Introduction
•Cardiovascular disease (CVD) is the single leading cause of global mortality and is projected to remain so.
•Cardiac arrhythmia, a very common CVD, is irregular rhythmic beating of the heart; it may indicate an increased risk of stroke or sudden cardiac death.
•The Electrocardiogram (ECG) is the most widely adopted clinical tool to diagnose and assess the risk of arrhythmia.
•During normal patient hospital visits, however, arrhythmias may not be detected on normal resting ECG machines since the condition may not be present at that moment in time.
•We seek to unite the portability of Holter monitors and the processing power of state-of-the-art resting ECG machines to provide an assistive diagnosis solution using cell phones.
Functions
•Collect ECG data from bluetooth and show it on the screen.
•Use DSP to analyze ECG signal and extract import features.
•Record patients' ECG record and do ANN training
•Utilize artificial neural networks to detect ECG deceases.
My responsibility
•Partly GUI design in C#.
•Research and design the algorithms to extract ECG features in Matlab.
•Design the plugin to implement feature extraction and ANN in C++.
Acknowledgement
•This research is currently being supported by the Cell Phone as a Platform for Healthcare award from Microsoft Research.
