TWO-LEAD QT INTERVAL PREDICTION
Embodiments of the present disclosure provide a mobile electrocardiogram (ECG) sensor comprising an electrode assembly comprising electrodes, wherein the electrode assembly senses heart-related signals when in contact with a body of a user, and produces electrical signals representing the sensed heart-related signals. The ECG sensor further comprises a processing device, operatively coupled to the electrode assembly, the processing device to provide the sensed heart-related signals to a machine learning module trained to predict a twelve-lead QT interval (QTc) value from the mobile ECG sensor comprising less than twelve leads. The ECG sensor also comprises a housing containing the electrode assembly and the processing device.
1 . (canceled)
2 . An apparatus comprising:
an electrode assembly configured to produce signals representing electrical activity of a user's heart; and
a processing device, operatively coupled to the electrode assembly, the processing device configured to:
train a machine learning (ML) model by:
for each of a plurality of training electrocardiogram (ECG) measurements:
analyzing, using the ML model, the training ECG measurement to generate an output;
comparing the output to a corresponding QT interval label to generate an error that is based the output and the corresponding QT interval label; and
updating the ML model based on the error;
provide the signals representing the electrical activity of the user's heart to the ML model to predict a twelve-lead QT interval (QTc) value based on the signals; and
analyze, using the ML model, the predicted QTc value to determine whether a health anomaly is present.
3 . The apparatus of claim 2 , wherein for each of the plurality of training ECG
measurements:
the processing device generates the error using a loss function that is based on a cross entropy term of the output, the corresponding QT interval label, and a root mean squared QT interval average.
4 . The apparatus of claim 2 , wherein the signals correspond to an ECG measurement that is less than 12 leads.
5 . The apparatus of claim 2 , wherein the plurality of training ECG measurements are from a single user so that the ML model is trained to predict the twelve-lead QTc value for the single user.
6 . The apparatus of claim 2 , wherein the ML model is a deep neural network ML model.
7 . The apparatus of claim 2 , wherein the signals comprise Lead I and Lead II signals.
8 . The apparatus of claim 2 , wherein to analyze the predicted QTc value to determine whether a health anomaly is present, the processing device analyzes the predicted QTc value to determine whether QTc prolongation is present.
9 . The apparatus of claim 2 , wherein the processing device is further to send a notification to a device of the user in response to determining that the health anomaly is present.
10 . A method comprising:
generating signals representing electrical activity of a user's heart;
training a machine learning (ML) model by:
for each of a plurality of training electrocardiogram (ECG) measurements:
analyzing, using the ML model, the training ECG measurement to generate an output;
comparing the output to a corresponding QT interval label to generate an error that is based the output and the corresponding QT interval label; and
updating the ML model based on the error;
providing the signals representing the electrical activity of the user's heart to the ML model to predict a twelve-lead QT interval (QTc) value based on the signals; and
analyze, using the ML model, the predicted QTc value to determine whether a health anomaly is present.
11 . The method of claim 10 , wherein for each of the plurality of training ECG
measurements:
the error is generated using a loss function that is based on a cross entropy term of the output, the corresponding QT interval label, and a root mean squared QT interval average.
12 . The method of claim 10 , wherein the signals correspond to an ECG measurement that is less than 12 leads.
13 . The method of claim 10 , wherein the plurality of training ECG measurements are from a single user so that the ML model is trained to predict the twelve-lead QTc value for the single user.
14 . The method of claim 10 , wherein the ML model is a deep neural network ML model.
15 . The method of claim 10 , wherein the signals comprise Lead I and Lead II signals.
16 . The method of claim 15 , wherein analyzing the predicted QTc value to determine whether a health anomaly is present comprises analyzing the predicted QTc value to determine whether QTc prolongation is present.
17 . The method of claim 15 , wherein further comprising sending a notification to a device of the user in response to determining that the health anomaly is present.
18 . A system comprising:
a user device; and
a monitoring device comprising:
an electrode assembly configured to produce signals representing electrical activity of a user's heart; and
a processing device, operatively coupled to the electrode assembly, the processing device configured to:
train a machine learning (ML) model by:
for each of a plurality of training electrocardiogram (ECG) measurements:
analyzing, using the ML model, the training ECG measurement to generate an output;
comparing the output to a corresponding QT interval label to generate an error that is based the output and the corresponding QT interval label; and
updating the ML model based on the error;
provide the signals representing the electrical activity of the user's heart to the ML model to predict a twelve-lead QT interval (QTc) value based on the signals;
analyze, using the ML model, the predicted QTc value to determine whether a health anomaly is present; and
send a notification to the user device in response to determining that the health anomaly is present.
19 . The system of claim 18 , wherein for each of the plurality of training ECG
measurements:
the processing device generates the error using a loss function that is based on a cross entropy term of the output, the corresponding QT interval label, and a root mean squared QT interval average.
20 . The system of claim 18 , wherein the signals correspond to an ECG measurement that is less than 12 leads.
21 . The system of claim 18 , wherein the plurality of training ECG measurements are from a single user so that the ML model is trained to predict the twelve-lead QTc value for the single user.