Model Zoo¶

DeepECG-Kit includes 21 model architectures for ECG signal classification. All models follow a consistent interface:

model = ModelClass(input_channels=1, output_size=4)

All Models¶

Registry Name	Class	Type	Description
`afmodel`	`AFModel`	CNN	Atrial Fibrillation model optimized for 30s ECG segments
`kanres`	`KanResWideX`	CNN+Residual	KAN-ResNet architecture with wide layers
`kanres-deep`	`KanResDeepX`	CNN+Residual	Deep KAN-ResNet architecture
`resnet`	`ResNet1D`	Residual CNN	1D ResNet adapted for ECG
`resnet-wang`	`ResNetWang`	Residual CNN	ResNet (Wang et al.) for time series
`se-resnet`	`SEResNet1D`	Residual+Attention	ResNet with Squeeze-and-Excitation
`xresnet`	`XResNet1D`	Residual CNN	XResNet with Mish activation and blur-pool
`xresnet1d-benchmark`	`XResNet1dBenchmark`	Residual CNN	XResNet from PTB-XL benchmark
`inception-time`	`InceptionTime1D`	Multi-scale CNN	Multi-scale temporal InceptionTime
`convnext-v2`	`ConvNeXtV21D`	Modern CNN	ConvNeXt V2 with depthwise conv and GRN
`deep-res-cnn`	`DeepResCNN`	2D Residual CNN	Deep Residual 2D CNN for multi-lead ECG
`fcn-wang`	`FCNWang`	FCN	Fully Convolutional Network (Wang et al.)
`simple-cnn`	`SimpleCNN`	Lightweight CNN	Lightweight CNN for fast inference
`crnn`	`CRNN`	CNN+LSTM	CNN-LSTM hybrid for temporal aggregation
`gru`	`GRUECG`	Recurrent	GRU-based sequential ECG model
`lstm`	`LSTMECG`	Recurrent	LSTM-based sequential ECG model
`tcn`	`TCN`	Temporal CNN	Temporal Convolutional Network with dilated causal convolutions
`transformer`	`TransformerECG`	Transformer	Transformer-based ECG classifier
`medformer`	`Medformer`	Transformer	Multi-granularity patching Transformer (NeurIPS 2024)
`dualnet`	`ECGDualNet`	Hybrid	Dual-path CNN-LSTM + Transformer
`mamba`	`Mamba1D`	State Space	Bidirectional Mamba with linear complexity

Choosing a Model¶

Fast inference / small datasets:

simple-cnn — Minimal parameters, fast training
kanres — Good accuracy/speed trade-off, pretrained weights available

Long sequences:

mamba — Linear complexity, handles very long sequences efficiently
tcn — Dilated convolutions capture long-range dependencies

Multi-lead ECG (12-lead):

deep-res-cnn — 2D convolutions across leads
xresnet1d-benchmark — Designed for the PTB-XL benchmark

State-of-the-art research:

medformer — Multi-granularity Transformer (NeurIPS 2024)
dualnet — Dual-path architecture combining CNN-LSTM and Transformer

Pretrained Weights¶

Weight Name	Model	Task
`kanres-af-30s`	KanResWideX	AF classification, 30s segments
`afmodel-30s`	AFModel	AF classification, 30s segments

from deepecgkit.utils.weights import load_pretrained_weights

state_dict = load_pretrained_weights("kanres-af-30s")
model.load_state_dict(state_dict, strict=False)

Or via the CLI:

deepecg train -m kanres -d af-classification --weights kanres-af-30s

Using the Registry¶

from deepecgkit.registry import get_model, get_model_names, get_model_info

print(get_model_names())

info = get_model_info("kanres")
print(info["description"])

model_class = get_model("kanres")
model = model_class(input_channels=1, output_size=4)

Model Comparison¶

Use the included benchmark script to compare architectures:

python examples/model_comparison.py --input-channels 1 --output-size 4 --signal-length 3000

This instantiates every model and reports parameter counts, feature dimensions, and inference speed. See the Model Comparison example for details.