17772405. MODEL TRAINING METHOD, SIGNAL RECOGNITION METHOD, APPARATUS, COMPUTING AND PROCESSING DEVICE, COMPUTER PROGRAM, AND COMPUTER-READABLE MEDIUM simplified abstract (BOE Technology Group Co., Ltd.)
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 17772405 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.
- 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 using a multi-task learning mechanism.
- The multi-task model includes a target task model and related task models.
- The target task model is trained as a target-abnormality-recognition model.
- The target-abnormality-recognition model is used to recognize target abnormalities in electrocardio signals.
Potential Applications: - Medical diagnostics for identifying abnormal electrocardio signals. - Healthcare monitoring systems for early detection of cardiac abnormalities.
Problems Solved: - Efficient recognition of abnormal electrocardio signals. - Enhanced accuracy in identifying target abnormalities.
Benefits: - Improved diagnostic capabilities. - Early detection of cardiac issues. - Enhanced patient care through timely interventions.
Commercial Applications: Title: "Advanced Electrocardio Signal Recognition Technology for Healthcare Systems" This technology can be used in medical devices, telemedicine platforms, and healthcare analytics software to improve cardiac health monitoring and diagnostics.
Prior Art: Researchers can explore existing patents related to multi-task learning in medical signal processing and abnormality recognition to understand the novelty of this approach.
Frequently Updated Research: Stay updated on advancements in multi-task learning algorithms for medical signal processing and their applications in healthcare technology.
Questions about Electrocardio Signal Recognition: 1. How does multi-task learning improve the accuracy of abnormality recognition in electrocardio signals? 2. What are the potential challenges in implementing this technology in real-time healthcare 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.
