Qilu University of Technology (Shandong Academy of Sciences) (20240324936). ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN) simplified abstract
Contents
- 1 ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN)
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN) - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about ECG Signal Enhancement
- 1.11 Original Abstract Submitted
ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN)
Organization Name
Qilu University of Technology (Shandong Academy of Sciences)
Inventor(s)
ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240324936 titled 'ELECTROCARDIOGRAPH (ECG) SIGNAL ENHANCEMENT METHOD BASED ON NOVEL GENERATIVE ADVERSARIAL NETWORK (GAN)
Simplified Explanation
The patent application describes a method for enhancing electrocardiograph (ECG) signals using a novel generative adversarial network (GAN) and a multi-branch structure of bi-directional long short-term memory (BiLSTM) neural networks.
Key Features and Innovation
- Utilizes a generative adversarial network (GAN) to enhance ECG signals.
- Employs a multi-branch structure of bi-directional long short-term memory (BiLSTM) neural networks with varying hidden neurons.
- Introduces an ECG signal enhancement module (EEA-Net) with adaptive convolutional and average pooling layers for improved processing of input sequences.
Potential Applications
The technology can be used in medical settings for improving the accuracy and clarity of ECG signals. It may also find applications in research labs for signal processing and analysis.
Problems Solved
Enhances the capability of a generator model to understand and express ECG data effectively. Addresses the need for flexible processing of input sequences of varying lengths. Improves the capture of important information in ECG signals.
Benefits
Enhanced accuracy and clarity of ECG signals. Improved signal processing capabilities. Better understanding and analysis of ECG data.
Commercial Applications
Title: Enhanced ECG Signal Processing Technology for Medical and Research Applications This technology can be commercialized as a software tool for healthcare providers, medical device companies, and research institutions. It has the potential to improve diagnostic accuracy and treatment outcomes in cardiology and related fields.
Questions about ECG Signal Enhancement
What are the potential implications of this technology in the field of cardiology?
This technology could lead to more accurate diagnoses and treatment plans for patients with heart conditions, ultimately improving patient outcomes.
How does this method compare to existing signal enhancement techniques in terms of efficiency and effectiveness?
This method offers a novel approach to ECG signal enhancement by combining GANs and BiLSTM networks, potentially providing superior results compared to traditional techniques.
Original Abstract Submitted
an electrocardiograph (ecg) signal enhancement method based on a novel generative adversarial network (gan) effectively enhances a capability of a generator model for understanding and expressing input data by using a multi-branch structure of bi-directional long short-term memory (bilstm) neural networks with different quantities of hidden neurons, and stitching outputs of last time steps of forward propagation of the different bilstm networks. a new ecg signal enhancement module eea-net is proposed, which uses an adaptive convolutional layer to dynamically adjust a size of a convolution kernel, making the model more flexible in processing input sequences of different lengths. in addition, the model uses an adaptive average pooling layer to perform weighted average pooling on the input data to better capture important information of the input data.
