# US 2021/0275058 A1 · Systems and methods for automated localization of wearable cardiac monitoring systems and sensor position-independent hemodynamic inference > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0275058 A1 - Document ID: US20210275058A1 - Status: Published application - Kind: A1 - Application number: 17/316,290 - Filed: 2021-05-10 - Published: 2021-09-09 - Earliest claimed: 2019-07-23 - Assignees: Georgia Tech Research Corporation - Applicants: Georgia Tech Research Corporation - Inventors: Jonathan Zia; Omer Inan; Jacob Kimball; Christopher Rozell - Web page: https://patentgrep.com/patent/US20210275058A1 ## CPC - A61B 5/7267: {involving training the classification device} - A61B 5/1102: {Ballistocardiography} - A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)} - A61B 5/7246: {using correlation, e.g. template matching or determination of similarity} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract The disclosed technology includes devices and methods for a wearable cardiac monitoring system to determine morphological variability and localization of the wearable cardiac monitoring system. The disclosed technology can include receiving seismocardiographic data, determining a classifier of the seismocardiographic data, determining a signal quality index of the seismocardiographic data, determining a quality of the seismocardiographic data based on the classifier and the signal quality index, and outputting an indication of the quality of the seismocardiographic data to a graphical user interface. ## Claims ### Claim 1 A method of detecting morphological changes using a wearable cardiac monitor, the method comprising: receiving seismocardiographic data from a motion sensor; determining, based at least in part on the seismocardiographic data, a classifier of the seismocardiographic data; determining, based at least in part on the seismocardiographic data, a signal quality index of the seismocardiographic data; determining, based on the classifier and the signal quality index, a quality of the seismocardiographic data; and outputting, to a graphical user interface, the quality of the seismocardiographic data. ### Claim 2 (depends on 1) The method of claim 1, wherein the classifier comprises multiple classifiers, and wherein the method further comprises: performing ensemble prediction by utilizing the multiple classifiers to create an ensemble classifier. ### Claim 3 (depends on 1) The method of claim 1 further comprising: retrieving a reference template, the reference template comprising representative seismocardiographic data; and determining the signal quality index by determining a distance between the seismocardiographic data and the reference template. ### Claim 4 (depends on 3) The method of claim 3 further comprising: estimating the distance between the seismocardiographic data and the reference template by dynamic-time feature matching, wherein dynamic-time feature matching comprises: determining a local minimum and a local maximum of the seismocardiographic data; determining a local minimum and a local maximum of the reference template; determining a prohibited intersection point; determining a candidate point; and generating a warp path through the candidate point. ### Claim 5 (depends on 1) The method of claim 1 further comprising: retrieving a location-specific reference template, the location-specific reference template comprising representative seismocardiographic data at a predetermine location; and determining a location of the motion sensor by at least determining a distance between the seismocardiographic data and the location-specific reference template. ### Claim 6 (depends on 5) The method of claim 5 further comprising: performing a seismocardiogram generative factor encoding; comparing the seismocardiographic data to a low-dimensional subspace; and applying a position-specific regression to the low-dimensional subspace to determine a hemodynamic factor. ### Claim 7 (depends on 5) The method of claim 5 further comprising: comparing the seismocardiographic data to a low-dimensional manifold; performing a manifold approximation to analyze a distance traveled along the low-dimensional manifold; and determining a hemodynamic change based on the distance traveled along the low-dimensional manifold. ### Claim 8 A wearable cardiac monitoring system comprising: a motion sensor configured to detect movement of a user's chest and output seismocardiographic data; and a non-transitory, computer-readable medium having instructions stored thereon that, when executed by one or more processors, cause a controller to: receive the seismocardiographic data from the motion sensor; determine, based at least in part on the seismocardiographic data, a classifier of the seismocardiographic data; determine, based at least in part on the seismocardiographic data, a signal quality index of the seismocardiographic data; determine, based on the classifier and the signal quality index, a quality of the seismocardiographic data; and output, to a graphical user interface, an indication of the quality of the seismocardiographic data. ### Claim 9 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the classifier comprises a property of a source distribution of the seismocardiographic data. ### Claim 10 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the classifier comprises multiple classifiers, and wherein the instructions, when executed by the one or more processors, further cause the controller to: perform ensemble prediction by utilizing the multiple classifiers to create an ensemble classifier. ### Claim 11 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the instructions, when executed by the one or more processors, further cause the controller to: retrieve a reference template, the reference template comprising representative seismocardiographic data; and determine the signal quality index by determining a distance between the seismocardiographic data and the reference template. ### Claim 12 (depends on 11) The wearable cardiac monitoring system of claim 11, wherein the instructions, when executed by the one or more processors, further cause the controller to: estimate the distance between the seismocardiographic data and the reference template by dynamic-time feature matching. ### Claim 13 (depends on 12) The wearable cardiac monitoring system of claim 12, wherein dynamic-time feature matching comprises: determining a local minimum and a local maximum of the seismocardiographic data; determining a local minimum and a local maximum of the reference template; determining a prohibited intersection point; determining a candidate point; and generating a warp path through the candidate point. ### Claim 14 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the instructions, when executed by the one or more processors, further cause the controller to: retrieve a location-specific reference template, the location-specific reference template comprising representative seismocardiographic data at a predetermine location; and determine a location of the motion sensor by at least determining a distance between the seismocardiographic data and the location-specific reference template. ### Claim 15 (depends on 14) The wearable cardiac monitoring system of claim 14, wherein the instructions, when executed by the one or more processors, further cause the controller to: perform a seismocardiogram generative factor encoding; compare the seismocardiographic data to a low-dimensional subspace; and apply a position-specific regression to the low-dimensional subspace to determine a hemodynamic factor. ### Claim 16 (depends on 14) The wearable cardiac monitoring system of claim 14, wherein the instructions, when executed by the one or more processors, further cause the controller to: compare the seismocardiographic data to a low-dimensional manifold; perform a manifold approximation to analyze a distance traveled along the low-dimensional manifold; and determine a hemodynamic change based on the distance traveled along the low-dimensional manifold. ### Claim 17 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the controller is configured to wirelessly receive the seismocardiographic data from the motion sensor. ### Claim 18 (depends on 17) The wearable cardiac monitoring system of claim 17, wherein the controller is a remote server configured to wirelessly receive the seismocardiographic data from the motion sensor. ### Claim 19 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the controller is integrated with the motion sensor into a single wearable device. ### Claim 20 (depends on 8) The wearable cardiac monitoring system of claim 8, wherein the instructions, when executed by the one or more processors, further cause the controller to: receive a first input to change a threshold value of the classifier; and receive a second input to change a threshold value of the signal quality index.