Methods and systems for analyzing ECG signals using neural networks

A system for identifying wave properties of beats in an electrocardiogram (ECG) signal measured for a patient, the beats in the ECG signal corresponding to heartbeats of the patient, the system comprising:

• one or more computer hardware processors configured to perform:
• training a machine learning (ML) model comprising:
• (1) an encoder neural network comprising multiple convolutional layers and non-linear activations, (2) a residual vector quantizer (RVQ) configured to map latent space representations to codebook entries in quantized space, and (3) a beat classifier to obtain a trained encoder neural network, a trained RVQ, and a trained beat classifier, wherein training the ML model is performed using training data comprising ECG signals and the training comprises, iteratively:
• processing one or more of the ECG signals using the encoder neural network, the RVQ, and a decoder neural network to obtain one or more generated ECG signals;
• classifying the one or more generated ECG signals as real ECG signals or fake ECG signals using a discriminator neural network model;
• and
• updating weights of the encoder neural network, the RVQ, and/or the decoder neural network based on results of the classifying;
• receiving the ECG signal measured for the patient;
• processing the received ECG signal using the trained encoder neural network to obtain a latent space representation of the ECG signal;
• generating quantized vectors by applying the trained RVQ to the latent space representation of the ECG signal;
• identifying wave properties of beats in the ECG signal by using the trained beat classifier to process the quantized vectors generated by the RVQ, the wave properties including one or more of P wave:
• onset, peak, and offset, Q wave:
• peak, R wave:
• peak, S wave:
• peak, T wave:
• onset, peak, and offset, U wave:
• peak and offset, and QRS complex:
• onset and offset;
• determining one or more conditions for beats in the ECG signal based on the wave properties identified for the beats in the ECG signal, the one or more conditions selected from:
• normal beats, premature atrial contractions (PACs), premature ventricular contractions (PVCs), bradycardia, tachycardia, pauses, atrioventricular (AV) block, long QT syndrome, and atrial fibrillation;
• based on the determined one or more conditions, determining a treatment recommendation;
• and
• initiating treatment by providing the treatment recommendation to a healthcare provider in furtherance of administering the recommended treatment to the patient.

At least one non-transitory computer-readable storage medium storing software that, when executed by one or more hardware processors, cause the one or more hardware processors to perform a method for identifying wave properties of beats in an electrocardiogram (ECG) signal measured for a patient, the beats in the ECG signal corresponding to heartbeats of the patient, the method comprising:

• training a machine learning (ML) model comprising (1) an encoder neural network comprising multiple convolutional layers and non-linear activations, (2) a residual vector quantizer (RVQ) configured to map latent space representations to codebook entries in quantized space and (3) a beat classifier to obtain a trained encoder neural network, a trained RVQ and a trained beat classifier, wherein training the ML model is performed using training data comprising ECG signals and the training comprises, iteratively:
• processing one or more of the ECG signals using the encoder neural network, the RVQ, and a decoder neural network to obtain one or more generated ECG signals;
• classifying the one or more generated ECG signals as real ECG signals or fake ECG signals using a discriminator neural network model;
• and
• updating weights of the encoder neural network, the RVQ, and/or the decoder neural network based on results of the classifying;
• receiving the ECG signal measured for the patient;
• processing the received ECG signal using the trained encoder neural network to obtain a latent space representation of the ECG signal;
• generating quantized vectors by applying the trained RVQ to the latent space representation of the ECG signal;
• identifying wave properties of beats in the ECG signal by using the trained beat classifier to process the quantized vectors generated by the RVQ, the wave properties including one or more of P wave:
• onset, peak, and offset, Q wave:
• peak, R wave:
• peak, S wave:
• peak, T wave:
• onset, peak, and offset, U wave:
• peak and offset, and QRS complex:
• onset and offset;
• determining one or more conditions for beats in the ECG signal based on the wave properties identified for the beats in the ECG signal, the one or more conditions selected from:
• normal beats, premature atrial contractions (PACs), premature ventricular contractions (PVCs), bradycardia, tachycardia, pauses, atrioventricular (AV) block, long QT syndrome, and atrial fibrillation;
• based on the determined one or more conditions,
• determining a treatment recommendation;
• and
• initiating treatment by providing the treatment recommendation to a healthcare provider in
• furtherance of administering the recommended treatment to the patient.