Company Filing History:
Years Active: 2022
Title: Innovations by Jun Lu in Electrocardiography Signal Processing
Introduction
Jun Lu is an accomplished inventor based in Guangzhou, China. He has made significant contributions to the field of electrocardiography through his innovative methods. His work focuses on improving the accuracy of detecting f waves from single-lead electrocardiography signals.
Latest Patents
Jun Lu holds a patent for a method titled "Method for extracting f wave from single-lead electrocardiography signal." This method involves establishing a two-channel temporal convolutional neural network model. The model includes two coding submodules, two mask estimation submodules, an information fusion module, and two decoding submodules. The process consists of constructing a training data set of ECG signals, training the model, and saving the trained model. The two-channel temporal convolution neural network encodes and decodes a QRST complex and the f wave respectively. It uses a mask of information fusion to estimate and construct regular items to restrict the distribution difference of QRST component coding features. This approach reduces the distortion of the QRST complex and selects potential features of a QRST complex and f wave with a high signal-to-noise ratio, thereby improving the detection accuracy of the f wave.
Career Highlights
Jun Lu is affiliated with the Guangdong University of Technology, where he continues to advance research in his field. His innovative methods have the potential to enhance the effectiveness of electrocardiography, which is crucial for diagnosing various heart conditions.
Collaborations
Jun Lu has collaborated with notable colleagues, including Lei Li and Shengli Xie. Their combined expertise contributes to the ongoing research and development in electrocardiography signal processing.
Conclusion
Jun Lu's innovative work in extracting f waves from electrocardiography signals showcases his dedication to advancing medical technology. His contributions are vital for improving diagnostic accuracy in cardiology.
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