Systems and methods for deep learning based ecg-signal classifications for canines

A computer system for classifying electrocardiogram signals of a canine, the computer system comprising:

• at least one memory storing instructions;
• and
• at least one processor configured to execute the instructions to perform operations comprising:
• receiving electrocardiogram data of a canine, the electrocardiogram data including at least one electrocardiogram signal;
• segmenting the electrocardiogram data into one or more data subsets;
• pre-processing the one or more data subsets, the pre-processing including excluding the one or more data subsets that include a poor electrocardiogram signal;
• augmenting the one or more data subsets;
• determining, using a trained machine-learning model, one or more signal classifications for the one or more data subsets;
• aggregating the one or more signal classifications to determine a result classification;
• and
• outputting the result classification to an electronic storage device and/or a display.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for classifying electrocardiogram signals of a canine, the operations comprising:

• receiving electrocardiogram data of a canine, the electrocardiogram data including at least one electrocardiogram signal;
• segmenting the electrocardiogram data into one or more data subsets;
• pre-processing the one or more data subsets, the pre-processing including excluding the one or more data subsets that have a poor electrocardiogram signal;
• augmenting the one or more data subsets;
• determining, using a trained machine-learning model, one or more signal classifications for the one or more data subsets;
• aggregating the one or more signal classifications to determine a result classification;
• and
• outputting the result classification to an electronic storage device and/or a display.