Apparatus and method for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data
Apparatus and method for generating pseudo-EGM data from ECG data are disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to generate EGM model training data, wherein generating the EGM model training data includes receiving the EGM model training data, wherein the EGM model training data includes exemplary ECG data correlated to exemplary EGM data and time synchronizing the exemplary ECG data and the exemplary EGM data, train an EGM machine-learning model using the EGM model training data, receive subject data, wherein the subject data includes subject ECG data and generate subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.
1. An apparatus for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data, the apparatus comprising:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
generate EGM model training data, wherein generating the EGM model training data comprises:
receiving the EGM model training data, wherein the EGM model training data comprises exemplary ECG data correlated to exemplary EGM data; and
time synchronizing the exemplary ECG data and the exemplary EGM data using a synchronization machine learning model trained using synchronization training data configured to correlate time synchronized ECG data and time synchronized EGM data, wherein the synchronization training data is iteratively updated using a feedback loop;
train an EGM machine-learning model using the EGM model training data;
receive subject data, wherein the subject data comprises subject ECG data; and
generate subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.
2. The apparatus of claim 1 , wherein generating the EGM model training data comprises:
determining a first disturbance within the exemplary ECG data and the exemplary EGM data; and
aligning the exemplary ECG data and the exemplary EGM data as a function of the first disturbance.
3. The apparatus of claim 1 , wherein generating the EGM model training data comprises:
updating the EGM model training data as a function of an input and output of the trained EGM machine-learning model; and
iteratively training the EGM machine-learning model as a function of the updated EGM model training data.
4. The apparatus of claim 1 , wherein receiving the subject ECG data comprises receiving the subject ECG data from at least an ECG sensor.
5. The apparatus of claim 4 , wherein the at least an ECG sensor comprises a 12 lead ECG.
6. The apparatus of claim 1 , wherein generating the subject EGM data as a function of the ECG data comprises:
time synchronizing the subject ECG data with the EGM model training data;
splitting the synchronized subject ECG data into a plurality of segments; and
generating the subject EGM data as a function of the plurality of segments of the synchronized subject ECG data.
7. The apparatus of claim 1 , wherein the subject EGM data comprises a continuous distribution of EGM data.
8. The apparatus of claim 1 , wherein generating the subject EGM data as a function of the subject ECG data comprises:
classifying the subject ECG data of the subject data into one or more subject cohorts; and
generating the subject EGM data as a function of the one or more subject cohorts.
9. The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to generate a diagnostic output as a function of the subject EGM data.
10. The apparatus of claim 1 , wherein the memory contains instructions configuring the at least a processor to transmit the subject EGM data to a user device to display the subject EGM data to a user.
11. A method for generating pseudo-electrogram (EGM) data from electrocardiogram (ECG) data, the method comprising:
generating, using at least a processor, EGM model training data, wherein generating the EGM model training data comprises:
receiving the EGM model training data, wherein the EGM model training data comprises exemplary ECG data correlated to exemplary EGM data; and
time synchronizing the exemplary ECG data and the exemplary EGM data using a synchronization machine learning model trained using synchronization training data configured to correlate time synchronized ECG data and time synchronized EGM data, wherein the synchronization training data is iteratively updated using a feedback loop;
training, using the at least a processor, an EGM machine-learning model using the EGM model training data;
receiving, using the at least a processor, subject data, wherein the subject data comprises subject ECG data; and
generating, using the at least a processor, subject EGM data as a function of the subject ECG data using the trained EGM machine-learning model.
12. The method of claim 11 , wherein generating the EGM model training data comprises:
determining a first disturbance within the exemplary ECG data and the exemplary EGM data; and
aligning the exemplary ECG data and the exemplary EGM data as a function of the first disturbance.
13. The method of claim 11 , wherein generating the EGM model training data comprises:
updating the EGM model training data as a function of an input and output of the trained EGM machine-learning model; and
iteratively training the EGM machine-learning model as a function of the updated EGM model training data.
14. The method of claim 11 , wherein receiving the subject ECG data comprises receiving the subject ECG data from at least an ECG sensor.
15. The method of claim 14 , wherein the at least an ECG sensor comprises a 12 lead ECG.
16. The method of claim 11 , wherein generating the subject EGM data as a function of the ECG data comprises:
time synchronizing the subject ECG data with the EGM model training data;
splitting the synchronized subject ECG data into a plurality of segments; and
generating the subject EGM data as a function of the plurality of segments of the synchronized subject ECG data.
17. The method of claim 11 , wherein the subject EGM data comprises a continuous distribution of EGM data.
18. The method of claim 11 , wherein generating the subject EGM data as a function of the subject ECG data comprises:
classifying the subject ECG data of the subject data into one or more subject cohorts; and
generating the subject EGM data as a function of the one or more subject cohorts.
19. The method of claim 11 , further comprising:
generating, using the at least a processor, a diagnostic output as a function of the subject EGM data.
20. The method of claim 11 , further comprising:
transmitting, using the at least a processor, the subject EGM data to a user device to display the subject EGM data to a user.