In this task, you need to train LSTM-based models to solve a time-series data classification problem. First, we will introduce the datasets. Then, we will provide the detailed requirements.
The dataset is the Electrocardiogram (ECG) Heartbeat Categorization Dataset, which contains heartbeat signals for exploring the categorization of heartbeats. The ECG dataset is composed of two benchmark datasets for heartbeat classification, i.e., the MIT-BIH Arrhythmia dataset and the PTB Diagnostic ECG dataset. MIT-BIH dataset contains 109446 samples and 5 categories, and PTB dataset contains 14552 samples and 2 categories. Each sample is an ECG whose size is [1, 187]. You can use the “utils.Vis_ECG” function to visualize the ECG data. The dataset can be downloaded via this link.
Design your own LSTM-based model. The LSTM-based model must contain an LSTM module. Hint: you can use the function nn.LSTM().
You need to train LSTM-based models on the full training set, and try to obtain at least 97.5% accuracy on the MIT-BIH dataset and 96% accuracy on the PTB dataset. Report the best test accuracy evaluated on the test set of both MIT-BIH and PTB datasets, and illustrate your methods to improve the performance in the report.
In this task, you need to train Transformer-based models for the heartbeat classification.
The Transformer-based model must contain a Transformer-based encoder module. Hint: you can use the PyTorch package to realize the Transformer-based encoder.
You need to train Transformer-based models on the full training set, and try to obtain at least 97.5% accuracy on the MIT-BIH dataset and 96.5% accuracy on the PTB dataset. Report the best test accuracy evaluated on the test set of both MIT-BIH and PTB datasets, and illustrate your methods to improve the performance in the report.
Report the best test accuracy of both Transformer-based models and LSTM-based models trained on MIT-BIH training sets with different sizes. You need to report the best test accuracy evaluated on the full MIT-BIH testing sets w.r.t. different subset sizes, and provide your observations as well as analyses in the report. Note that the variable “subset_percent” in the code means that the current training set is the #subset_percent fraction of the full training set
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