| CPC A61B 5/361 (2021.01) [A61B 5/355 (2021.01); A61B 5/36 (2021.01); A61B 5/367 (2021.01); A61B 5/7264 (2013.01); G16H 50/20 (2018.01); A61B 5/7267 (2013.01); A61B 18/18 (2013.01)] | 28 Claims |
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15. A method for treating a target patient, the method comprising:
performing by one or more computing system for training of an atrial fibrillation (AF) epoch machine learning (ML) model, the method comprising:
accessing a plurality of training T-Q intervals along with indications of whether the training T-Q intervals represent an AF epoch or not an AF epoch;
for each training T-Q interval, generating training data that includes a feature vector with one or more features derived from that training T-Q interval and a label indicating whether that training T-Q interval represents an AF epoch or not an AF epoch;
training the AF epoch ML model that is a neural network using the training data wherein the AF epoch ML model inputs a feature vector with one or more features derived from a T-Q interval of a patient cardiogram and outputs an indication of whether the T-Q interval represents an AF epoch; and
inputting to the trained AF epoch ML model features derived from a target patient T-Q interval of a target patient cardiogram of the target patient wherein the trained AF epoch ML model outputs an indication of whether the target patient T-Q interval represents an AF each; and
performing an ablation on the target patient based on the target patient T-Q interval representing an AF epoch.
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