Title: Development of AI-based Digital Heart
This project will develop an AI-based digital heart to identify the strengths and weaknesses of the current technology which will be served as a foundation for improvement and development of future AI-enabled ECG in deep-learning convolutional neural networks. The most desirable outcome is a fully automated AI-enabled ECG system that mimics human-like interpretation of ECGs to assist medical practitioners in making greater diagnosis and clinical decisions.
Mobile phone APP development based on Android system.
Here you need to develop an APP for the Android system, the basic function is that you can use the APP to connect with the wearable ECG patch and perform some simple data analysis, etc.
Mobile phone APP development based on iOS system
Similar to the first project, now the platform is changed to iOS, the APP is particular for iPhone, iPad, etc
Computer-based user interface
Develop AI-based computer software which can automatically diagnose heart diseases. The above three subprojects are highly related.
Supervisor: Branka Vucetic and Zihuai Lin. Email: email@example.com; firstname.lastname@example.org
Requirements: up to 6 students are required for this project. The students participating in this project should have good knowledge on smart phone APP development. Programming skills are essential.
1. L. Meng, K. Ge, Y. Song, D. Yang, and Z. Lin, Long-term wearable electrocardiogram signal monitoring and analysis based on convolutional neural network, the IEEE Transactions on Instrumentation & Measurement, Vol. 70, April 2021, DOI:10.1109/TIM.2021.3072144
2. Z. Chen, Z. Lin, P. Wang, and M. Ding, Negative-ResNet: noisy ambulatory electrocardiogram signal classification scheme, Neural Computing and Applications, Vol. 33, Issue 14, July 2021, pp. 8857-8869
3. P, Wang, Z. Lin, Z. Chen, X. Yan, M. Ding, A Wearable ECG Monitor for Deep Learning-Based Real-Time Cardiovascular Disease Detection, https://arxiv.org/abs/2201.10083
4. X. Yan, Z. Lin, P. Wang, Wireless Electrocardiograph Monitoring Based on Wavelet Convolutional Neural Network, Proceedings of the IEEE WCNC 2020.