lung function data analysis routine  124  to diagnose respiratory or other illnesses. Large-scale trials utilizing this novel multi-modal sensor technology will require adapting big-data technologies, machine learning, and artificial intelligence to process the sheer quantity of data produced, and to extract actionable intelligence for healthcare professionals. 
     Data Analysis and Signature Processing 
     In an embodiment,  FIG. 8  shows one exemplary system  400  for processing motion data  115  ( FIG. 1 ) operable on workstation  122  by using a breathing signature database  420  containing signatures of chest and abdomen motion data derived from subjects with diseased and normal lungs, as well as prior motion data from the same subject  110  from which motion data is currently being analyzed. System  400  thereby determines the chest motion activity of subject  110  without other input. System  400  includes a signature extractor and comparison module  421  that processes motion data against signatures within breathing signature database  420  to determine current chest motion data  424  that defines the activity being performed by subject  102 . 
     A registration database  422  correlates the location of each biostamp sensor device  102 ,  103 ,  104  on subject  110 . For example, registration database  422  may define that sensing device  102  is positioned on a right chest of subject  110   
     Workstation  122  uses signature extraction and comparison module  421 , implemented as machine readable instructions stored within memory  444  and executed by processor  442 , to determine current chest motion data  424  of subject  110 . Signature extraction and comparison module  421  processes sensor data against a breathing signature database  420  ( FIG. 8 ) in memory  126  and generates current chest motion data  424  that defines the identified activities being performed by subject  110 . Breathing signature database  420  may represent “Big data” and may include signatures corresponding to breathing of many subjects. A signature extraction and comparison module of chest motion analyzer  430  thereby matches signatures derived from sensor data to signatures defined within database  420  to determine breathing related current chest motion data  424  of subject  110  and analyze the chest motion in chest motion analyzer  430  to determine an FEV1/FVC ratio and, if chest motion breathing, and FEV1/FVC ratio matches normal breathing patterns  432  for that subject  110 , abnormal warranting alerts  436 , or simply variant activity  434 . 
       FIG. 9  is a flowchart illustrating a method  600  for detecting change in breathing of subject  110  based upon chest motion. Method  600  is implemented within signature extraction and comparison module  421  and chest motion analyzer  430  of workstation  122  ( FIG. 1 ). 
     In step  602 , method  600  receives motion data  115  from the multiple biostamp sensors as configured with sensor placements. In step  604 , method  600  extracts one or more signatures from the sensor data. In step  606 , method  600  matches the sensor signatures of step  604  to signatures within a breathing signature database to identify a corresponding activity. 
     In step  608 , method  600  compares a current pattern of activities to previous patterns of breathing. In one example of step  608 , breathing analyzer  430  determines FVC and FEV1 together with an FEV1/FVC ratio and a breathing rate. In alternative embodiments, breathing analyzer determines flow rates such as Maximum Expiratory Flow (MEF), Forced Expiratory Flow from 25% to 75% of vital capacity (FEF 25-75), or Mixed Midexpiratory Flow Rate (MMFR) as additional or substitute measures of lung function. 
     Step  610  is a decision. If, in step  610 , method  600  determines that the current pattern of breathing does not match the previous or expected pattern of breathing, method  600  continues with step  612 ; otherwise, method  600  continues with step  614 . In step  612 , method  600  generates an alert indicative of the pattern change. In one example of step  612 , a breathing analyzer module of chest motion analyzer  430  generates alert  436  to indicate changes in the pattern of current breathing  424  as compared to previous patterns within normal breathing patterns  432 . Method  600  then continues with step  614 . 
     In step  614 , method  600  compares performance of the current breathing to previous breathing. In one example of step  614 , breathing analyzer module of chest motion analyzer  430  compares breathing performance defined by current chest motion data  424  to previous performances of the same activity within normal breathing patterns  432  to determine breathing variances  434 . 
     Step  616  is a decision. If, in step  616 , method  600  determines that there are changes, method  600  continues with step  618 ; otherwise, method  600  terminates. 
     In step  618 , method  600  generates an alert indicative of the change breathing. Method  600  then terminates. 
     Changes may be made in the above methods and systems without departing from the scope hereof. It should thus be noted that the matter contained in the above description or shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. The following claims are intended to cover all generic and specific features described herein, as well as all statements of the scope of the present method and system, which, as a matter of language, might be said to fall therebetween.