US 12,465,266 B1
Methods and systems for analyzing ECG signals using neural networks
John Paul Duffy, Toronto (CA); Michael Feist, St. Albert (CA); and Esmatullah Naikyar, Edmonton (CA)
Assigned to NeuralCloud Solutions Inc., Toronto (CA)
Filed by NeuralCloud Solutions Inc., Toronto (CA)
Filed on Mar. 5, 2025, as Appl. No. 19/070,703.
Application 19/070,703 is a division of application No. 18/896,810, filed on Sep. 25, 2024, abandoned.
Claims priority of provisional application 63/684,432, filed on Aug. 18, 2024.
Int. Cl. A61B 5/346 (2021.01); A61B 5/00 (2006.01)
CPC A61B 5/346 (2021.01) [A61B 5/7264 (2013.01)] 16 Claims
OG exemplary drawing
 
1. 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:
using one or more processors 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.