Model Card for ECGPeakDetector

Collection: NeuralLib: Deep Learning Models for Biosignals Processing

Description: GRU-based model for ECG peak detection.

• Architecture: GRUseq2seq
• Model Name: ECGPeakDetector
• Task: peak detection
• Train Dataset: private_gib01

Biosignal(s): ECG

Sampling frequency: 360

Benchmark Results

Validation Loss: 0.1488

Training Time: 11375.49 seconds

FLOPs per timestep: 0

Number of trainable parameters: 0

Hyperparameters

Parameter	Value
bidirectional	True
dropout	0
hid_dim	[32, 64, 64]
learning_rate	0.001
model_name	ECGPeakDetector
multi_label	True
n_features	1
n_layers	3
num_classes	1
task	classification

Example

import NeuralLib.model_hub as mh

model_name = ECGPeakDetector()

model = mh.ProductionModel(model_name=model_name)

signal = torch.rand(1, 100, 1) # Example input signal

predictions = model.predict(signal)

print(predictions)