Detecting influenza and other respiratory infections from electrocardiography data using machine learning

Abstract

Electrocardiography (ECG) data acquired from a subject and processed with a machine learning model to determine whether the subject has, or is developing, an influenza infection. The machine learning model generates classified feature data that are indicative of the presence and / or likelihood of an influenza infection. Scalogram and / or spectrogram data may be generated from the ECG data and processed with a machine learning model to determine whether the subject has, or is developing, a respiratory infection, such as an influenza or COVID-19 infection. The machine learning model generates classified feature data that are indicative of the presence and / or likelihood of a respiratory infection.

Description

DETECTING INFLUENZA AND OTHER RESPIRATORY INFECTIONS FROM ELECTROCARDIOGRAPHY DATA USING MACHINE LEARNINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application SerialNo. 63 / 581,100, filed on September 7, 2023, and entitled “DETECTING INFLUENZA AND OTHER RESPIRATORY INFECTIONS FROM ELECTROCARDIOGRAPHY DATA USING MACHINE LEARNING,’’ and U.S. Provisional Patent Application Serial No. 63 / 572,739, filed on April 1, 2024, and entitled “DETECTING INFLUENZA AND OTHER RESPIRATORY INFECTIONS FROM ELECTROCARDIOGRAPHY DATA USING MACHINE LEARNING,’’ both of which are herein incorporated by reference in their entirety.STATEMENT OF FEDERALLY SPONSORED RESEARCH

[0002] This invention was made with government support under TR001436 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND

[0003] Electrocardiography (ECG) systems measure electrophysiological signals of cardiac activity in a subject. For example, voltages of the electrical activity of the heart are measured using electrodes places on the subject’s skin. In a conventional 12-lead ECG, electrodes are placed on the subject’s limbs and chest. Although ECG data are representative of electrical activity of the heart, it is possible that additional information about the subject’s health can be derived or estimated from the ECG data.

[0004] The use of an artificial intelligence-based deep learning network approach has the potential to overcome challenges with analyzing ECG data for non-cardiac related information.SUMMARY OF THE DISCLOSURE

[0005] The present disclosure addresses the aforementioned drawbacks by providing a method for generating classified feature data indicative of an influenza infection in a subject. The method includes accessing electrocardiography (ECG) data using a computer system, where the ECG data are acquired from a subject. A machine learning model is also accessed with the computer system, where the machine learning model has been trained on training datato detect a presence of an influenza infection based on ECG data. The ECG data are applied to the machine learning model using the computer system, generating output as classified feature data indicative of an influenza infection in the subject. The classified feature data are output to a user.

[0006] In another aspect of the present disclosure, a method for generating classified feature data indicative of a respiratory infection in a subject is provided. The method includes accessing at least one of scalogram data or spectrogram data using a computer system, where the scalogram data and / or spectrogram data are generated from ECG data acquired from a subject. A machine learning model is also accessed with the computer system, where the machine learning model has been trained on training data to detect a presence of a respiratory infection based on scalogram and / or spectrogram data generated from ECG data. The scalogram and / or spectrogram data are applied to the machine learning model using the computer system, generating output as classified feature data indicative of a respiratory infection in the subject. The classified feature data are output to a user.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 is a flowchart setting forth the steps of an example method for generating classified feature data indicative of an influenza infection in a subject by applying electrocardiography (ECG) data to a machine learning model, such as a neural network.

[0008] FIG. 2 is a flowchart setting forth the steps of an example method for training a machine learning model, such as a neural network, to detect the presence or likelihood of a subject having or otherwise developing an influenza infection based on ECG data obtained from the subject.

[0009] FIG. 3 is a flowchart setting forth the steps of an example method for generating classified feature data indicative of a respiratory7infection or illness in a subject by applying scalogram, spectrogram, or other N-dimensional data generated from ECG data to a machine learning model, such as a neural network.

[0010] FIG. 4 is a flowchart setting forth the steps of an example method for training a machine learning model, such as a neural network, to detect the presence or likelihood of a subject having or developing a respiratory infection or illness based on scalogram, spectrogram, or other N-dimensional data generated from ECG data obtained from the subject.

[0011] FIG. 5 illustrates an example wearable device that can be used to record physiological data, such as ECG data, and to generate classified feature data indicative of a respiratory infection or illness in a subject.

[0012] FIG. 6 is a block diagram of example components that can implement the wearable device of FIG. 5.

[0013] FIG. 7 illustrates an example system for detecting a respiratory infection or illness in a subject based on ECG data.

[0014] FIG. 8 is a block diagram of example components that can implement the system of FIG. 7.DETAILED DESCRIPTION

[0015] Described here are systems and methods for detecting respiratory illnesses based on analysis of electrocardiograph (ECG) data acquired from the subject. In general, the ECG data obtained from the subject, which may be standard 12-lead ECG data, are applied to a neural network or other machine learning algorithm, which generates classified feature data indicating the presence and / or likelihood of influenza or other respiratory illness. As a result, the systems and methods described in the present disclosure provide a point-of-care test for detecting respiratory illness, which does not require specialized tests.

[0016] The systems and methods described in the present disclosure utilize a neural network or other machine learning or Al algorithm to detect, identify, or otherwise characterize subtle patterns in ECG data, or images or maps generated from such ECG data, that are indicative of the presence of a respiratory infection or illness, such as influenza, a coronavirus infection (e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) such as COVID-19), a bacterial infection (e.g., pneumonia, bronchitis, pertussis, tuberculosis, etc.), or the like. Influenza may include respiratory illnesses caused by various strains of influenza virus, including influenza A. influenza B, influenza B. influenza C, and / or influenza D viruses. Additionally or alternatively, influenza may include respiratory illnesses caused by human parainfluenza virus (HP IV) strains, such as HPIV type 1, HPIV type 2, HPIV type 3, and / or HPIV type 4 (including subtypes 4a and 4b). Additionally or alternatively, influenza may include respiratory illnesses caused by haemophilus influenzae. Further, the systems and methods are capable of differentiating between different types of respiratory infection. As a result, the disclosed systems and methods can be used as an initial screening tool in a hospitalor clinical-based setting and translated to a point-of-care test that can be delivered through a portable or potentially a wearable device.

[0017] In some embodiments, the disclosed systems and methods utilize a machine learning model to detect and characterize respiratory infection or illness from a standard 12- lead ECG. In these instances, the disclosed systems and methods provide a cost-effective, non- invasive, low-risk intervention to subjects in a point-of-care or home setting. Additionally or alternatively, the disclosed systems and methods can be implemented using a wearable patch or other wearable device with one or more channels and wearable elements, including shirts, watches, bands, and bracelets with conductive elements capable of recording physiologic signals. In still other embodiments, ECG data can be collected from implanted devices such as loop recorders, pacemakers, or defibrillators. In each of these instances, classified feature data can be generated from the recorded ECG data, processed by a machine learning model or algorithm to generate classified feature data indicative of a respiratory infection or illness, and allow for notifying the user or clinicians that a respiratory infection or illness has been detected (e.g., via an alert or message).

[0018] Inputs from various different data sources can be integrated into a single output to provide a scalable and automated means for users (e.g., subject, patients) and / or clinicians to analyze whether respiratory' illness may be present by analyzing only a standard 12-lead, ECG data obtained with other physiological sensors (e.g.. a wearable device, an implanted device), or other images or maps generated from such ECG data.

[0019] In general, the ECG data can be input to one or more artificial intelligence and / or machine learning (AI / ML) models to generate classified feature data indicative of a respiratory infection or other illness. Any suitable AI / ML or other computational modelling can be used for the analysis of the ECG data, including deep learning, a generative adversarial network (GAN), a convolutional neural network (CNN), a large language modeling (LLM), foundation models, Riffusion, anomaly detection, diffusion, etc. Advantageously, classical machine learning algorithms and models (e.g., random forest, support vector machines, naive Bayes classifiers, nearest neighbors, decision trees. AdaBoost, QDA, Gaussian process, etc.) can also be used in some instances. Additionally or alternatively, these classical machine learning models could be used for the analysis of ECG data and / or related patient health data, or could advantageously be used to initially determine which AI / ML algorithm or model is likely to provide the highest accuracy to develop further models for the analysis of ECG dataand / or related patient health data for detecting the presence and / or likelihood of influenza or other respiratory illness.

[0020] Referring now to FIG. 1, a flowchart is illustrated as setting forth the steps of an example method for generating classified feature data using a suitably trained neural network or other machine learning algorithm (e.g., large language models, generative pretrained transformer models, or the like). As will be described, the neural network or other machine learning algorithm takes ECG data as input data and generates classified feature data as output data. As an example, the classified feature data can be indicative of the presence of influenza (e.g., an influenza infection) in the subject from whom the ECG data were obtained.

[0021] The method includes accessing ECG data with a computer system, as indicated at step 102. Accessing the ECG data may include retrieving such data from a memory or other suitable data storage device or medium. Additionally or alternatively, accessing the ECG data may include acquiring such data with a wearable device or an ECG system (e.g., an ECG measurement system using a 12-lead configuration, or other lead or electrode combination) and transferring or otherwise communicating the data to the computer system, which may be a part of the wearable device or ECG system. For example, a 12-lead ECG system can include a I Lateral lead (also referred to as a I lead), a II Inferior lead (also referred to as a II lead), a III Inferior lead (also referred to as a III lead), an aVR lead, an aVL Lateral lead (also referred to as an aVL lead), an aVF Inferior lead (also referred to as an aVF lead), a VI Septal lead (also referred to as a VI lead), a V2 Septal lead (also referred to as a V2 lead), a V3 Anterior lead (also referred to as a V3 lead), a V4 Anterior lead (also referred to as a V4 lead), a V5 Lateral lead (also referred to as a V5 lead), and a V6 Lateral lead (also referred to as a V6 lead). Additionally or alternatively, the ECG system can implement fewer than 12 leads, such as a single lead, six leads (e.g.. all limb leads: I, II. III. avF, avR, AVL). or the like.

[0022] Additionally or alternatively, other physiological data can be accessed, including other electrophysiology data (e.g., EEG, data, EMG data), PPG data, etc.

[0023] In some examples, the ECG data may be obtained using a radiofrequency (RF)- based sensor device. These RF-based sensors are capable of measuring ECG signals in addition to other biophysical signals (e.g., heart beats, respiratory rates) using transmitted RF waves, which in some instances may include RF waves transmitted according to Wi-Fi or other wireless network protocol. Such sensors enable non-contact measurement of ECG data or other biophysical data. The RF-based sensors can be implemented in a standalone device, integrated into a mobile device (e.g., a smartphone, a tablet computer), integrated into a wearable device(e.g., a smartwatch, a fitness tracker, a wearable patch, a band, a bracelet), integrated into other wearables (e.g., a shirt or other wearable garment with conductive elements capable of recording physiologic signals), integrated into an implanted device (e g., loop recorders, pacemakers, defibrillators), integrated into other medical devices (e.g., digital stethoscopes), or integrated into another device or system (e.g., an automobile or other vehicle, such as an autonomous vehicle that can transport an individual to a clinic or hospital if a condition is detected). The biophysical signals captured from radiofrequencies and / or Wi-Fi can then be used with the systems and methods described in the present disclosure to improve on the diagnosis, management, prognostics, and / or treatment of respiratory' illnesses.

[0024] Furthermore, radiofrequency signals can detect respiratory rates and other signals that can then be synchronously combined with ECG data, phonocardiogram (PCG) data, and / or continuous arterial blood pressure waveform data and used in the neural networks or other Al models described in the present disclosure.

[0025] A trained neural network (or other suitable machine learning algorithm) is then accessed with the computer system, as indicated at step 104. In general, the neural network is trained, or has been trained, on training data in order to detect, identify, or otherwise characterize patterns in ECG data that are indicative of an influenza infection in the subject from whom the ECG data were acquired.

[0026] Accessing the trained neural network may include accessing network parameters (e.g.. weights, biases, or both) that have been optimized or otherwise estimated by training the neural network on training data. In some instances, retrieving the neural network can also include retrieving, constructing, or otherwise accessing the particular neural network architecture to be implemented. For instance, data pertaining to the layers in the neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be retrieved, selected, constructed, or otherwise accessed.

[0027] An artificial neural network generally includes an input layer, one or more hidden layers (or nodes), and an output layer. Typically, the input layer includes as many nodes as inputs provided to the artificial neural network. The number (and the type) of inputs provided to the artificial neural network may vary based on the particular task for the artificial neural network.

[0028] The input layer connects to one or more hidden layers. The number of hidden layers varies and may depend on the particular task for the artificial neural network.Additionally, each hidden layer may have a different number of nodes and may be connected to the next layer differently. For example, each node of the input layer may be connected to each node of the first hidden layer. The connection between each node of the input layer and each node of the first hidden layer may be assigned a weight parameter. Additionally, each node of the neural network may also be assigned a bias value. In some configurations, each node of the first hidden layer may not be connected to each node of the second hidden layer. That is, there may be some nodes of the first hidden layer that are not connected to all of the nodes of the second hidden layer. The connections between the nodes of the first hidden layers and the second hidden layers are each assigned different weight parameters. Each node of the hidden layer is generally associated with an activation function. The activation function defines how the hidden layer is to process the input received from the input layer or from a previous input or hidden layer. These activation functions may vary and be based on the type of task associated with the artificial neural network and also on the specific type of hidden layer implemented.

[0029] Each hidden layer may perform a different function. For example, some hidden layers can be convolutional hidden layers which can, in some instances, reduce the dimensionality of the inputs. Other hidden layers can perform statistical functions such as max pooling, which may reduce a group of inputs to the maximum value; an averaging layer; batch normalization; and other such functions. In some of the hidden layers each node is connected to each node of the next hidden layer, which may be referred to then as dense layers. Some neural networks including more than, for example, three hidden layers may be considered deep neural networks.

[0030] The last hidden layer in the artificial neural network is connected to the output layer. Similar to the input layer, the output layer typically has the same number of nodes as the possible outputs.

[0031] The ECG data are then input to the one or more trained neural networks, generating output as classified feature data, as indicated at step 106. For example, the classified feature data may include a risk score. The risk score can provide physicians or other clinicians with a recommendation to consider additional monitoring for subjects whose ECG data indicate the likelihood of the subject having or developing an influenza infection.

[0032] As another example, the classified feature data may indicate the probability for a particular classification (i.e., the probability that the ECG data include patterns, features, or characteristics indicative of detecting, differentiating, and / or determining the severity of oneor more medical conditions), such as whether the subject from whom the ECG data were acquired has or is developing an influenza infection.

[0033] Additionally or alternatively, the classified feature data may classify the ECG data as indicating the presence of an influenza infection (e.g., “influenza positive” or “influenza negative”). In still other embodiments, the classified feature data may indicate a severity of an influenza infection. For example, the classified feature data may include a severity score that quantifies a severity of an influenza infection.

[0034] The classified feature data generated by inputting the ECG data to the trained neural network(s) can then be displayed to a user, stored for later use or further processing, or both, as indicated at step 108.

[0035] Referring now to FIG. 2. a flowchart is illustrated as setting forth the steps of an example method for training one or more neural networks (or other suitable machine learning algorithms) on training data, such that the one or more neural networks are trained to receive ECG as input data in order to generate classified feature data as output data, where the classified feature data are indicative of the presence of an influenza infection.

[0036] In general, the neural network(s) can implement any number of different neural network architectures. For instance, the neural network(s) could implement a convolutional neural network, a residual neural network, or the like. Alternatively, the neural network(s) could be replaced with other suitable machine learning or artificial intelligence algorithms, such as those based on supervised learning, unsupervised learning, deep learning, ensemble learning, dimensionality reduction, and so on. In some implementations, a large language model, generative pre-trained transformer model, or other foundation model may also be used, as described in more detail below.

[0037] The method includes accessing training data with a computer system, as indicated at step 202. Accessing the training data may include retrieving such data from a memory or other suitable data storage device or medium. Alternatively, accessing the training data may include acquiring such data with a wearable device, a network of wearable devices, an ECG system, or the like, and transferring or otherwise communicating the data to the computer system.

[0038] In general, the training data can include ECG data obtained from a plurality of subjects. The ECG data may be obtained using 12-lead configurations, or fewer leads (e.g., single lead, three leads, six leads, and the like). Additionally, the training data may include other data, such as other health information collected from the subjects. In some embodiments.the training data may include ECG data that have been labeled (e.g.. labeled as containing patterns, features, or characteristics indicative of an influenza infection; labeled as being collected from a subject suffering from an influenza infection; and the like). In some examples, the ECG data may be obtained using an RF-based sensor device.

[0039] The method can include assembling training data from ECG data using a computer system. This step may include assembling the ECG data into an appropriate data structure on which the machine learning algorithm can be trained. Assembling the training data may include assembling ECG data, segmented ECG data, and other relevant data. For instance, assembling the training data may include generating labeled data and including the labeled data in the training data. Labeled data may include ECG data, segmented ECG data, or other relevant data that have been labeled as belonging to, or otherwise being associated with, one or more different classifications or categories. For instance, labeled data may include ECG data and / or segmented ECG data that have been labeled as being associated with a subject suffering from an influenza infection.

[0040] In some instances, synthetic training data may be generated. As one nonlimiting example, techniques such as diffusion models can be modified to create realistic ECG data and / or transformed ECG data from text prompts. Synthetic training data produced by these models can augment real training data to increase diagnostic accuracy. This form of synthetic data can help solve the detection and treatment of uncommon diseases through Al learning problems for which training data is scarce.

[0041] One or more neural networks (or other suitable machine learning algorithms) are trained on the training data, as indicated at step 204. In general, the neural network can be trained by optimizing network parameters (e.g., weights, biases, or both) based on minimizing a loss function. As one non-limiting example, the loss function may be a mean squared error loss function.

[0042] Training a neural network may include initializing the neural network, such as by computing, estimating, or otherwise selecting initial network parameters (e.g., weights, biases, or both). During training, an artificial neural network receives the inputs for a training example and generates an output using the bias for each node, and the connections between each node and the corresponding weights. For instance, training data can be input to the initialized neural network, generating output as classified feature data. The artificial neural network then compares the generated output with the actual output of the training example in order to evaluate the quality of the classified feature data. For instance, the classified featuredata can be passed to a loss function to compute an error. The current neural network can then be updated based on the calculated error (e.g., using backpropagation methods based on the calculated error). For instance, the current neural network can be updated by updating the network parameters (e.g., weights, biases, or both) in order to minimize the loss according to the loss function. The training continues until a training condition is met. The training condition may correspond to, for example, a predetermined number of training examples being used, a minimum accuracy threshold being reached during training and validation, a predetermined number of validation iterations being completed, and the like. When the training condition has been met (e.g., by determining whether an error threshold or other stopping criterion has been satisfied), the current neural network and its associated network parameters represent the trained neural network. Different types of training processes can be used to adjust the bias values and the weights of the node connections based on the training examples. The training processes may include, for example, gradient descent, Newton's method, conjugate gradient, quasi-Newton, Levenberg-Marquardt, among others.

[0043] The artificial neural network can be constructed or otherwise trained based on training data using one or more different learning techniques, such as supervised learning, unsupervised learning, reinforcement learning, ensemble learning, active learning, transfer learning, or other suitable learning techniques for neural networks. As an example, supervised learning involves presenting a computer system with example inputs and their actual outputs (e.g.. categorizations). In these instances, the artificial neural network is configured to leam a general rule or model that maps the inputs to the outputs based on the provided example inputoutput pairs.

[0044] When training a neural network, or other machine learning model, to receive ECG data from an RF-based sensor, the model may additionally or alternatively be trained to output interval measurements from the input ECG data. For instance, the model may be trained on pairs of RF-based ECG data and 12-lead ECG data, on which interval measurements have been annotated.

[0045] The one or more trained neural networks are then stored for later use. as indicated at step 206. Storing the neural network(s) may include storing network parameters (e.g., weights, biases, or both), which have been computed or otherwise estimated by training the neural network(s) on the training data. Storing the trained neural network(s) may also include storing the particular neural network architecture to be implemented. For instance, datapertaining to the layers in the neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be stored.

[0046] Referring now to FIG. 3, a flowchart is illustrated as setting forth the steps of an example method for generating classified feature data using a suitably trained neural network or other machine learning algorithm (e g., large language models, generative pretrained transformer models, or the like). As will be described, the neural network or other machine learning algorithm takes transformed ECG data (e.g., scalogram data, spectrogram data, or other N-dimensional (for N > 2) images, maps, matrices, or data structures generated from ECG data) as input data and generates classified feature data as output data. As an example, the classified feature data can be indicative of the presence of a respiratory infection (e.g.. influenza, COVID-19, other coronavirus, etc.) in the subject from whom the ECG data were obtained.

[0047] The method includes accessing transformed ECG data, which may include scalogram data and / or spectrogram data, with a computer system, as indicated at step 302. Accessing the transformed ECG data may include retrieving such data from a memory or other suitable data storage device or medium. Additionally or alternatively, accessing the transformed ECG data may include generating such data with a computer system. For instance, the transformed ECG data may be generated from ECG data acquired from the subject.

[0048] As a non-limiting example, scalogram data may include one or more scalograms generated from ECG data. In general, a scalogram includes an image, map. or other N- dimensional matrix or data structure depicting the time-frequency distribution of ECG data. In some implementations, a scalogram may be generated by computing a continuous wavelet transform (CWT) of the ECG data and constructing the scalogram based on the CWT coefficients. As an example, the scalogram may be depicted as a heat map or other image.

[0049] As another example, spectrogram data may include one or more spectrograms generated from ECG data. In general, a spectrogram includes an image, map, or other N- dimensional matrix or data structure depicting a spectrum of frequencies of ECG signals in the ECG data as they vary with time. In some implementations, a spectrogram may be generated by computing a Fourier transform of the ECG signals in the ECG data and constructing the spectrogram based on the coefficients of the Fourier transform. As an example, the scalogram may be depicted as a heat map or other image.

[0050] Additionally or alternatively, other images, maps, or other N-dimensional (for N > 2) matrices or other data structures may be generated from the ECG signals in the ECG signal data.

[0051] A trained neural network (or other suitable machine learning algorithm) is then accessed with the computer system, as indicated at step 304. In general, the neural network is trained, or has been trained, on training data in order to detect, identify, or otherwise characterize patterns in scalogram, spectrogram, other N-dimensional data generated from ECG data, or other transformed ECG data that are indicative of a respiratory7infection or illness in the subj ect from whom the ECG data were acquired.

[0052] Accessing the trained neural network may include accessing network parameters (e.g.. weights, biases, or both) that have been optimized or otherwise estimated by training the neural network on training data. In some instances, retrieving the neural network can also include retrieving, constructing, or otherwise accessing the particular neural network architecture to be implemented. For instance, data pertaining to the layers in the neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be retrieved, selected, constructed, or otherwise accessed.

[0053] The transformed ECG data are then input to the one or more trained neural networks, generating output as classified feature data, as indicated at step 306. For example, the classified feature data may include a risk score. The risk score can provide physicians or other clinicians with a recommendation to consider additional monitoring for subjects whose scalogram and / or spectrogram data indicate the likelihood of the subject suffering from a particular medical condition.

[0054] As another example, the classified feature data may indicate the probability for a particular classification (i.e., the probability that the scalogram and / or spectrogram data include patterns, features, or characteristics indicative of detecting, differentiating, and / or determining the severity of one or more medical conditions).

[0055] Additionally or alternatively, the classified feature data may classify the scalogram and / or spectrogram data as indicating a particular medical condition (e.g., “COVID- 19 positive,” '‘COVID-19 negative,” “influenza positive,” “influenza negative,” “HPIV positive,” “HPIV negative,” etc.). The classified feature data may include a single classification output, or may include multiple classification outputs (e.g., an indication of more than one medical condition being positive and / or negative). In these instances, the classified feature datacan differentiate between different medical conditions. In still other embodiments, the classified feature data may indicate a severity of a medical condition. For example, the classified feature data may include a severity score that quantifies a severity of a medical condition.

[0056] The classified feature data generated by inputting the transformed ECG data to the trained neural network(s) can then be displayed to a user, stored for later use or further processing, or both, as indicated at step 308.

[0057] Referring now to FIG. 4, a flowchart is illustrated as setting forth the steps of an example method for training one or more neural networks (or other suitable machine learning algorithms) on training data, such that the one or more neural networks are trained to receive transformed ECG data as input data in order to generate classified feature data as output data, where the classified feature data are indicative of the presence of a respiratory infection.

[0058] In general, the neural network(s) can implement any number of different neural network architectures. For instance, the neural network(s) could implement a convolutional neural network, a residual neural network, or the like. Alternatively, the neural network(s) could be replaced with other suitable machine learning or artificial intelligence algorithms, such as those based on supervised learning, unsupervised learning, deep learning, ensemble learning, dimensionality reduction, and so on. In some implementations, a large language model, generative pre-trained transformer model, or other foundation model may also be used, as described in more detail below.

[0059] The method includes accessing training data with a computer system, as indicated at step 402. Accessing the training data may include retrieving such data from a memory or other suitable data storage device or medium. Alternatively, accessing the training data may include accessing ECG data with the computer system and generating the training data from the ECG data using the computer system.

[0060] In general, the training data can include scalogram, spectrogram, other N- dimensional data, or other forms of transformed ECG data generated from ECG data obtained from a plurality of subjects. The ECG data may be obtained using 12-lead configurations, or fewer leads (e.g., single lead, three leads, six leads, and the like). In some examples, the ECG data may be obtained using an RF-based sensor device.

[0061] Additionally, the training data may include other data, such as other health information collected from the subjects. In some embodiments, the training data may include ECG data that have been labeled (e.g., labeled as containing patterns, features, orcharacteristics indicative of an influenza infection; labeled as being collected from a subject suffering from an influenza infection; and the like).

[0062] The method can include assembling training data from scalogram, spectrogram, other N-dimensional data, or other forms of transformed ECG data generated from ECG data using a computer system. This step may include assembling the transformed ECG data into an appropriate data structure on which the machine learning algorithm can be trained. Assembling the training data may include assembling transformed ECG data and other relevant data. For instance, assembling the training data may include generating labeled data and including the labeled data in the training data. Labeled data may include transformed ECG data or other relevant data that have been labeled as belonging to, or otherwise being associated with, one or more different classifications or categories. For instance, labeled data may include transformed ECG data, and / or the underlying ECG data, that have been labeled as being associated with a subject suffering from a respiratory infection or illness.

[0063] In some instances, synthetic training data may be generated. As one nonlimiting example, techniques such as diffusion models can be modified to create realistic ECG data and / or transformed ECG data from text prompts. Synthetic training data produced by these models can augment real training data to increase diagnostic accuracy. This form of synthetic data can help solve the detection and treatment of uncommon diseases through Al learning problems for which training data is scarce.

[0064] One or more neural networks (or other suitable machine learning algorithms) are trained on the training data, as indicated at step 404. In general, the neural network can be trained by optimizing network parameters (e.g., weights, biases, or both) based on minimizing a loss function. As one non-limiting example, the loss function may be a mean squared error loss function.

[0065] Training a neural network may include initializing the neural network, such as by computing, estimating, or otherwise selecting initial network parameters (e.g., weights, biases, or both). During training, an artificial neural network receives the inputs for a training example and generates an output using the bias for each node, and the connections between each node and the corresponding weights. For instance, training data can be input to the initialized neural network, generating output as classified feature data. The artificial neural network then compares the generated output with the actual output of the training example in order to evaluate the quality of the classified feature data. For instance, the classified feature data can be passed to a loss function to compute an error. The current neural network can thenbe updated based on the calculated error (e.g., using backpropagation methods based on the calculated error). For instance, the current neural network can be updated by updating the network parameters (e g., weights, biases, or both) in order to minimize the loss according to the loss function. The training continues until a training condition is met. The training condition may correspond to, for example, a predetermined number of training examples being used, a minimum accuracy threshold being reached during training and validation, a predetermined number of validation iterations being completed, and the like. When the training condition has been met (e.g., by determining whether an error threshold or other stopping criterion has been satisfied), the current neural network and its associated network parameters represent the trained neural network. Different types of training processes can be used to adjust the bias values and the weights of the node connections based on the training examples. The training processes may include, for example, gradient descent, Newton’s method, conjugate gradient, quasi -Newton. Levenberg-Marquardt, among others.

[0066] The artificial neural network can be constructed or otherwise trained based on training data using one or more different learning techniques, such as supervised learning, unsupervised learning, reinforcement learning, ensemble learning, active learning, transfer learning, or other suitable learning techniques for neural netw orks. As an example, supervised learning involves presenting a computer system with example inputs and their actual outputs (e.g., categorizations). In these instances, the artificial neural network is configured to leam a general rule or model that maps the inputs to the outputs based on the provided example inputoutput pairs.

[0067] The one or more trained neural networks are then stored for later use, as indicated at step 406. Storing the neural network(s) may include storing network parameters (e.g.. weights, biases, or both), which have been computed or otherwise estimated by training the neural network(s) on the training data. Storing the trained neural network(s) may also include storing the particular neural network architecture to be implemented. For instance, data pertaining to the layers in the neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers, hyperparameters for layers) may be stored.

[0068] In some embodiments, foundation models can be used to additionally or alternatively process the ECG data or transformed ECG data (e.g., scalogram data, spectrogram data, other N-dimensional data generated from ECG data). Foundation models can receive various types of data (e.g., text data, image data, sound data, other ID. 2D, and / or 3D data types) and generate various types of clinically relevant outputs. For example, the foundationmodels may generate outputs as predictive scores, text outputs, classifications, and so on. As a non-limiting example, text outputs may include answers to questions posed by a clinical user (e.g., medical question answering), interpretive reports of ECG data and / or transformed ECG data, or other text-based reports and / or summaries of the input data. Beyond giving vital measurements and diagnostic capabilities, generating reports across temporal and spatial domains with the ECG data and / or transformed ECG data is advantageous for identifying if a patient’s vitals and / or conditions are improving, worsening, or staying the same.

[0069] As one example, a language model, such as a large language model (LLM), can be used. Large language modeling with electrocardiograms and medical images involves using machine learning models (e.g., deep learning models) to process and analyze medical images and associated clinical text data. This approach can combine natural language processing (NLP) with computer vision to understand the content of medical images or other physiologic signal data (e.g., ECG data, transformed ECG data) and extract relevant information from them. Advantageously, large language modeling can be applied to ECG data, transformed ECG data, and / or medical images to enable automatic analysis of the input data and to extract clinically relevant information, such as the presence of respiratory illness or abnormalities. This approach can help healthcare professionals make more accurate diagnoses and develop personalized treatment plans for patients.

[0070] One example method for large language modeling with ECG data and / or transformed ECG data uses CNNs to extract features from the input data, which are then fed into a recurrent neural network (RNN) to generate text descriptions of the ECG data and / or transformed ECG data. The RNN can also be used to generate clinical reports based on the input data. Another example approach is to use a transformer-based model, such as a BERT or GPT-4 model, to analyze both the ECG data (or transformed ECG data) and accompanying text data. These models can be pre-trained on large datasets of ECG data and / or transformed ECG data and associated text to improve their accuracy and ability to identify7relevant features in the input data.

[0071] As a non-limiting example, the ECG data and / or transformed ECG data can be provided as an input to the LLM. The ECG data and / or transformed ECG data are first tokenized and converted into a numerical format. The tokenized input data may then be applied to an embedding layer to transform each tokenized input into a high-dimensional vector that captures relationships between the ECG data and / or transformed ECG data. In some instances, the embedding layer may additionally or alternatively capture semantic relationships betweentext data and the ECG data and / or transformed ECG data. The resulting embedded vectors form a one-dimensional sequence that will be input to the LLM. Each element of the embedded vectors corresponds to a token in the tokenized input data.

[0072] Any suitable LLM can be used. As one non-limiting example, the LLM may be based on a recurrent layer model, such as a long short term memory' (LSTM) model. As another non-limiting example, the LLM may be based on an attention mechanism (e.g., transformer). For instance, the LLM may be based on a generative pre-trained transformer (GPT) model. In still other examples, the LLM may be based on combinations of such model types.

[0073] GPT is a ty pe of large language model that is pre-trained on a massive corpus of text data and can generate human-like language. Language models can also be trained on data from other modalities (e.g., images, audio recordings, videos, etc.) to enable more diverse capabilities, provide a stronger learning signal, and increase learning speed. The GPT model is based on the transformer architecture, which allows it to process long sequences of text efficiently. GPT can be used in a wide range of natural language processing (NLP) tasks, including language translation, text summarization, and question answering.

[0074] ECG data and transformed ECG data contain valuable information that can aid in the diagnosis and treatment of various medical conditions. By combining GPT-based language modeling with ECG data (or transformed ECG data) analysis, the disclosed systems and methods can analyze the input ECG data and / or transformed ECG data to generate textual reports summarizing the findings. For example, a GPT-based model can be trained on a large corpus of ECG data (and / or transformed ECG data) and medical reports and then used to generate reports for new ECG and / or transformed ECG data. The generated reports can include information such as the location and size of any abnormalities associated with a respiratory illness, as well as recommendations for further testing or treatment.

[0075] These LLMs can also be used to develop a chatbot based on a foundation model that can serve as a physician’s assistant to support more accurate diagnosis and tailored therapy selection. These capabilities can improve the accuracy and efficiency of patient care while increasing patient engagement and adherence to therapy. Once diagnosis is determined as a result, the output can be incorporated into the patient’s medical documentation through electronic medical records.

[0076] Once diagnosis is made with these models, clinical documents can be generated as described above. A foundation model can then generate tailored patient education materials and explain their care plan at the appropriate reading level based on the clinical documents.The models can also be used to draft a clinic note in real-time based on the results. As another advantage, the models can also be used to optimize clinic scheduling or to simplify generation of medical codes for billing (e g., current procedural terminology (CPT) codes), disease surveillance, and even automated follow-up reminders.

[0077] In some instances, the code to these models can be automated and / or updated using an LLM. GPT, or the like. For example, auto-GPT can be used to write and update its own code and execute scripts. This allows the model to recursively debug, develop, and selfimprove. While input data are applied to an auto-GPT-based model, the model can update itself automatically.

[0078] As described herein, multimodal language modeling can be used to receive multiple sources of information to train a language model. In the context of ECG data and transformed ECG data, this can mean combining textual information from clinical notes, laboratories, or reports with visual information from ECG data, transformed ECG data, or other sources, including for example medical images such as x-rays, CT scans, or MRIs. One approach to multimodal language modeling is to use the GPT architecture, which can be effective in a variety of NLP tasks. The GPT architecture is based on a transformer network, which can learn to model long-range dependencies between words in a sentence. To adapt GPT for multimodal language modeling with ECG data and / or transformed ECG data, visual information can be incorporated into the GPT model through pretraining with contrastive learning. This process involves training the model to predict which ECG and text pairs are related, while also ensuring that unrelated pairs are distinguishable from each other. The resulting model can then be fine-tuned for specific tasks, such as ECG data captioning, medical report generation, or disease diagnosis. For example, a model trained on clinical notes and ECG signals or transformed ECG signals could be fine-tuned to generate reports to predict the presence of certain diseases (e g., respiratory illnesses) based on the ECG data or transformed ECG data.

[0079] As another example, a visual transformer-based model can be used. In these instances, higher-dimensional inputs can be provided to the underlying foundation model. For example, the ECG data and / or transformed ECG data may be tokenized and embedded, as described above, into a 2D or other N-dimensional (N > 1) vector, matrix, or tensor. These higher dimensional input data can then be applied to a suitable foundation model, such as a visual transformer-based model.

[0080] Vision-language processing can be improved by using paired samples sharing semantics. For instance, given an image and text pair, the text should describe the image with minimal extraneous detail. Without knowledge of this initial image, temporal information in the text modality (e.g., “condition is improving,” “condition is worsening,” “condition is stable”) could pertain to any image including “condition”, creating vagueness during contrastive training. Vision-language processing implementations can, in some instances, assume alignment between single images and reports, removing temporal content from reports in training data to prevent hallucinations in downstream report generation.

[0081] In other implementations of vision-language processing, temporal information can provide complementary self-supervision by using an existing structure without requiring additional data. Rather than treating all image-report pairs in the dataset as independent, temporal correlations can be used by making previous images available for comparison to a given report. A temporal vision-language processing pre-training framework can be learned from this structure. In some implementations, a multi-image encoder that can handle the absence of previous images and potential spatial misalignment between images across time can be used in this vision-language processing. Prior images can be accounted for where available, thereby removing cross-modal ambiguity. Linking multiple images has the advantage of improving image and text models and performance on temporal image classification and report generation. Prefixing the prior report can significantly improve performance. When available during training and fine-tuning, earlier images and labels can also be accounted for.

[0082] As one non-limiting example, a convolutional neural network (CNN) or LLM visual transformer hybrid multi-image encoder can be trained jointly with a text model. The hybrid model can provide improved processing for tasks in both single-image and multi-image structures, achieving performance on disease progression classification, phrase grounding, and document generation, while offering consistent modifications on disease category and sentence-similarity tasks. The similarity between the ECG data, transformed ECG data, or other signals and text embeddings can be computed to obtain probabilities, which can be used to classify the various categories of the ECG data (or transformed ECG data) and then reported textually. The outputs can also include classified feature data indicating risk categories of no risk, low' risk, medium risk, and high risk. These outputs can be displayed into risk and probability categories where the model draw s more attention for action or testing if it falls into a high risk category versus normal.

[0083] As yet another example, a sound transformer-based model can be used. In these instances, the ECG data and / or transformed ECG data may be converted to an audio data format before being input to the sound transformer-based model. As above, these audio data can be tokenized and embedded into one-dimensional embedded vectors that are applied to the sound transformer-based model. Additional audio data (e.g., stethoscope recordings) may also be input to the sound transformer-based model.

[0084] Referring now to FIGS. 5 and 6, an example of a wearable device 600 for recording ECG or other physiological data and / or generating classified feature data in accordance with some embodiments is shown.

[0085] As shown in FIG. 5, the wearable device 600 can include a device that is configured to be worn on a user’s wrist or limb (e.g., a smart watch, a band, a bracelet), placed on a user’s skin (e.g., a wearable patch), or worn as an article of clothing (e g., a shirt). The wearable device 600 can be in communication with an external device 610 and / or a server 612 either directly or indirectly via a network 608.

[0086] The network 608 may be a long-range wireless network such as the Internet, a local area network (LAN), a wide area network (WAN), or a combination thereof. In other embodiments, the network 608 may be a short-range wireless communication network, and in yet other embodiments, the network 608 may be a wired network using, for example, USB cables. In some embodiments, the network 608 may include both wired and wireless devices and connections. Similarly, the server 612 may transmit information to the external device 610 to be forwarded to the wearable device 600.

[0087] In some embodiments, the wearable device 600 communicates directly with the external device 610. For example, the wearable device 600 can transmit data (e.g., physiological sensor data, other data collected or generated by the wearable device 600) to the external device 610. Similarly, the wearable device 600 can receive data (e.g., settings, machine learning algorithm parameters, firmware updates, etc.) from the external device 610.

[0088] In some other embodiments, the wearable device 600 bypasses the external device 610 to access the network 608 and communicate with the server 612 via the network 608. In some embodiments, the wearable device 600 is equipped with a long-range transceiver instead of or in addition to a short-range transceiver. In such embodiments, the wearable device 600 communicates directly with the server 612 or with the server 612 via the network 608 (in either case, bypassing the external device 610).

[0089] In some embodiments, the wearable device 600 may communicate directly with both the server 612 and the external device 610. In such embodiments, the external device 610 may, for example, generate a graphical user interface to facilitate control and programming of the wearable device 600 while the server 612 may store and analyze larger amounts of data (e.g., training data, trained machine learning models and parameters) for future programming or operation of the wearable device 600. In other embodiments, the wearable device 600 may communicate directly with the server 612 without utilizing a short-range communication protocol with the external device 610.

[0090] In the illustrated embodiment, the wearable device 600 communicates with the external device 610. The external device 610 may include, for example, a smartphone, a tablet computer, a cellular phone, a laptop computer, a smart watch, another wearable device, and the like. The wearable device 600 communicates with the external device 610, for example, to transmit at least a portion of the physiological sensor data or other data collected or generated by the wearable device 600, which in some instances may include classified feature data generated by the wearable device 600.

[0091] In some embodiments, the external device 610 may include a short-range transceiver to communicate with the wearable device 600, and a long-range transceiver to communicate with the server 612. In the illustrated embodiment, the wearable device 600 can also include a transceiver to communicate with the external device 610 via, for example, a short-range communication protocol such as Bluetooth®. In some embodiments, the external device 610 bridges the communication between wearable device 600 and the server 612. That is, the wearable device 600 transmits data to the external device 610, and the external device 610 forwards the data from wearable device 600 to the server 612 over the network 608.

[0092] The server 612 includes a server electronic control assembly having a server electronic processor, a server memory, and a transceiver. The transceiver allows the server 612 to communicate with the wearable device 600, the external device 610, or both. The server electronic processor receives physiological sensor data or other data collected with or generated by the wearable device 600, and stores the received data in the server memory. The server 612 may maintain a database (e.g., on the server memory) for containing physiological data, training data, trained machine learning controls (e.g., trained machine learning models and / or algorithms), artificial intelligence controls (e.g., rules and / or other control logic implemented in an artificial intelligence model and / or algorithm), and the like.

[0093] Although illustrated as a single device, the server 612 may be a distributed device in which the server electronic processor and server memory are distributed among two or more units that are communicatively coupled (e g., via the network 608).

[0094] The wearable device 600 includes an electronic controller 620, a power source 652, a wireless communication device 660, and one or more sensors 672, among other components. In some embodiments, the wearable device 600 may not include a wireless communication device 660.

[0095] The electronic controller 620 can include an electronic processor 630 and memory 640. The electronic processor 630 and the memory 640 can communicate over one or more control buses, data buses, etc., which can include a device communication bus 676. The control and / or data buses are shown generally in FIG. 6 for illustrative purposes. The use of one or more control and / or data buses for the interconnection between and communication among the various modules, circuits, and components would be known to a person skilled in the art.

[0096] The electronic processor 630 can be configured to communicate with the memory 640 to store data and retrieve stored data. The electronic processor 630 can be configured to receive instructions 642 and data from the memory' 640 and execute, among other things, the instructions 642. In particular, the electronic processor 630 executes instructions 642 stored in the memory 640. Thus, the electronic controller 620 coupled with the electronic processor 630 and the memory 640 can be configured to perform the methods described herein (e g., the process 100 of FIG. 1).

[0097] The memory' 640 can include read-only memory' (ROM), random access memory (RAM), other non-transitory computer-readable media, or a combination thereof. The memory 640 can include instructions 642 for the electronic processor 630 to execute. The instructions 642 can include software executable by the electronic processor 630 to enable the electronic controller 620 to, among other things, receive data and / or commands, transmit data, and the like. The software can include, for example, firmware, one or more applications, program data, filters, rules, one or more program modules, and other executable instructions.

[0098] The electronic processor 630 is configured to retrieve from memory 640 and execute, among other things, instructions related to the control processes and methods described herein. The electronic processor 630 is also configured to store data on the memory 640 including physiological sensor data, classified feature data, and the like.

[0099] The wearable device 600 receives electrical power from the power source 652, which as an example may include a battery. Additionally or alternatively, the power source 652 may include an external power source (e.g., a wall outlet when the wearable device 600 is connected to a wall outlet).

[0100] In some embodiments, the wearable device 600 may also include a wireless communication device 660. In these embodiments, the wireless communication device 660 is coupled to the electronic controller 620 (e.g., via the device communication bus 676). The wireless communication device 660 may include, for example, a radio transceiver and antenna, a memory', and an electronic processor. In some examples, the wireless communication device 660 can further include a global navigation satellite system (GNSS) receiver configured to receive signals from GNSS satellites (e.g., global positioning system (GPS) satellites), land- based transmitters, etc. The radio transceiver and antenna operate together to send and receive wireless messages to and from the external device 610, the server 612, and the like. The memory of the wireless communication device 660 stores instructions to be implemented by the electronic processor of the wireless communication device 660 and / or may store data related to communications between the wearable device 600 and the external device 610 and / or the server 612.

[0101] The electronic processor for the wireless communication device 660 controls wireless communications between the wearable device 600 and the external device 610 and / or the server 612. For example, the electronic processor of the wireless communication device 660 buffers incoming and / or outgoing data, communicates with the electronic processor 630, and determines the communication protocol and / or settings to use in wireless communications.

[0102] In some embodiments, the wireless communication device 660 is a Bluetooth® controller. The Bluetooth® controller communicates with the external device 610 and / or the server 612 employing the Bluetooth® protocol. In such embodiments, therefore, the external device 610 and / or the server 612 and the wearable device 600 are within a communication range (i.e., in proximity) of each other while they exchange data. In other embodiments, the wireless communication device 660 communicates using other protocols (e.g., Wi-Fi, cellular protocols, a proprietary protocol, etc.) over a different type of wireless network. For example, the wireless communication device 660 may be configured to communicate via Wi-Fi through a wide area network such as the Internet or a local area netw ork, or to communicate through a piconet (e.g.. using infrared or NFC communications). The communication via the wirelesscommunication device 660 may be encrypted to protect the data exchanged between the wearable device 600 and the external device 610 and / or the server 612 from third parties.

[0103] The wireless communication device 660, in some embodiments, exports physiological sensor data or other data collected with or generated by the wearable device 600 (e.g., classified feature data). The wireless communication device 660 also enables the wearable device 600 to sync or otherwise communicate data with other devices, such as an external device 610 that is configured as a smart watch or smartphone. In some instances, the wearable device 600 can send and receive additional health information for the user (e.g., by syncing with a heartrate monitor, smart watch, or other wearable device).

[0104] The wearable device 600 can include or be coupled to one or more physiological sensors 672. For example, the physiological sensors 672 can include ECG leads, electrodes, or other conductive elements capable of measuring electrophysiology signals. That is, in some embodiments, the physiological sensors 672 are capable of recording or otherwise measuring ECG data. The physiological sensors 672 can implement various lead or electrode configurations, including a 12-lead configuration, a 6-lead configuration, a 3-lead configuration, a 1-lead configuration, or the like. Additionally or alternatively, the physiological sensors 672 can also record or measure other electrophysiology signals, such as electromyography (EMG) signals.

[0105] In other embodiments, the physiological sensors 672 can include additional sensors, such photoplethysmography (PPG) sensors or PPG sensing circuits, temperature sensors or temperature sensing circuits, inertial sensors or inertial sensing circuits (e.g., accelerometers, gy roscopes, magnetometers), a pressure sensor or pressure sensing circuit (e.g., a barometer), or the like.

[0106] In some embodiments, the wearable device 600 can include one or more inputs 690 (e.g., one or more buttons, switches, touchscreen, and the like) that are coupled to the electronic controller 620 and allow a user to interact with and control the wearable device 600. In some embodiments, the input 690 includes an interactive graphical user interface (GUI) element that enables user interaction with the wearable device 600.

[0107] In some embodiments, the wearable device 600 may include one or more outputs 692 that are also coupled to the electronic controller 620. The output(s) 692 can receive control signals from the electronic controller 620 to present data or information to a user in response, or to generate other visual, audio, or other outputs. As one example, the output(s) 692 can generate a visual signal to convey information regarding the physiological sensor data.other health data, generated classified feature data, or the like. The output(s) 692 may include, for example. LEDs or a display screen and may generate various signals indicative of, for example, physiological sensor data, other health data, generated classified feature data, or the like. For example, the output(s) 692 may indicate the detection of a respiratory' infection or illness in a subject based on classified feature data generated by inputting ECG data measured by the physiological sensors 672 to a trained neural network or other machine learning model.

[0108] FIG. 7 shows an example of a system 700 for detecting respiratory illness from ECG data, and / or scalogram or spectrogram data generated from ECG data, in accordance with some embodiments of the systems and methods described in the present disclosure. As shown in FIG. 7, a computing device 750 can receive one or more types of data (e.g., ECG data, scalogram data, spectrogram data) from data source 702. In some embodiments, computing device 750 can execute at least a portion of a ECG-based respiratory' illness detection system 704 to detect respiratory' illness from data received from the data source 702.

[0109] Additionally or alternatively, in some embodiments, the computing device 750 can communicate information about data received from the data source 702 to a server 752 over a communication network 754, which can execute at least a portion of the ECG-based respiratory illness detection system 704. In such embodiments, the server 752 can return information to the computing device 750 (and / or any other suitable computing device) indicative of an output of the ECG-based respiratory’ illness detection system 704.

[0110] In some embodiments, computing device 750 and / or server 752 can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device 750 and / or server 752 can also reconstruct images from the data.

[0111] In some embodiments, data source 702 can be any suitable source of data (e.g., ECG data, images or maps generated from ECG data, processed ECG data), another computing device (e.g., a server storing ECG data, images or maps generated from ECG data, processed ECG data), and so on. In some embodiments, data source 702 can be local to computing device 750. For example, data source 702 can be incorporated with computing device 750 (e.g., computing device 750 can be configured as part of a device for measuring, recording, estimating, acquiring, or otherwise collecting or storing data). As another example, data source 702 can be connected to computing device 750 by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, data source 702 can be located locallyand / or remotely from computing device 750, and can communicate data to computing device 750 (and / or server 752) via a communication network (e.g.. communication network 754).

[0112] In some embodiments, communication network 754 can be any suitable communication network or combination of communication networks. For example, communication network 754 can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), other types of wireless network, a wired network, and so on. In some embodiments, communication network 754 can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-pnvate network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in FIG. 7 can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links. Bluetooth links, cellular links, and so on.

[0113] Referring now to FIG. 8, an example of hardware 800 that can be used to implement data source 702, computing device 750, and server 752 in accordance with some embodiments of the systems and methods described in the present disclosure is shown.

[0114] As shown in FIG. 8, in some embodiments, computing device 750 can include a processor 802, a display 804, one or more inputs 806, one or more communication systems 808, and / or memory 810. In some embodiments, processor 802 can be any suitable hardware processor or combination of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), and so on. In some embodiments, display 804 can include any suitable display devices, such as a liquid cry stal display (LCD) screen, a light-emitting diode (LED) display, an organic LED (OLED) display, an electrophoretic display (e.g., an “e-ink” display), a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 806 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0115] In some embodiments, communications systems 808 can include any suitable hardware, firmware, and / or software for communicating information over communication network 754 and / or any other suitable communication networks. For example, communications systems 808 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 808 can includehardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0116] In some embodiments, memory 810 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 802 to present content using display 804, to communicate with server 752 via communications system(s) 808, and so on. Memory 810 can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory 810 can include random-access memory (RAM), read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), other forms of volatile memory, other forms of non-volatile memory, one or more forms of semivolatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 810 can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device 750. In such embodiments, processor 802 can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server 752, transmit information to server 752, and so on. For example, the processor 802 and the memory 810 can be configured to perform the methods described herein (e.g., the method of FIG. 1, the method of FIG. 2, the method of FIG. 3, the method of FIG. 4).

[0117] In some embodiments, server 752 can include a processor 812. a display 814, one or more inputs 816. one or more communications systems 818. and / or memory 820. In some embodiments, processor 812 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display 814 can include any suitable display devices, such as an LCD screen, LED display, OLED display, electrophoretic display, a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs 816 can include any suitable input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on.

[0118] In some embodiments, communications systems 818 can include any suitable hardware, firmware, and / or software for communicating information over communication network 754 and / or any other suitable communication networks. For example, communications systems 818 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 818 can include hardware, firmware, and / or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0119] In some embodiments, memory' 820 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 812 to present content using display 814, to communicate with one or more computing devices 750, and so on. Memory 820 can include any suitable volatile memory', non-volatile memory', storage, or any suitable combination thereof. For example, memory 820 can include RAM. ROM, EPROM, EEPROM, other ty pes of volatile memory, other ty pes of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 820 can have encoded thereon a server program for controlling operation of server 752. In such embodiments, processor 812 can execute at least a portion of the server program to transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 750, receive information and / or content from one or more computing devices 750, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on.

[0120] In some embodiments, the server 752 is configured to perform the methods described in the present disclosure. For example, the processor 812 and memory 820 can be configured to perform the methods described herein (e.g., the method of FIG. 1, the method of FIG. 2, the method of FIG. 3, the method of FIG. 4).

[0121] In some embodiments, data source 702 can include a processor 822, one or more data acquisition systems 824, one or more communications systems 826. and / or memory 828. In some embodiments, processor 822 can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more data acquisition systems 824 are generally configured to acquire electrophysiology data such as ECG data, and can include one or more measurement electrodes. Additionally or alternatively, in some embodiments, the one or more data acquisition systems 824 can include any suitable hardyvare, firmware, and / or software for coupling to and / or controlling operations of an electrophysiology' measurement device. In some embodiments, one or more portions of the data acquisition system(s) 824 can be removable and / or replaceable.

[0122] Note that, although not shown, data source 702 can include any suitable inputs and / or outputs. For example, data source 702 can include input devices and / or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, data source 702 can include any suitabledisplay devices, such as an LCD screen, an LED display, an OLED display, an electrophoretic display, a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on.

[0123] In some embodiments, communications systems 826 can include any suitable hardware, firmware, and / or software for communicating information to computing device 750 (and, in some embodiments, over communication network 754 and / or any other suitable communication networks). For example, communications systems 826 can include one or more transceivers, one or more communication chips and / or chip sets, and so on. In a more particular example, communications systems 826 can include hardware, firmware, and / or software that can be used to establish a wired connection using any suitable port and / or communication standard (e.g., VGA, DVI video, USB, RS-232, etc ), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on.

[0124] In some embodiments, memory 828 can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor 822 to control the one or more data acquisition systems 824, and / or receive data from the one or more data acquisition systems 824; to generate images from data; present content (e.g., data, images, a user interface) using a display; communicate with one or more computing devices 750; and so on. Memory 828 can include any suitable volatile memory7, non-volatile memory7, storage, or any suitable combination thereof. For example, memory 828 can include RAM, ROM, EPROM, EEPROM, other ty pes of volatile memory, other ty pes of non-volatile memory, one or more types of semi-volatile memory, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory 828 can have encoded thereon, or otherwise stored therein, a program for controlling operation of data source 702. In such embodiments, processor 822 can execute at least a portion of the program to generate images, transmit information and / or content (e.g., data, images, a user interface) to one or more computing devices 750, receive information and / or content from one or more computing devices 750, receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on.

[0125] In some embodiments, any suitable computer-readable media can be used for storing instructions for performing the functions and / or processes described herein. For example, in some embodiments, computer-readable media can be transitory7or non-transitory. For example, non-transitory computer-readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs.Blu-ray discs), semiconductor media (e.g., RAM, flash memory', EPROM, EEPROM), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and / or any suitable tangible media. As another example, transitory computer- readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and / or any suitable intangible media.

[0126] As used herein in the context of computer implementation, unless otherwise specified or limited, the terms “component,” “system,” “module,” “framework,” and the like are intended to encompass part or all of computer-related systems that include hardware, software, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a processor device, a process being executed (or executable) by a processor device, an object, an executable, a thread of execution, a computer program, or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components (or system, module, and so on) may reside within a process or thread of execution, may be localized on one computer, may be distributed between two or more computers or other processor devices, or may be included within another component (or system, module, and so on).

[0127] In some implementations, devices or sy stems disclosed herein can be utilized or installed using methods embodying aspects of the disclosure. Correspondingly, description herein of particular features, capabilities, or intended purposes of a device or system is generally intended to inherently include disclosure of a method of using such features for the intended purposes, a method of implementing such capabilities, and a method of installing disclosed (or otherwise known) components to support these purposes or capabilities. Similarly, unless otherwise indicated or limited, discussion herein of any method of manufacturing or using a particular device or system, including installing the device or system, is intended to inherently include disclosure, as embodiments of the disclosure, of the utilized features and implemented capabilities of such device or system.

[0128] The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.

Claims

CLAIMS1. A method for generating classified feature data indicative of an influenza infection in a subject, the method comprising:(a) accessing electrocardiography (ECG) data using a computer system, wherein the ECG data are acquired from a subject;(b) accessing a machine learning model with the computer system, wherein the machine learning model has been trained on training data to detect a presence of an influenza infection based on ECG data;(c) applying the ECG data to the machine learning model using the computer system, generating output as classified feature data indicative of an influenza infection in the subject; and(d) outputting the classified feature data to a user.

2. The method of claim 1, wherein the ECG data are obtained with a 12-lead ECG system.

3. The method of claim 1. wherein the ECG data are obtained with fewer than twelve leads.

4. The method of claim 3. wherein the ECG data are obtained with six leads.

5. The method of claim 3, wherein the ECG data are obtained with a single lead.

6. The method of claim 1 , wherein the ECG data are obtained with a radiofrequency (RF)-based biophysical sensor.

7. The method of claim 1, wherein accessing the ECG data comprises acquiring the ECG data with one or more physiological sensors.

8. The method of claim 7. wherein the ECG data are acquired using one or more physiological sensors coupled to a wearable device.

9. The method of claim 7. wherein the ECG data are acquired using one or more physiological sensors not in contact with the subject.

10. The method of claim 1, wherein the machine learning model comprises a neural network.

11. The method of claim 1 , wherein the machine learning model comprises a large language model (LLM).

12. The method of claim 11, wherein the LLM implements a generative pretrained transformer (GPT) architecture.

13. The method of claim 11, wherein the classified feature data include text data comprising a report.

14. The method of claim 13, wherein the report indicates whether the subject’s condition is improving, worsening, or staying the same.

15. The method of claim 13, wherein the report indicates recommendations for further testing or treatment of the subj ect.

16. The method of claim 13, wherein the report includes patient education materials that explain a care plan tailored to the subject.

17. The method of claim 13, wherein the report comprises clinic notes.

18. The method of claim 13, wherein the report comprises medical billing codes.

19. The method of claim 1, wherein the classified feature data comprise a risk score for the subject having the influenza infection.

20. The method of claim 1. wherein the classified feature data comprise a classification of the ECG data as being indicative of the influenza infection.

21. The method of claim 1, wherein the machine learning model comprises a sound transformer-based model and applying the ECG data to the machine learning model comprises converting the ECG data to an audio data format before being applied to the sound transformer-based model.

22. A method for generating classified feature data indicative of a respiratory- infection in a subject, the method comprising:(a) accessing at least one of scalogram data or spectrogram data using a computer system, wherein the at least one of scalogram data or spectrogram data are generated from electrocardiography (ECG) data acquired from a subject;(b) accessing a machine learning model with the computer system, wherein the machine learning model has been trained on training data to detect a presence of a respiratory infection based on at least one of scalograms or spectrograms generated from ECG data;(c) applying the at least one of scalogram data or spectrogram data to the machine learning model using the computer system, generating output as classified feature data indicative of a respiratory infection in the subject: and(d) outputting the classified feature data to a user.

23. The method of claim 22, wherein the ECG data are obtained with a 12-lead ECG system.

24. The method of claim 22, wherein the ECG data are obtained with fewer than twelve leads.

25. The method of claim 24, wherein the ECG data are obtained with six leads.

26. The method of claim 24, wherein the ECG data are obtained with a single lead.

27. The method of claim 22, wherein the ECG data are obtained with a radiofrequency (RF)-based biophysical sensor.

28. The method of claim 22, wherein accessing the at least one of scalogram data or spectrogram data comprises acquiring the ECG data with one or more physiological sensors and generating the at least one of scalogram data or spectrogram data from the ECG data using the computer system.

29. The method of claim 28, wherein the ECG data are acquired using one or more physiological sensors coupled to a wearable device.

30. The method of claim 28, wherein the ECG data are acquired using one or more physiological sensors not in contact with the subject.

31. The method of claim 22, wherein the machine learning model comprises a neural network.

32. The method of claim 22, wherein the machine learning model comprises a large language model (LLM).

33. The method of claim 32, wherein the LLM implements a generative pretrained transformer (GPT) architecture.

34. The method of claim 32, wherein the classified feature data include text data comprising a report.

35. The method of claim 34, wherein the report indicates whether the subject's condition is improving, worsening, or staying the same.

36. The method of claim 34, wherein the report indicates recommendations for further testing or treatment of the subj ect.

37. The method of claim 34, wherein the report includes patient education materials that explain a care plan tailored to the subject.

38. The method of claim 34, wherein the report comprises clinic notes.

39. The method of claim 34, wherein the report comprises medical billing codes.

40. The method of claim 22, wherein the respiratory infection comprises an influenza infection such that the classified feature data are indicative of the subject having the influenza infection.

41. The method of claim 22, wherein the respiratory infection comprises a COVID- 19 infection such that the classified feature data are indicative of the subject having the COVID-19 infection.

42. The method of claim 22, wherein the respiratory infection comprises a human parainfluenza virus (HP IV) infection such that the classified feature data are indicative of the subject having the HPIV infection.

43. The method of claim 22, wherein the respiratory infection comprises a haemophilus influenzae infection such that the classified feature data are indicative of the subject having the haemophilus influenzae infection.

44. The method of claim 22, wherein the respiratory infection comprises a bacterial infection such that the classified feature data are indicative of the subject having the bacterial infection.

45. The method of claim 22, wherein the classified feature data comprise a risk score for the subject having the respiratory infection.

46. The method of claim 22, wherein the classified feature data comprise a classification of the at least one of scalogram data or spectrogram data as being indicative of the respiratory infection.

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