SYSTEMS AND METHODS OF QT INTERVAL ANALYSIS
Disclosed systems and method receive electrocardiogram (EKG) data of a subject. The EKG data comprises a record of at least a full beat of the subject. The EKG data is input into a machine learning model to generate an output. The output can include a segmentation of the full beat or a measurement of a QT interval of the subject.
1 . A method comprising:
inputting each of a set of training electrocardiogram (EKG) measurements into a machine learning model to generate, for each of the set of training EKG measurements, a corresponding set of outputs comprising an estimated start time of a first feature and an estimated end time of a second feature;
for each of the training EKG measurements:
comparing the corresponding set of outputs to a first feature start time label and a second feature end time label of the training EKG measurement; and
adjusting one or more weight matrices of the machine learning model based on the comparing to train the machine learning model;
receiving EKG data of a subject, wherein the EKG data comprises at least a full beat of the subject; and
inputting, by a processing device, the EKG data into the machine learning model to generate a first output comprising at least a start time of the first feature of the EKG data and an end time of the second feature of the EKG data.
2 . The method of claim 1 , further comprising determining, by the processing device, whether a time difference between the start time of the first feature of the EKG data and the end time of the second feature of the EKG data is greater than a threshold.
3 . The method of claim 2 , further comprising generating an alert indicating that the time difference satisfies the threshold in response to determining that the time difference is greater than the threshold.
4 . The method of claim 3 , further comprising determining a previous interval between the first feature and the second feature for the subject and generating the threshold based at least in part on the previous interval.
5 . The method of claim 1 , wherein the machine learning model comprises a neural network comprising one or more of a convolutional layer or a recurrent layer.
6 . The method of claim 1 , wherein the first feature is associated with one of a J-point, a T-wave, a P-wave, an R-wave, or an S-wave.
7 . The method of claim 1 , wherein the second feature is associated with one of a J-point, a T-wave, a P-wave, an R-wave, or an S-wave.
8 . A system comprising:
a memory; and
a processing device operatively coupled to the memory, the processing device to:
input each of a set of training electrocardiogram (EKG) measurements into a machine learning model to generate, for each of the set of training EKG measurements, a corresponding set of outputs comprising an estimated start time of a first feature and an estimated end time of a second feature;
for each of the training EKG measurements:
compare the corresponding set of outputs to a first feature start time label and a second feature end time label of the training EKG measurement; and
adjust one or more weight matrices of the machine learning model based on the comparing to train the machine learning model;
receive EKG data of a subject, wherein the EKG data comprises at least a full beat of the subject; and
input, by a processing device, the EKG data into the machine learning model to generate a first output comprising at least a start time of the first feature of the EKG data and an end time of the second feature of the EKG data.
9 . The system of claim 8 , wherein the processing device is further to:
determine whether a time difference between the start time of the first feature of the EKG data and the end time of the second feature of the EKG data is greater than a threshold.
10 . The system of claim 9 , wherein the processing device is further to:
generate an alert indicating that the time difference satisfies the threshold in response to determining that the time difference is greater than the threshold.
11 . The system of claim 10 , wherein the processing device is further to:
determine a previous interval between the first feature and the second feature for the subject and generate the threshold based at least in part on the previous interval.
12 . The system of claim 8 , wherein the machine learning model comprises a neural network comprising one or more of a convolutional layer or a recurrent layer.
13 . The system of claim 8 , wherein the first feature is associated with one of a J-point, a T-wave, a P-wave, an R-wave, or an S-wave.
14 . The system of claim 8 , wherein the second feature is associated with one of a J-point, a T-wave, a P-wave, an R-wave, or an S-wave.
15 . A non-transitory computer-readable medium having instructions stored thereon which, when executed by a processing device, cause the processing device to:
input each of a set of training electrocardiogram (EKG) measurements into a machine learning model to generate, for each of the set of training EKG measurements, a corresponding set of outputs comprising an estimated start time of a first feature and an estimated end time of a second feature;
for each of the training EKG measurements:
compare the corresponding set of outputs to a first feature start time label and a second feature end time label of the training EKG measurement; and
adjust one or more weight matrices of the machine learning model based on the comparing to train the machine learning model;
receive EKG data of a subject, wherein the EKG data comprises at least a full beat of the subject; and
input, by a processing device, the EKG data into the machine learning model to generate a first output comprising at least a start time of the first feature of the EKG data and an end time of the second feature of the EKG data.
16 . The non-transitory computer-readable medium of claim 15 , wherein the processing device is further to:
determine whether a time difference between the start time of the first feature of the EKG data and the end time of the second feature of the EKG data is greater than a threshold.
17 . The non-transitory computer-readable medium of claim 16 , wherein the processing device is further to:
generate an alert indicating that the time difference satisfies the threshold in response to determining that the time difference is greater than the threshold.
18 . The non-transitory computer-readable medium of claim 17 , wherein the processing device is further to:
determine a previous interval between the first feature and the second feature for the subject and generate the threshold based at least in part on the previous interval.
19 . The non-transitory computer-readable medium of claim 15 , wherein the machine learning model comprises a neural network comprising one or more of a convolutional layer or a recurrent layer.
20 . The non-transitory computer-readable medium of claim 15 , wherein the first feature is associated with one of a J-point, a T-wave, a P-wave, an R-wave, or an S-wave.