Apparatus and method for validating labeled data generated by a prediction generator
An apparatus and method for validating, using user input, labeled data generated by a prediction generator. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The processor receives at least a potential signal, generates, processed data using the at least a potential signal, trains a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises the processed data associated with at least an annotation, generates, using the prediction generator, a plurality of labeled data as a function of dynamic data, displays the plurality of labeled data to a user interface of a graphical user interface, receives a user input associated with the plurality of labeled data, and retrains the prediction generator using the user input.
1. An apparatus for validating, using user input, labeled data generated by a prediction generator, wherein the apparatus comprises:
at least a catheter configured for intracardiac use, the at least a catheter comprising at least a transducer configured to detect a cardiac phenomenon and output at least a potential signal, as a function of the cardiac phenomenon;
at least a localization system configured to detect at least a position signal as a function of a location of the at least a catheter; and
at least a computing device, wherein the computing device comprises:
a memory; and
at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to:
receive the at least a potential signal, wherein the at least a potential signal corresponds to temporal data;
generate, using the at least a processor, processed data using the at least a potential signal;
train a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises archive data associated with at least an annotation;
generate, using the prediction generator, a plurality of labeled data as a function of the processed data;
display, using a downstream device, the plurality of labeled data to a user interface of a graphical user interface;
receive a user input associated with the plurality of labeled data, wherein the user input comprises one or more of a correction datum and a confirmation datum and wherein the correction datum comprises one or more of a signal segment correction and a segment annotation correction;
retrain the prediction generator using the user input.
2. The apparatus of claim 1 , wherein the at least a potential signal comprises electrocardiogram data.
3. The apparatus of claim 1 , wherein generating the processed data comprises:
normalizing the potential signal into a consistent data format;
generating a combined potential signal by combining a first potential signal with a second potential signal of the at least a potential signal, wherein the first potential signal is a duplicate of the second potential signal; and
generating a scaled potential signal of the at least a potential signal.
4. The apparatus of claim 1 , wherein the prediction generator comprises a semi-supervised machine learning model, wherein the semi-supervised machine learning model comprises a neural network.
5. The apparatus of claim 1 , wherein the signal segment correction is configured to correct the signal segment generated by the prediction generator.
6. The apparatus of claim 1 , wherein the segment annotation correction is configured to correct the segment annotation generated by the prediction generator.
7. The apparatus of claim 1 , wherein the signal segment correction and the segment annotation correction are stored in a database.
8. The apparatus of claim 1 , wherein the plurality of labeled training data comprises at least a signal segment associated with at least a segment annotation.
9. A method for validating, using user input, labeled data generated by a prediction generator, wherein the method comprises:
using, at least a catheter configured for intracardiac use, the at least a catheter comprising at least a transducer configured to detect a cardiac phenomenon and output at least a potential signal, as a function of the cardiac phenomenon and at least a localization system configured to detect at least a position signal as a function of a location of the at least a catheter;
receiving, using the at least a processor, at least a potential signal, wherein the at least a potential signal corresponds to temporal data;
generating, using the at least a processor, processed data using the at least a potential signal;
training a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises the processed data associated with at least an annotation;
generating, using the prediction generator, a plurality of labeled data as a function of dynamic data;
displaying, using a downstream device, the plurality of labeled data to a user interface of a graphical user interface;
receiving, using the at least a processor, a user input associated with the plurality of labeled data, wherein the user input comprises one or more of a correction datum and a confirmation datum and wherein the correction datum comprises one or more of a signal segment correction and a segment annotation correction;
retraining, using the at least a processor, the prediction generator using the user input.
10. The method of claim 9 , wherein the at least a potential signal comprises electrocardiogram data.
11. The method of claim 9 , wherein generating the processed data comprises:
normalizing the potential signal into a consistent data format;
generating a combined potential signal by combining a first potential signal with a second potential signal of the at least a potential signal, wherein the first potential signal is a duplicate of the second potential signal; and
generating a scaled potential signal of the at least a potential signal.
12. The method of claim 9 , wherein the prediction generator comprises a semi-supervised machine learning model, wherein the semi-supervised machine learning model comprises a neural network.
13. The method of claim 11 , wherein the signal segment correction is configured to correct the signal segment generated by the prediction generator.
14. The method of claim 11 , wherein the segment annotation correction is configured to correct the segment annotation generated by the prediction generator.
15. The method of claim 9 , wherein the signal segment correction and the segment annotation correction are stored in a database.
16. The method of claim 9 , wherein the plurality of labeled training data comprises at least a signal segment associated with at least a segment annotation.