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Project Proposal

Project # 0 Group members:


Laura, Chen

Title: Improving ECG Classification Using Generative Adversarial Networks

Description: Electrocardiography (ECG) is a non-invasive tool used for diagnosis and followup of cardiac anomalies, functional dis- orders and cardiac arrhythmias. In this paper, the author developed classification and diagnosis of ECG data using long short-term memory algorithm. In addition, the author also used Generative Adversarial Network to generate Electrocardiography (ECG) synthetic data. The studied data contains 48 half-hour ECG record obtained from patients studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Each record contains two 30-minutes ECG lead signals digitized at 360 samples per second. Annotations for both heartbeat class and timing information are included in the dataset.

The LSTM-based Heartbeat Classification considered the temporal nature of ECG signals while avoiding strong model assumptions required by preprocessing and feature engineering approach. When generating ECG heartbeat signals using GANs, the author included generator network and discriminator network to train the model.

The results of the LSTM network classification result for all 4 heartbeat classes outperforms the fully connected network and achieved accuracy all above 80%. For the generated ECG signal, all type of heartbeats the GAN learns to generate synthetic heartbeats which have which have the same PQRST morphology as their corresponding real heartbeats from the training set.

Paper presentation

Date Name Paper number Title Link to the paper Link to the summary Link to the video
Sep 15 (example) Ri Wang Sequence to sequence learning with neural networks. Paper Summary [1]
Week of Nov 16 Ali Ghodsi
Week of Nov 16
Week of Nov 16
Week of Nov 16