This example shows how to classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using machine learning and signal processing. In particular, the example use diagnostic feature designer to extract time-domain features and later use classification learner app to classify it. For this example, I have downloaded the dataset and structure them into the form that required for our diagnostic feature designer app.
Download the structurd dataset : https://www.dropbox.com/s/ilaofyb6h6m5sr6/ECGTable.mat?dl=0
In MathWorks website, there are other approaches :
1) Classify Time Series Using Wavelet Analysis and Deep Learning
2) Classify ECG Signals Using Long Short-Term Memory Network
Tips how to prepare the data for diagnostic feature designer app
Use diagnostic feature designer app to extract time-domain features.
Use classification learner app to train machine learning model
Product Focus :
Signal Processing Toolbox
Statistics and Machine Learning Toolbox
System Identification Toolbox
Predictive Maintenance Toolbox
Kevin Chng (2021). Classify ECG Data Using MATLAB App (No Coding) (https://www.mathworks.com/matlabcentral/fileexchange/71967-classify-ecg-data-using-matlab-app-no-coding), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform CompatibilityWindows macOS Linux
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