Methods and systems for analyzing ECG signals using neural networks

A method for classifying cardiac rhythms in electrocardiogram (ECG) data, the method comprising:

• measuring a candidate ECG signal from a patient using a Holter monitor, wherein the Holter monitor is communicatively connected to one or more processors;
• and
• using the one or more processors to perform:
• receiving a dataset comprising ECG signals and corresponding labels, each of the ECG signals representing a respective sequence of beats, and the corresponding labels indicating cardiac rhythm types for the beats, the cardiac rhythm types including normal sinus rhythm, atrial fibrillation, and atrial flutter;
• generating quantized vectors representing the ECG signals by applying a trained neural network encoder and a residual vector quantizer (RVQ) to the ECG signals, the residual vector quantizer trained to compress and encode data into a lower-dimensional space;
• positionally encoding the quantized vectors to obtain positionally-encoded quantized vectors;
• training a rhythm classifier to classify rhythms of ECG signal beats using the positionally-encoded quantized vectors, the training comprising:
• processing the positionally-encoded quantized vectors using a multi-head self-attention mechanism to generate transformed representations of the positionally-encoded quantized vectors, the processing comprising:
• computing, for each particular ECG signal of the ECG signals, attention scores between each beat and other beats of the respective sequence of beats represented by the particular ECG signal, wherein the multi-head self-attention mechanism utilizes Query, Key, and Value vectors to capture temporal dependencies among individual beats;
• passing the transformed representations through a feed-forward neural network to obtain, for each particular ECG signal of the ECG signals, respective cardiac rhythm classifications for the respective sequence of beats represented by the particular ECG signal, each of the cardiac rhythm classifications indicating a respective one of the cardiac rhythm types;
• and
• updating parameters of the rhythm classifier based on a comparison between the cardiac rhythm classifications and the labels in the dataset;
• classifying cardiac rhythms of beats in the candidate ECG signal, measured for the patient using the Holter monitor, using the trained rhythm classifier, the classifying comprising:
• receiving the candidate ECG signal measured for the patient using the Holter monitor, the candidate ECG signal comprising beats representing respective heartbeats of the patient;
• generating candidate quantized vectors for the candidate ECG signal by applying the trained neural network encoder and the residual vector quantizer to the candidate ECG signal;
• positionally encoding the candidate quantized vectors to obtain positionally-encoded candidate quantized vectors;
• and
• classifying the rhythms of the beats in the candidate ECG signal into respective cardiac rhythm types selected from among the cardiac rhythm types, the classifying comprising:
• generating transformed representations of the positionally-encoded candidate quantized vectors by processing the positionally-encoded candidate quantized vectors using the multi-head self-attention mechanism;
• and
• passing the transformed representations through the feed-forward neural network to obtain cardiac rhythm classifications of the beats in the candidate ECG signal;
• and
• generating an output indicating the cardiac rhythm classifications of the beats in the candidate ECG signal measured for the patient using the Holter monitor.