Model Card for ECGDenoiser
Collection: NeuralLib: Deep Learning Models for Biosignals Processing
Description: GRU-based model for ECG noise removal. Model and results published in the paper 'Cleaning ECG with Deep Learning: A Denoiser Tested in Industrial Settings'
- Architecture: GRUseq2seq
- Model Name: ECGDenoiser
- Task: ecg denoising: removing MA, BW and EM noise
- Train Dataset: PTB-XL+MIT-BIH-Noise-Stress-Test-Database
Biosignal(s): ECG
Sampling frequency: 360
Benchmark Results
Validation Loss: 0.0000
Training Time: 0.00 seconds
FLOPs per timestep: 0
Number of trainable parameters: 26121
Hyperparameters
| Parameter | Value |
|---|
| bidirectional | True |
| dropout | 0 |
| hid_dim | [64, 1] |
| learning_rate | 0.005 |
| model_name | ECGDenoiser |
| multi_label | False |
| n_features | 1 |
| n_layers | 2 |
| num_classes | NA |
| task | regression |
| fc_out_bool | False |
Example
import NeuralLib.model_hub as mh
model_name = ECGDenoiser()
model = mh.ProductionModel(model_name=model_name)
signal = torch.rand(1, 100, 1) # Example input signal
predictions = model.predict(signal)
print(predictions)
