Feature Extraction for the Cardiovascular Disease Diagnosis

• Main Author: Tang, Yu
• Format: Others
• Language: English
• Published: Mittuniversitetet, Avdelningen för informationssystem och -teknologi 2018
• Subjects: ECG; Feature Extraction; CWT; Unsupervised Learning; Clustering; User interface; Feature Visualization; ECG Disease Diagnosis.; Computer Systems; Datorsystem
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-33742

Cardiovascular disease is a serious life-threatening disease. It can occur suddenly and progresses rapidly. Finding the right disease features in the early stage is important to decrease the number of deaths and to make sure that the patient can fully recover. Though there are several methods of examination, describing heart activities in signal form is the most cost-effective way. In this case, ECG is the best choice because it can record heart activity in signal form and it is safer, faster and more convenient than other methods of examination. However, there are still problems involved in the ECG. For example, not all the ECG features are clear and easily understood. In addition, the frequency features are not present in the traditional ECG. To solve these problems, the project uses the optimized CWT algorithm to transform data from the time domain into the time-frequency domain. The result is evaluated by three data mining algorithms with different mechanisms. The evaluation proves that the features in the ECG are successfully extracted and important diagnostic information in the ECG is preserved. A user interface is designed increasing efficiency, which facilitates the implementation.