Systems and methods for reduced lead electrocardiogram diagnosis using deep neural networks and rule-based systems

A method for training a deep neural network to automatically diagnose reduced lead electrocardiograms (ECGs), the method comprising:

• selecting an ECG training data pair comprising a lead signal set and a ground truth diagnosis corresponding to the lead signal set;
• selectively removing one or more lead signals from the lead signal set to produce a reduced lead signal set;
• feeding the reduced lead signal set to the deep neural network, the deep neural being network configured to output a diagnosis based on the reduced lead signal set to a diagnosis;
• calculating a difference between the diagnosis and the ground truth diagnosis;
• and
• adjusting one or more parameters of the deep neural network based on the calculated difference.

An electrocardiogram (ECG) processing system comprising:

• a display device;
• a memory storing a deep neural network library comprising a plurality of trained deep neural networks, and instructions;
• and
• a processor communicably coupled to the memory and the display device and when executing the instructions, configured to:
• acquire reduced lead ECG data, wherein the reduced lead ECG data comprises less than twelve lead signals;
• select a deep neural network from the deep neural network library based on the less than twelve lead signals;
• map the less than twelve lead signals to a diagnosis using the deep neural network;
• and
• display the diagnosis via the display device.