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machine-learning-project's Introduction

It's final project of EE271(Artificial Intelligence and Machine Learning) of SUSTech.

Final Project Description:

This dataset comprises a total of 8528 recordings with 188 features (the first 188 columns of data.csv) extracted from single ECG signals. There are supposed to be 4 categories labeled 1 through 4 (shown in the column of data.csv), corresponding to “Normal”, “Atrial Fibrillation (AF)”, “Non-AF related abnormal heart rhythms”, and noisy recording”. The distribution of normal, AF, other rhythms and noisy data is largely imbalanced in the dataset. 

(1) Try to use a fully connected feedforward deep network, a CNN (could be any modern CNN network), a RNN (could be any RNN such as Pyramid RNN, LSTM, GRU, Grid LSTM), and an attention network to solve the above 4-class classification problem. 

(2) Consider the following performance metrics: F1-score for normal (F_norm), AF (F_af),  other rhythms (F_oth), and the final accuracy F_T=□(1/3) 〖(F〗_norm+F_af+F_oth). Compare and analyze the results obtained from the four approaches in (1). It is suggested a 5-fold cross validation is considered to observe the performance. 

For more details, please check Requirement.docx. The implements of different network are written in src folder. The comparision and results of different networks is reported in Final_Report.pdf.

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