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,