torch_ecg.models

This module contains the model architectures for ECG various tasks.

Convolutional neural backbones

ResNet(in_channels, **config)	ResNet model.
RegNet(in_channels, **config)	RegNet model.
VGG16(in_channels, **config)	CNN feature extractor of VGG architecture.
Xception(in_channels, **config)	Xception model.
DenseNet(in_channels, **config)	The core part of the SOTA model (framework) of CPSC2020.
MobileNetV1(in_channels, **config)	MobileNet V1.
MobileNetV2(in_channels, **config)	MobileNet V2.
MobileNetV3(in_channels, **config)	MobileNet V3.
MultiScopicCNN(in_channels, **config)	CNN part of the SOTA model from CPSC2019 challenge (entry 0416).

Downstream task models

ECG_UNET(classes, n_leads[, config])	U-Net for (multi-lead) ECG wave delineation.
ECG_SUBTRACT_UNET(classes, n_leads[, config])	U-Net for ECG wave delineation.
ECG_CRNN(classes, n_leads[, config])	Convolutional (Recurrent) Neural Network for ECG tasks.
ECG_SEQ_LAB_NET(classes, n_leads[, config])	SOTA model from CPSC2019 challenge.
RR_LSTM(classes[, config])	LSTM model for RR time series classification or sequence labeling.

Saliency analysis

GradCam(model, feature_module, ...[, ...])

NOT finished,