Boe technology group co., ltd. (20240188895). MODEL TRAINING METHOD, SIGNAL RECOGNITION METHOD, APPARATUS, COMPUTING AND PROCESSING DEVICE, COMPUTER PROGRAM, AND COMPUTER-READABLE MEDIUM simplified abstract
MODEL TRAINING METHOD, SIGNAL RECOGNITION METHOD, APPARATUS, COMPUTING AND PROCESSING DEVICE, COMPUTER PROGRAM, AND COMPUTER-READABLE MEDIUM
Organization Name
boe technology group co., ltd.
Inventor(s)
Zhenzhong Zhang of Beijing (CN)
MODEL TRAINING METHOD, SIGNAL RECOGNITION METHOD, APPARATUS, COMPUTING AND PROCESSING DEVICE, COMPUTER PROGRAM, AND COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240188895 titled 'MODEL TRAINING METHOD, SIGNAL RECOGNITION METHOD, APPARATUS, COMPUTING AND PROCESSING DEVICE, COMPUTER PROGRAM, AND COMPUTER-READABLE MEDIUM
The patent application describes a method for training a model to recognize abnormal electrocardio signals, involving multi-task learning mechanisms.
- Acquiring a training sample set that includes sample electrocardio signals and abnormal labels.
- Inputting sample electrocardio signals into a multi-task model for training.
- Training the multi-task model based on the abnormal labels, which include target abnormal labels and related abnormal labels.
- The multi-task model consists of a target task model and at least one related task model.
- The target task model is trained as a target-abnormality-recognition model.
- The target-abnormality-recognition model is configured to recognize target abnormalities in the inputted electrocardio signals.
Potential Applications: - Medical diagnostics for identifying abnormal electrocardio signals. - Healthcare monitoring systems for early detection of cardiac abnormalities.
Problems Solved: - Efficient training of models for recognizing abnormal electrocardio signals. - Improved accuracy in identifying target abnormalities in electrocardio signals.
Benefits: - Early detection of cardiac abnormalities leading to timely medical intervention. - Enhanced accuracy in diagnosing heart conditions.
Commercial Applications: Title: "Advanced Electrocardio Signal Recognition Technology for Healthcare Systems" This technology can be utilized in medical devices, telemedicine platforms, and healthcare monitoring systems to improve the detection of cardiac abnormalities, enhancing patient care and outcomes.
Prior Art: Readers can explore existing research in the field of multi-task learning mechanisms for medical signal recognition to understand the advancements made by this patent application.
Frequently Updated Research: Researchers are continually exploring new methods and technologies to enhance the accuracy and efficiency of recognizing abnormal electrocardio signals for improved healthcare outcomes.
Questions about Electrocardio Signal Recognition: 1. How does the multi-task learning mechanism improve the accuracy of recognizing abnormal electrocardio signals? 2. What are the potential implications of this technology in the field of cardiology and healthcare monitoring systems?
Original Abstract Submitted
model training method, signal recognition method, apparatus, computing and processing device, computer program, and computer-readable medium. the model training method comprises: acquiring a training sample set, training sample set includes sample electrocardio-signals and abnormal labels of sample electrocardio-signals, and abnormal labels include a target abnormal labels and at least one related abnormal labels; inputting sample electrocardio-signals into multi-task model, training multi-task model based on a multi-task learning mechanism according to an output of multi-task model and the abnormal labels; multi-task model includes a target task model and at least one related task model, a target output of the target task model is target abnormality labels of inputted sample electrocardio-signals, and a target output of related task model is related abnormal labels of inputted sample electrocardio-signals; determining target task model after trained as target-abnormality-recognition model, and target-abnormality-recognition model is configured for recognizing target abnormality in the electrocardio-signals inputted into target-abnormality-recognition model.
