"远程动态心电信号异常检测研究及深度学习技术应用"

The master's thesis "Research on Remote Dynamic Electrocardiogram Signal Abnormality Detection" by Tian Xiaobing explores the use of deep learning technology in wearable intelligent ECG monitoring to detect anomalies in heart signals. Traditional methods of analyzing ECG signals, such as manual analysis by medical professionals, are time-consuming and labor-intensive. This often leads to abnormalities in the patient's heart going undetected, as they may not be in the onset phase during analysis. The thesis highlights the importance of remote monitoring of ECG signals using wearable devices equipped with deep learning algorithms. These devices can continuously monitor the heart signals of patients, allowing for real-time detection of abnormalities. The use of deep learning technology enables more accurate and efficient detection of abnormal states in the heart, improving patient outcomes. The study conducted by Tian Xiaobing demonstrates the effectiveness of using deep learning algorithms for remote dynamic ECG signal abnormality detection. By leveraging the power of artificial intelligence, the research aims to revolutionize the way we monitor and diagnose heart conditions. The findings of this thesis have the potential to significantly impact the field of cardiology and improve the overall quality of healthcare for patients with heart conditions. Overall, the research presented in the thesis contributes to the advancement of remote healthcare monitoring technologies and highlights the importance of utilizing deep learning algorithms for the detection of abnormal heart signals. By combining the capabilities of wearable ECG devices with cutting-edge AI technology, we can improve the early detection and treatment of heart conditions, ultimately leading to better outcomes for patients.