Fusing MCT and holter cardiac reports

Abstract

A method includes completing, at a server, a mobile cardiac telemetry (MCT) study based on packets of electrocardiogram (ECG) data periodically received by the server. The method also includes completing, at the server, a dynamic electrocardiogram (Holter) study based on the packet of ECG data. Completing the Holter study includes generating a report that includes results of the Holter study and the MCT study.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Provisional Application No. 63 / 335,614, filed on April 27, 2022, which is incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates to devices, methods and systems for analyzing cardiac activity and cardiac events. Background Art

[0004] Monitoring devices for collecting biometric data are becoming more and more common in diagnosing and treating patients' medical conditions. For example, mobile devices can be used to monitor a patient's cardiac data. Such cardiac monitoring can provide doctors with valuable information about the occurrence and regularity of various heart diseases and irregular pulses in patients. Cardiac monitoring can be used, for example, to identify abnormal heart rhythms so that critical alerts can be provided to the patient, doctor or other care provider and the patient can receive treatment. Summary of the invention

[0005] In Example 1, a method includes completing a mobile cardiac telemetry (MCT) study at a server based on packets of electrocardiogram (ECG) data periodically received by the server. The method also includes completing a Holter study at the server based on the packets of ECG data.

[0006] In Example 2, according to the method of Example 1, wherein completing the MCT study includes automatically detecting cardiac events based on the package of ECG data.

[0007] In Example 3, according to the method of any of the preceding Examples, wherein completing the MCT study includes sending a notification to a patient or a physician regarding a detected cardiac event.

[0008] In Example 4, according to the method of any of the preceding examples, wherein completing the Holter study includes generating a data packet, wherein the data packet includes a strip of ECG data, metadata associated with the strip of ECG data, and a summary of automatically detected cardiac events.

[0009] In Example 5, the method of Example 4 further includes transmitting the data packet to a remote computing system and receiving the Holter report at the server.

[0010] In Example 6, according to the method of Example 5, wherein the Holter report includes a list of cardiac events detected during the MCT study.

[0011] In Example 7, according to the method of Example 6, wherein the Holter report includes the cardiac event and ECG data associated with the cardiac event.

[0012] In Example 8, the method according to any one of Examples 5 to 7, wherein the Holter report includes the patient initiated event and ECG data associated with the patient initiated event.

[0013] In Example 9, the method of any one of Examples 5 to 8 further includes transmitting the executable code along with the data package to a remote computing system.

[0014] In Example 10, according to the method of any of the preceding Examples, wherein the package of ECG data includes all ECG data generated during the MCT study.

[0015] In Example 11, the method according to any of the preceding Examples, wherein completing the Holter study includes generating a final Holter report.

[0016] In Example 12, according to the method of any of the preceding Examples, wherein completing the Holter study occurs only after completing the MCT study.

[0017] In Example 13, a computer program product includes instructions for causing one or more processors to perform the steps of the methods of Examples 1-12.

[0018] In Example 14, a computer readable medium has the computer program product of Example 13 stored thereon.

[0019] In Example 15, a computer includes the computer readable medium of Example 14.

[0020] In example 16, a system includes a server having a first processor and a first computer-readable medium, on which a first set of computer-executable instructions is embodied. The first set of instructions is configured to be executed by the first processor to cause the first processor to perform the following operations as part of an MCT study: using a machine learning model, detecting cardiac events based on packets of ECG data periodically received by the server; determining a severity of the detected event; and causing a notification to be sent to a user based on the severity. The first set of instructions is configured to be executed by the first processor to cause the first processor to perform the following operations as part of a Holter study: using a machine learning model, determining a rhythm classification of the event based on packets of ECG data.

[0021] In Example 17, the system of Example 16, wherein the first set of instructions is configured to be executed by the first processor to cause the first processor to perform the following operations as part of a Holter study: using a machine learning model, determine a beat classification based on a packet of ECG data.

[0022] In Example 18, the system according to Example 17, wherein the first set of instructions is configured to be executed by the first processor to cause the first processor to perform the following operations as part of a Holter study: generating a data packet. The data packet includes a strip of ECG data, a rhythm classification, a beat classification, and a detected cardiac event from the MCT study.

[0023] In Example 19, the system according to Example 18, wherein the detected cardiac events include patient-initiated cardiac events and automatically detected cardiac events.

[0024] In Example 20, the system of Example 18, wherein the first set of instructions is configured to be executed by the first processor to cause the first processor to perform the following operations as part of a Holter study: transmitting a packet of data to a remote computing system; and receiving, at the server, a Holter report generated by the remote computing system.

[0025] In Example 21, the system according to Example 20, wherein the Holter report includes a list of detected cardiac events from the MCT study.

[0026] In Example 22, the system according to Example 20, wherein the Holter report includes strips of ECG data associated with detected cardiac events from the MCT study.

[0027] In Example 23, the system according to Example 20 further includes a remote computing system having a second processor and a second computer-readable medium, on which a second set of computer-executable instructions is contained. The second set of instructions is configured to be executed by the second processor to cause the second processor to: generate a Holter report.

[0028] In Example 24, the system of Example 23, wherein the first set of instructions is configured to be executed by the first processor to cause the first processor to: transmit the executable code along with the data packet to a remote computing system.

[0029] In Example 25, the system of Example 24, wherein the second set of instructions comprises executable code.

[0030] In Example 26, the system according to Example 16, wherein the first set of instructions is configured to be executed by the first processor to cause the first processor to: complete an MCT study before initiating a Holter study.

[0031] In Example 27, the system according to Example 16, wherein the severity includes stable events, severe events, and emergency events.

[0032] In Example 28, the system of Example 16, wherein the machine learning model comprises a deep learning neural network.

[0033] In Example 29, the system of Example 16, wherein the ECG data periodically received by the server is generated by a remote ECG monitor attached to the patient.

[0034] In Example 30, a method includes completing an MCT study at a server based on packets of ECG data periodically received by the server. Completing the MCT study includes automatically detecting cardiac events based on the packets of ECG data using a machine learning model. The method also includes completing a Holter study at the server based on the packets of ECG data. Completing the Holter study includes determining a rhythm classification of an event based on the packets of ECG data using a machine learning model.

[0035] In Example 31, according to the method of Example 30, wherein completing the MCT study includes sending a notification to the patient or physician regarding the detected cardiac event.

[0036] In Example 32, according to the method of Example 30, wherein completing the Holter study comprises generating a package of data. The data package includes a strip of ECG data, a rhythm classification, and a summary of automatically detected cardiac events from the MCT study.

[0037] In Example 33, the method according to Example 32 further includes: transmitting the packet of data to a remote computing system; and receiving the Holter report at the server.

[0038] In Example 34, according to the method of Example 33, wherein the Holter report includes a list of automatically detected cardiac events from the MCT study and ECG data associated with the automatically detected cardiac events.

[0039] In Example 35, according to the method of Example 30, wherein completing the Holter study occurs only after completing the MCT study.

[0040] Although multiple examples are disclosed, other examples of the present disclosure will become apparent to those skilled in the art from the following detailed description, wherein illustrative examples of the present disclosure are shown and described. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A cardiac monitoring system according to certain examples of the present disclosure is shown.

[0042] Figure 2 A server, a remote computer, and a user interface are shown according to certain examples of the present disclosure.

[0043] Figure 3 An example MCT end-of-service report is shown in accordance with certain examples of the present disclosure.

[0044] Figure 4 A view of a report construction page is shown according to certain examples of the present disclosure.

[0045] Figure 5 A view of a report simulation page is shown according to certain examples of the present disclosure.

[0046] Figure 6 A flow chart depicting an illustrative method for combining results from an MCT study and a Holter study is shown according to an example of the present disclosure.

[0047] Figure 7 is a block diagram depicting an illustrative computing device according to examples of the present disclosure.

[0048] Although the disclosed subject matter may be subjected to various modifications and alternative forms, specific examples have been shown by way of example in the drawings and described in detail below. However, it is not intended to limit the disclosure to the specific examples described. On the contrary, the disclosure is intended to cover all modifications, equivalents and alternatives falling within the scope of the disclosure as defined by the appended claims. DETAILED DESCRIPTION

[0049] The present disclosure relates to devices, methods and systems for facilitating analysis of cardiac activity and cardiac events (e.g., abnormal heart rhythm or other problems). A patient's electrocardiogram (ECG) data can be used to monitor the patient and identify whether the patient has experienced a cardiac event and what type of cardiac event has occurred.

[0050] One type of ECG-based study is called mobile cardiac telemetry monitoring, which is sometimes referred to as MCOT monitoring or MCT monitoring. Using MCT monitoring, a patient's cardiac activity is monitored in near real time, and if the cardiac activity meets certain criteria established by the physician, the patient's physician is notified. At the end of the MCT monitoring period (or at various periods during that period), an end-of-service report can be created to summarize the cardiac events detected during the MCT monitoring period.

[0051] Another type of ECG-based study is called dynamic cardiac rhythm monitoring, which is sometimes referred to as Holter monitoring. Dynamic monitoring is a retrospective analysis of a longer study (e.g., a 24 to 48 hour or sometimes longer study conducted on an outpatient basis). At the end of the Holter monitoring period, a final report can be generated to provide a summary of the monitored heart activity and to provide representative examples of the rhythms and beats detected. The final Holter report includes more detail than the end-of-service report generated for an MCT study. However, Holter monitoring does not necessarily involve near real-time physician notification.

[0052] Examples of the present disclosure are therefore directed to systems, methods, and devices for combining aspects of MCT monitoring and Holter monitoring.

[0053] Figure 1 Patient 10 and example system 100 are shown. System 100 includes a monitor 102 attached to patient 10 to detect cardiac activity of patient 10. Monitor 102 can generate electrical signals representing cardiac activity in patient 10. For example, monitor 102 can detect the patient's heartbeat (e.g., using infrared sensors, electrodes) and convert the detected heartbeat into electrical signals representing ECG data. Monitor 102 transmits the ECG data to mobile device 104 (e.g., mobile phone).

[0054] The mobile device 104 may include a program (e.g., a mobile phone application) that receives, processes, and analyzes ECG data. For example, the program may analyze the ECG data and detect or mark cardiac events (e.g., periods of irregular cardiac activity) contained within the ECG data. As noted above, because the ECG data may be continuously generated, the amount of ECG data stored and processed locally on the mobile device 104 may be overwhelming. Therefore, the mobile device 104 may periodically transmit blocks of ECG data to another device or system that may process the blocks of ECG data and metadata associated with the blocks of ECG data (e.g., time, duration, detected / marked cardiac events), attach them together, and archive them. In some instances, the monitor 102 may be programmed to transmit ECG data directly to other devices or systems without utilizing the mobile device 104.

[0055] In some instances, the monitor 102 and / or mobile device 104 includes a button or touch screen icon that allows the patient 10 to initiate an event. Such an indication can be recorded and transmitted to another device or system. In some instances, the patient-initiated event is time-stamped and causes the monitor 102 and / or mobile device 104 to send ECG data packets surrounding the patient-initiated event to a server.

[0056] Cardiac Event Server

[0057] exist Figure 1 In the example shown in , mobile device 104 transmits ECG data (and associated metadata, if any) to cardiac event server 106 (hereinafter referred to as "server 106" for brevity). Server 106 includes multiple platforms, layers, or modules that work together to process and analyze ECG data so that cardiac events can be detected, filtered, prioritized, and ultimately reported to the patient's physician for analysis and treatment. Figure 1 In the example of , the server 106 includes one or more machine learning models 108 (e.g., a type of deep neural network), a cardiac event router 110, a Holter platform 112, and an MCT platform 114.

[0058] Although Figure 1 Only one server 106 is shown, and the server 106 may include multiple separate physical servers, and the various platforms / modules / layers may be distributed among the multiple servers. Each of the platforms / modules / layers may represent a separate program, application, and / or code block, wherein the output of one of the platforms / modules / layers is the input to another of the platforms / modules / layers. Each platform / module / layer may communicate between or among other platforms / modules / layers and systems and devices external to the server 106 using an application programming interface.

[0059] The server 106 applies the machine learning model 108 to the ECG data to classify the cardiac activity of the patient 10. For example, the machine learning model 108 can compare the ECG data with the labeled ECG data to determine which labeled ECG data the ECG data is most similar to. The labeled ECG data can identify specific cardiac events, including but not limited to ventricular tachycardia, bradycardia, atrial fibrillation, pause, normal sinus rhythm, or artifact / noise. In addition, the labeled ECG data can identify beat classifications such as normal, ventricular, and supraventricular.

[0060] In some embodiments, the machine learning model 108 includes two paths, wherein the first path is a deep convolutional neural network and the second path is a deep fully connected neural network. The deep convolutional neural network receives one or more groups of beats (e.g., a beat train with 3-10 beats), which are processed through a series of layers in the deep convolutional neural network. The series of layers may include a convolutional layer that performs convolution on the time series data in the beat train, a batch normalization layer that normalizes the output from the convolutional layer (e.g., centers the result around the origin), and a nonlinear activation function layer for receiving the normalized value from the batch normalization layer. The beat train then passes through a set of repeated layers, such as another convolutional layer, a batch normalization layer, and a nonlinear activation function layer. This set of layers can be repeated multiple times.

[0061] The deep fully connected neural network receives RR-interval data (e.g., the time interval between adjacent beats) and processes the RR-interval data through a series of layers: a fully connected layer, a nonlinear activation function layer, another fully connected layer, another nonlinear activation function layer, and a regularization layer. The outputs from both paths are then provided to the fully connected layer. The resulting values ​​pass through the fully connected layer and the softmax layer to produce a probability distribution for the beat class.

[0062] If the machine learning model 108 determines that the ECG data is closest to a labeled ECG data associated with a cardiac event, then the machine learning model 108 can determine that the patient 10 has experienced the cardiac event. In addition, the machine learning model 108 can measure or determine certain characteristics of the patient's 10 cardiac activity based on the ECG data. For example, the machine learning model 108 can determine the patient's 10 heart rate, duration, or beat count during the cardiac event based on the ECG data. The server 106 stores the cardiac event (and associated metadata, such as information like beat classification, heart rate, duration, beat count, etc.) in a database for storage. Subsequently, the server 106 can retrieve the cardiac event and associated information from the database.

[0063] In some instances, the mobile device 104 (or monitor 102) can initially classify a cardiac event based on the ECG data. The server 106 can then reclassify or confirm the cardiac event using the machine learning model 108. Doing so allows for more computationally expensive analysis of the ECG data to be performed using the computing resources of the server 106 rather than the limited resources of the mobile device 104.

[0064] In some instances, once the ECG data is processed by the machine learning model 108, the ECG data is made available to the Holter platform 112 and / or the MCT platform 114. As will be described in more detail below, the Holter platform 112 and the MCT platform 114 may be accessed by a user at a clinic or laboratory 118 through a corresponding remote computer 116 (e.g., a client device such as a laptop computer, mobile phone, desktop computer, and the like).

[0065] Figure 2 The server 106 is shown communicatively coupled (e.g., via a network) to a remote computer 116. Figure 2 In the example of FIG. 1 , the remote computer 116 includes a monitor showing a user interface 122 (hereinafter referred to as “UI 122” for brevity) that displays features of the Holter platform 112 and / or the MCT platform 114 hosted by the server 106. The UI 122 includes multiple pages or screens for tracking and facilitating analysis of patient ECG data.

[0066] Return to reference Figure 1 In some instances, the cardiac event router 110 is used to determine what platform will further process the ECG data. The determination can be based on a classification associated with the cardiac event. For example, if the identified cardiac event is severe, the cardiac event router 110 can mark or send the ECG data, etc. to the MCT platform 114. The MCT platform 114 can be programmed to immediately send a notification (along with the relevant ECG data and associated metadata) to the patient's physician / care team (e.g., to their computer system, email, mobile phone application) and / or to the patient 10 (e.g., to their computer system, email, mobile phone application).

[0067] In other examples, the server 106 does not include the cardiac event router 110. Alternatively, the server 106 can be programmed to first route the ECG data (and metadata) from the machine learning model 108 to the MCT platform 114 and then to the Holter platform 112 at the end of the MCT study.

[0068] MCT Platform

[0069] During the MCT study, the server 106 periodically receives chunks of ECG data, which are processed by the machine learning model 108. The machine learning model 108 is programmed to automatically detect that a cardiac event has occurred and associate the detected cardiac event with an initial event classification (e.g., as an atrial fibrillation event, a ventricular tachycardia event, a flutter event, and the like). Additionally, the machine learning model 108 may determine an initial acuity or severity for each detected event. For example, each detected event may be determined to be a stable event, a severe event, or an emergency event. Furthermore, the machine learning model 108 may associate each individual beat with a beat classification.

[0070] The determined events and classifications can be analyzed by the MCT platform 114 to determine whether and how the detected events should be further processed and upgraded. Once a cardiac event is determined to be an emergency, the MCT platform 114 can send a notification to the patient and / or his / her doctor / care team. In some instances, before sending the notification, a technician (e.g., an ECG technician) intercepts the notification and confirms whether the cardiac event is an emergency. In addition, during the MCT study, the technician can review each detected event and determine whether the associated acuity and event classification are correct. The technician can give priority to cardiac events that were initially designated as emergencies, followed by severe events, and then followed by stable events.

[0071] As noted above, at the end of an MCT monitoring period, an end of service (EOS) report may be created to summarize the cardiac events detected during the MCT monitoring period.

[0072] Figure 3 An example EOS report 150 is shown. The EOS report 150 includes a list of cardiac events detected during the MCT study. Figure 3 In the example of , cardiac events are divided into two lists. The first list 152A includes cardiac events automatically detected by the machine learning model 108, while the second list 152B includes cardiac events initiated by the patient. The listed events can be sorted by their severity. For each category of events in lists 152A and 152B, the categories can indicate the severity of the event, a description of the event category, and the number of events that occurred during the MCT study. The EOS report 150 can also include a high-level summary 154 of the number of events (by severity) that occurred during the MCT study.

[0073] As in Figure 3As can be seen in the EOS report 150, the EOS report 150 lacks information such as basic ECG data, beat type, RR interval, and other content that may be useful to the doctor. In order to help generate this type of additional information, the basic ECG data from the MCT study can be processed by the Holter platform 112 to initiate the Holter study. For example, the basic ECG data can be processed again by the machine learning model 108 and then structured to be sent to the remote computing system 116. In some instances, the ECG data will be reprocessed only after the MCT study is completed. The MCT study can be completed after receiving the ECG data for a predetermined period of time (e.g., 14 days, 30 days).

[0074] Holter Platform

[0075] The Holter platform 112 is a software-as-a-service (SaaS) platform hosted by the server 106. To access the Holter platform 112, a user (e.g., a technician) interacts with the UI 122 to log into the Holter platform 112 via a web browser so that the user can use and interact with the Holter platform 112. When a user at a clinic or laboratory 118 is ready to analyze a patient's ECG data, the user can select the patient's profile through the UI 122.

[0076] The server 106 (e.g., via programming associated with the Holter platform 112) may initiate a process for sending data (e.g., a packet of data) to the remote computer 116. The data includes ECG data (e.g., a strip of ECG data) and metadata associated with the ECG data. The packet of data may also include a data structure representing cardiac events detected during the MCT study and metadata associated with each detected event, such as severity and event classification.

[0077] As noted above, once ECG data from a monitored patient has been collected, the machine learning model 108 can determine certain features of the patient's 10 cardiac activity based on the ECG data, including estimating that a cardiac event has occurred and associating or generating metadata for the determined event. The metadata can include information about the patient 10, the patient's 10 heart rate during the cardiac event, the duration of the cardiac event, the beat count of the cardiac event, the confidence level of the machine learning model's identification of the cardiac event, and / or the beat classification (e.g., normal, ventricular, supraventricular, unclassified).

[0078] Accessing, processing and displaying one or more days' worth of ECG data and metadata consumes a large amount of computing resources, network bandwidth resources and human resources. To help alleviate the burden on these resources, server 106 (e.g., via Holter platform 112) can selectively transmit packages of ECG data and metadata to remote computer 116.

[0079] The initial packet of data may include: (1) a short strip of ECG data surrounding a detected cardiac event (e.g., a 60 second strip), (2) metadata associated with the strip, and (3) executable code (e.g., JavaScript code). In some instances, only ECG data associated with the highest priority cardiac event is initially transferred. After the initial packet of data is transferred from the server to the remote computer 116, additional packets of data may be transferred in response to selections made by the user in the UI 122.

[0080] Holter Report Build Page

[0081] Using these initial packages of data, the user accesses (via the remote computer 116 and UI 122) Figure 4 1. The Holter report construction page 200 is shown in FIG. The Holter report construction page 200 facilitates analysis of cardiac events and metadata associated with cardiac events. These pages are generated at the remote computer 116 based on the package of data and they are selectively displayed via the UI 122. As will be described in more detail below, as the user interacts with the Holter report construction page 200, the metadata is updated in real time at the remote computer 116.

[0082] Figure 4 A screen shot of a Holter report construction page 200 is shown. The Holter report construction page 200 is used by a user to review and analyze a patient's ECG data and metadata. The Holter report construction page 200 includes a plurality of windows for displaying data, plots, icons, links, tags, indicators, and the like.

[0083] Window 202 displays a heart rate graph for multiple days worth of ECG data. This window 202 provides an initial visual insight into which time periods appear to contain abnormal heart rate activity. Figure 4 In the example shown, window 202 displays four days of ECG data, although shorter or longer time periods may be displayed by default or changed by the user.

[0084] Window 204 allows the user to view a shorter graph of ECG data. For example, window 204 can display ECG data associated with a detected cardiac event as well as ECG data before and after the detected event. This window 204 provides visual insight into the onset of the detected event and whether the detected event may be an artifact, a subsequent event, etc. As the user scrolls through window 204, window 202 displays an indicator (e.g., a vertical line) showing the location of the ECG data of window 204 within the heart rate graph of window 202.

[0085] Window 208 shows a graph of shorter ECG data (e.g., approximately 10 beats) than the graphs of windows 202 and 204. Window 208 displays a closer view of a portion of the ECG data of windows 202 and 204. A user may use window 204 to select which shorter set of ECG data to display in window 208. Each of windows 202, 204, and 208 may include markers, indicators, icons, etc. to visually annotate the location of a detected cardiac event within a strip of ECG data.

[0086] Combination MCT and HOLTER Study

[0087] Figure 5 A screenshot of a report simulation page 250 is shown. The initial ECG data, metadata, and strips of ECG data shown in the report simulation page 250 may be generated by the Holter reporting platform 112. That is, when the user initially selects the report simulation page 250, the user is viewing an initial simulation report automatically generated by the Holter reporting platform 112.

[0088] In addition, the report simulation page 250 can include a list of cardiac events detected during the MCT study, including the severity and rhythm classification of each detected cardiac event and any recorded symptoms. In some instances, the report simulation page 250 also includes a strip of ECG data (e.g., ECG waveforms) associated with the cardiac events detected during the MCT study. By incorporating the results of the MCT study into the Holter study, the details of the detected cardiac events found in the MCT study can be included in the final Holter report (which is discussed in more detail below).

[0089] The simulated report shown in the report simulation page 250 mimics the formatting and data that will ultimately be included in the final report sent to the patient's physician. Example final reports include Holter reports (e.g., short-term or long-term) and cardiac event reports, and the reports may be in the form of a pdf file or other file type.

[0090] Because the simulated report displayed on the report simulation page 250 mimics the final report, the simulated report allows the user to see a preview of the final report without taking the time to generate the actual final report, which may take 10 to 20 minutes. Thus, the report simulation page 250 saves the user the time of having to create multiple reports before finalizing the final version that is then sent to the patient's physician.

[0091] In addition to information from the MCT study, the report simulation page 250 includes a baseline summary of patient information generated from the ECG data. Figure 5 As shown, the report simulation page 250 includes heart rate information, ectopics, and summaries of events—each of which may have been initially generated by the machine learning model 108. For a given type of event, the report simulation page 250 includes one or more example strips of ECG data 252.

[0092] The report simulation page 250 can be automatically updated in real time as the user makes changes in the Holter report construction page 250. For example, if the user recharacterizes the beat, the report simulation page 250 will be updated to reflect the change. Such updating of data can include, among other things, automatically changing the order of cardiac events on the report simulation page 250, changing which band of ECG data represents a given cardiac event, and / or updating a baseline summary of patient information generated from the ECG data, etc. In addition, as the user selects representative events on the report construction page 250, those events are automatically added to the report simulation page 250.

[0093] To conserve processing and network resources and allow these changes to metadata to occur in real time, the calculation and changes to the cardiac event classification and the automatic updating of the beat classification can be performed locally on the remote computer 116 - rather than sending data back and forth between the server 106 and the remote computer 116. For example, the calculations can use cache memory 124 (e.g., Figure 2 The Holter platform 112 may be configured to perform local processing and updating using the processing power (e.g., one or more microprocessors) of the remote computer 116. To enable local processing and updating, the Holter platform 112 may send code to the remote computer 116 for local execution. The code uses (or operates on) the output of the machine learning model 108, such as beat classification and rhythm classification (as opposed to the underlying or raw ECG data), which reduces the computing resources required to process changes made by the user locally at the remote computer 116. In some embodiments, this code is executed by an Internet browser operating on the remote computer 116.

[0094] Once the user is satisfied with the simulated report, the user can select the "Finish Report" button 254 on the report simulation page 250. This selection will start the process of generating a final report, which will be sent to the patient's doctor. In some instances, the server 106 generates the final report.

[0095] In some instances, once the report is constructed and completed, the remote computer 116 can send the report and any changes to the metadata (e.g., subsequent beat classifications and subsequent rhythm classifications) to the server 106 and its database. The server 106 can then replace the metadata initially created by the machine learning model (and saved to the database) with the metadata generated by the remote computer while the user is reviewing and editing the metadata. Thus, if the ECG and metadata need to be accessed again, the server's database has the latest version of the metadata. In addition, the machine learning model 108 can be further trained on the metadata generated by the user at the remote computer.

[0096] Figure 6 A block diagram of a method 400 for combining results from an MCT study and a Holter study is shown. The method 400 completes an MTC study at a server based on packets of ECG data periodically received by the server ( Figure 6 The method 400 also includes completing a Holter study based on the package of ECG data at the server ( Figure 6 404 in FIG.

[0097] Computing devices and systems

[0098] Figure 7 600 according to an example of the present disclosure. The computing device 600 may include any type of computing device suitable for implementing aspects of an example of the disclosed subject matter. Examples of computing devices include special-purpose computing devices or general-purpose computing devices, such as workstations, servers, laptops, desktops, tablet computers, handheld devices, smart phones, general-purpose graphics processing units (GPCPUs), and the like. Each of the various components shown and described in the figures may include their own set of special-purpose computing device components, such as Figure 7 For example, mobile device 104, server 106, and remote computer 116 may each include their own set of components, such as Figure 7 As shown in and described below.

[0099] In the example, computing device 400 includes bus 410, which directly and / or indirectly couples one or more of the following devices: processor 420, memory 430, input / output (I / O) port 440, I / O component 450, and power supply 460. Any number of additional components, different components, and / or combinations of components may also be included in computing device 400.

[0100] Bus 410 represents a bus that may be one or more buses, such as, for example, an address bus, a data bus, or a combination thereof. Similarly, in an example, computing device 400 may include multiple processors 420, multiple memory components 430, multiple I / O ports 440, multiple I / O components 450, and / or multiple power supplies 460. In addition, any number or combination of these components may be distributed and / or replicated across multiple computing devices.

[0101] In an example, memory 430 includes computer-readable media in the form of volatile and / or non-volatile memory and may be removable, non-removable, or a combination thereof. Examples of media include random access memory (RAM); read only memory (ROM); electronically erasable programmable read only memory (EEPROM); flash memory; optical or holographic media; magnetic cassettes, tapes, disk storage or other magnetic storage devices; data transmission; and / or any other medium that can be used to store information and can be accessed by a computing device. In an example, memory 430 stores computer-executable instructions 470 for causing processor 420 to implement aspects of examples of components discussed herein and / or perform aspects of examples of methods and processes discussed herein. Memory 430 may include a non-transitory computer-readable medium storing computer-executable instructions 470.

[0102] Computer executable instructions 470 may include, for example, computer code, machine usable instructions, and the like, such as, for example, program components that can be executed by one or more processors 420 (e.g., microprocessors) associated with computing device 400. Program components may be programmed using any number of different programming environments, including various languages, development kits, frameworks, and / or the like. Some or all of the functionality contemplated herein may also or alternatively be implemented in hardware and / or firmware.

[0103] According to an example, for example, the instructions 470 may be configured to be executed by the processor 420 and cause the processor 420 to perform certain processes after execution. In some examples, the processor 420, the memory 430, and the instructions 470 are part of a controller such as an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and / or the like. Such devices may be used to perform the functions and steps described herein.

[0104] The I / O components 450 may include presentation components configured to present information to a user, such as, for example, a display device, a speaker, a printing device, and / or the like, and / or input components, such as, for example, a microphone, a joystick, a satellite dish, a scanner, a printer, a wireless device, a keyboard, a pen, a voice input device, a touch input device, a touch screen device, an interactive display device, a mouse, and / or the like.

[0105] The devices and systems described herein may be communicatively coupled via a network, which may include a local area network (LAN), a wide area network (WAN), a cellular data network, via the Internet using an Internet service provider, and the like.

[0106] Aspects of the present disclosure are described with reference to flowchart illustrations and / or block diagrams of methods, devices, systems, and computer program products. It will be appreciated that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions.

[0107] Various modifications and additions may be made to the exemplary examples discussed without departing from the scope of the disclosed subject matter. For example, although the examples described above refer to specific features, the scope of the present disclosure also includes examples with different combinations of features and examples that do not include all of the described features. Therefore, the scope of the disclosed subject matter is intended to include all such substitutions, modifications, and variations that fall within the scope of the claims, together with all equivalents thereof.

Claims

1. A method comprising: completing a mobile cardiac telemetry (MCT) study at the server based on packets of electrocardiogram (ECG) data periodically received by the server; as well as A Holter study is performed at the server based on the packets of ECG data.

2. The method according to claim 1, wherein: Completion of the MCT study included automated detection of cardiac events based on the package of ECG data.

3. A method according to any one of the preceding claims, wherein: Completion of the MCT study includes sending a notification to the patient or physician regarding a detected cardiac event.

4. A method according to any one of the preceding claims, wherein: Completing the Holter study includes generating a package of data, wherein the data package includes a strip of ECG data, metadata associated with the strip of ECG data, and a summary of automatically detected cardiac events from the MCT study.

5. The method according to claim 4, further comprising transmitting the packet of data to a remote computing system; and A Holter report is received at the server.

6. The method according to claim 5, wherein: The Holter report included a listing of cardiac events detected during the MCT study.

7. The method according to claim 6, wherein: The Holter report includes the cardiac event and ECG data associated with the cardiac event.

8. The method according to any one of claims 5 to 7, wherein: The Holter report includes a patient-initiated event and ECG data associated with the patient-initiated event.

9. The method according to any one of claims 5 to 8, further comprising: The executable code is transmitted along with the packet of data to the remote computing system.

10. A method according to any one of the preceding claims, wherein: The package of ECG data includes all ECG data generated during the MCT study.

11. A method according to any one of the preceding claims, wherein: Completing the Holter study includes generating a final Holter report.

12. A method according to any one of the preceding claims, wherein: Completion of the Holter study occurred only after completion of the MCT study.

13. A computer program product comprising instructions for causing one or more processors to perform the steps of the method according to claims 1 to 12.

14. A computer readable medium having stored thereon a computer program product according to claim 13.

15. A computer comprising the computer readable medium according to claim 14.

Images