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DeepPSP / torch_ecg

Deep learning ECG models implemented using PyTorch
MIT License
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deep-learning ecg pytorch

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torch_ecg

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ECG Deep Learning Framework Implemented using PyTorch.

Documentation (under development):

The system design is depicted as follows

Installation

torch_ecg requires Python 3.6+ and is available through pip:

python -m pip install torch-ecg

One can download the development version hosted at GitHub via

git clone https://github.com/DeepPSP/torch_ecg.git
cd torch_ecg
python -m pip install .

or use pip directly via

python -m pip install git+https://github.com/DeepPSP/torch_ecg.git

Main Modules

Augmenters

Click to expand! Augmenters are classes (subclasses of `torch` `Module`) that perform data augmentation in a uniform way and are managed by the [`AugmenterManager`](torch_ecg/augmenters/augmenter_manager.py) (also a subclass of `torch` `Module`). Augmenters and the manager share a common signature of the `formward` method: ```python forward(self, sig:Tensor, label:Optional[Tensor]=None, *extra_tensors:Sequence[Tensor], **kwargs:Any) -> Tuple[Tensor, ...]: ``` The following augmenters are implemented: 1. baseline wander (adding sinusoidal and gaussian noises) 2. cutmix 3. mixup 4. random flip 5. random masking 6. random renormalize 7. stretch-or-compress (scaling) 8. label smooth (not actually for data augmentation, but has simimlar behavior) Usage example (this example uses all augmenters except cutmix, each with default config): ```python import torch from torch_ecg.cfg import CFG from torch_ecg.augmenters import AugmenterManager config = CFG( random=False, fs=500, baseline_wander={}, label_smooth={}, mixup={}, random_flip={}, random_masking={}, random_renormalize={}, stretch_compress={}, ) am = AugmenterManager.from_config(config) sig, label, mask = torch.rand(2,12,5000), torch.rand(2,26), torch.rand(2,5000,1) sig, label, mask = am(sig, label, mask) ``` Augmenters can be stochastic along the batch dimension and (or) the channel dimension (ref. the `get_indices` method of the [`Augmenter`](torch_ecg/augmenters/base.py) base class). :point_right: [Back to TOC](#torch_ecg)

Preprocessors

Click to expand! Also [preprecessors](torch_ecg/_preprocessors) acting on `numpy` `array`s. Similarly, preprocessors are monitored by a manager ```python import torch from torch_ecg.cfg import CFG from torch_ecg._preprocessors import PreprocManager config = CFG( random=False, resample={"fs": 500}, bandpass={}, normalize={}, ) ppm = PreprocManager.from_config(config) sig = torch.rand(12,80000).numpy() sig, fs = ppm(sig, 200) ``` The following preprocessors are implemented 1. baseline removal (detrend) 2. normalize (z-score, min-max, naïve) 3. bandpass 4. resample For more examples, see the [README file](torch_ecg/preprocessors/README.md)) of the `preprecessors` module. :point_right: [Back to TOC](#torch_ecg)

Databases

Click to expand! This module include classes that manipulate the io of the ECG signals and labels in an ECG database, and maintains metadata (statistics, paths, plots, list of records, etc.) of it. This module is migrated and improved from [DeepPSP/database_reader](https://github.com/DeepPSP/database_reader) After migration, all should be tested again, the progression: | Database | Source | Tested | | ------------- | ---------------------------------------------------------------- | ------------------ | | AFDB | [PhysioNet](https://physionet.org/content/afdb/1.0.0/) | :heavy_check_mark: | | ApneaECG | [PhysioNet](https://physionet.org/content/apnea-ecg/1.0.0/) | :x: | | CinC2017 | [PhysioNet](https://physionet.org/content/challenge-2017/1.0.0/) | :x: | | CinC2018 | [PhysioNet](https://physionet.org/content/challenge-2018/1.0.0/) | :x: | | CinC2020 | [PhysioNet](https://physionet.org/content/challenge-2020/1.0.1/) | :heavy_check_mark: | | CinC2021 | [PhysioNet](https://physionet.org/content/challenge-2021/1.0.2/) | :heavy_check_mark: | | LTAFDB | [PhysioNet](https://physionet.org/content/ltafdb/1.0.0/) | :x: | | LUDB | [PhysioNet](https://physionet.org/content/ludb/1.0.1/) | :heavy_check_mark: | | MITDB | [PhysioNet](https://physionet.org/content/mitdb/1.0.0/) | :heavy_check_mark: | | SHHS | [NSRR](https://sleepdata.org/datasets/shhs) | :x: | | CPSC2018 | [CPSC](http://2018.icbeb.org/Challenge.html) | :heavy_check_mark: | | CPSC2019 | [CPSC](http://2019.icbeb.org/Challenge.html) | :heavy_check_mark: | | CPSC2020 | [CPSC](http://2020.icbeb.org/CSPC2020) | :heavy_check_mark: | | CPSC2021 | [CPSC](http://2021.icbeb.org/CPSC2021) | :heavy_check_mark: | | SPH | [Figshare](https://doi.org/10.6084/m9.figshare.c.5779802.v1) | :heavy_check_mark: | NOTE that these classes should not be confused with a `torch` `Dataset`, which is strongly related to the task (or the model). However, one can build `Dataset`s based on these classes, for example the [`Dataset`](benchmarks/train_hybrid_cpsc2021/dataset.py) for the The 4th China Physiological Signal Challenge 2021 (CPSC2021). One can use the built-in `Dataset`s in [`torch_ecg.databases.datasets`](torch_ecg/databases/datasets) as follows ```python from torch_ecg.databases.datasets.cinc2021 import CINC2021Dataset, CINC2021TrainCfg config = deepcopy(CINC2021TrainCfg) config.db_dir = "some/path/to/db" dataset = CINC2021Dataset(config, training=True, lazy=False) ``` :point_right: [Back to TOC](#torch_ecg)

Implemented Neural Network Architectures

Click to expand! 1. CRNN, both for classification and sequence tagging (segmentation) 2. U-Net 3. RR-LSTM A typical signature of the instantiation (`__init__`) function of a model is as follows ```python __init__(self, classes:Sequence[str], n_leads:int, config:Optional[CFG]=None, **kwargs:Any) -> None ``` if a `config` is not specified, then the default config will be used (stored in the [`model_configs`](torch_ecg/model_configs) module). #### Quick Example A quick example is as follows: ```python import torch from torch_ecg.utils.utils_nn import adjust_cnn_filter_lengths from torch_ecg.model_configs import ECG_CRNN_CONFIG from torch_ecg.models.ecg_crnn import ECG_CRNN config = adjust_cnn_filter_lengths(ECG_CRNN_CONFIG, fs=400) # change the default CNN backbone # bottleneck with global context attention variant of Nature Communications ResNet config.cnn.name="resnet_nature_comm_bottle_neck_gc" classes = ["NSR", "AF", "PVC", "SPB"] n_leads = 12 model = ECG_CRNN(classes, n_leads, config) model(torch.rand(2, 12, 4000)) # signal length 4000, batch size 2 ``` Then a model for the classification of 4 classes, namely "NSR", "AF", "PVC", "SPB", on 12-lead ECGs is created. One can check the size of a model, in terms of the number of parameters via ```python model.module_size ``` or in terms of memory consumption via ```python model.module_size_ ``` #### Custom Model One can adjust the configs to create a custom model. For example, the building blocks of the 4 stages of a `TResNet` backbone are `basic`, `basic`, `bottleneck`, `bottleneck`. If one wants to change the second block to be a `bottleneck` block with sequeeze and excitation (`SE`) attention, then ```python from copy import deepcopy from torch_ecg.models.ecg_crnn import ECG_CRNN from torch_ecg.model_configs import ( ECG_CRNN_CONFIG, tresnetF, resnet_bottle_neck_se, ) my_resnet = deepcopy(tresnetP) my_resnet.building_block[1] = "bottleneck" my_resnet.block[1] = resnet_bottle_neck_se ``` The convolutions in a `TResNet` are anti-aliasing convolutions, if one wants further to change the convolutions to normal convolutions, then ```python for b in my_resnet.block: b.conv_type = None ``` or change them to separable convolutions via ```python for b in my_resnet.block: b.conv_type = "separable" ``` Finally, replace the default CNN backbone via ```python my_model_config = deepcopy(ECG_CRNN_CONFIG) my_model_config.cnn.name = "my_resnet" my_model_config.cnn.my_resnet = my_resnet model = ECG_CRNN(["NSR", "AF", "PVC", "SPB"], 12, my_model_config) ``` :point_right: [Back to TOC](#torch_ecg)

CNN Backbones

Click to expand! #### Implemented 1. VGG 2. ResNet (including vanilla ResNet, ResNet-B, ResNet-C, ResNet-D, ResNeXT, TResNet, [Stanford ResNet](https://github.com/awni/ecg), [Nature Communications ResNet](https://github.com/antonior92/automatic-ecg-diagnosis), etc.) 3. MultiScopicNet (CPSC2019 SOTA) 4. DenseNet (CPSC2020 SOTA) 5. Xception In general, variants of ResNet are the most commonly used architectures, as can be inferred from [CinC2020](https://cinc.org/archives/2020/) and [CinC2021](https://cinc.org/archives/2021/). #### Ongoing 1. MobileNet 2. DarkNet 3. EfficientNet #### TODO 1. HarDNet 2. HO-ResNet 3. U-Net++ 4. U-Squared Net 5. etc. More details and a list of references can be found in the [README file](torch_ecg/models/cnn/README.md) of this module. :point_right: [Back to TOC](#torch_ecg)

Components

Click to expand! This module consists of frequently used components such as loggers, trainers, etc. #### [Loggers](torch_ecg/components/loggers.py) Loggers including 1. CSV logger 2. text logger 3. tensorboard logger are implemented and manipulated uniformly by a manager. #### [Outputs](torch_ecg/components/outputs.py) The `Output` classes implemented in this module serve as containers for ECG downstream task model outputs, including - `ClassificationOutput` - `MultiLabelClassificationOutput` - `SequenceTaggingOutput` - `WaveDelineationOutput` - `RPeaksDetectionOutput` each having some required fields (keys), and is able to hold an arbitrary number of custom fields. These classes are useful for the computation of metrics. #### [Metrics](torch_ecg/components/metrics.py) This module has the following pre-defined (built-in) `Metrics` classes: - `ClassificationMetrics` - `RPeaksDetectionMetrics` - `WaveDelineationMetrics` These metrics are computed according to either [Wikipedia](https://en.wikipedia.org/wiki/Precision_and_recall), or some published literatures. #### [Trainer](torch_ecg/components/trainer.py) An abstract base class `BaseTrainer` is implemented, in which some common steps in building a training pipeline (workflow) are impemented. A few task specific methods are assigned as `abstractmethod`s, for example the method ```python evaluate(self, data_loader:DataLoader) -> Dict[str, float] ``` for evaluation on the validation set during training and perhaps further for model selection and early stopping. :point_right: [Back to TOC](#torch_ecg)

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Other Useful Tools

Click to expand! ### [R peaks detection algorithms](torch_ecg/utils/rpeaks.py) This is a collection of traditional (non deep learning) algorithms for R peaks detection collected from [WFDB](https://github.com/MIT-LCP/wfdb-python) and [BioSPPy](https://github.com/PIA-Group/BioSPPy). :point_right: [Back to TOC](#torch_ecg)

Usage Examples

Click to expand! See case studies in the [benchmarks folder](benchmarks/). a large part of the case studies are migrated from other DeepPSP repositories, some are implemented in the old fasion, being inconsistent with the new system architecture of `torch_ecg`, hence need updating and testing | Benchmark | Architecture | Source | Finished | Updated | Tested | | ---------------------------------------------- | ------------------------- | ------------------------------------------------------- | ------------------ | ------------------ | ------------------ | | [CinC2020](benchmarks/train_crnn_cinc2020/) | CRNN | [DeepPSP/cinc2020](https://github.com/DeepPSP/cinc2020) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | [CinC2021](benchmarks/train_crnn_cinc2021/) | CRNN | [DeepPSP/cinc2021](https://github.com/DeepPSP/cinc2021) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | [CinC2022](benchmarks/train_mtl_cinc2022/)[^1] | Multi Task Learning (MTL) | [DeepPSP/cinc2022](https://github.com/DeepPSP/cinc2022) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | [CPSC2019](benchmarks/train_multi_cpsc2019/) | SequenceTagging/U-Net | NA | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | [CPSC2020](benchmarks/train_hybrid_cpsc2020/) | CRNN/SequenceTagging | [DeepPSP/cpsc2020](https://github.com/DeepPSP/cpsc2020) | :heavy_check_mark: | :x: | :x: | | [CPSC2021](benchmarks/train_hybrid_cpsc2021/) | CRNN/SequenceTagging/LSTM | [DeepPSP/cpsc2021](https://github.com/DeepPSP/cpsc2021) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | [LUDB](benchmarks/train_unet_ludb/) | U-Net | NA | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | [^1]: Although `CinC2022` dealt with acoustic cardiac signals (phonocardiogram, PCG), the tasks and signals can be treated similarly. Taking [CPSC2021](benchmarks/train_hybrid_cpsc2021) for example, the steps are 1. Write a [`Dataset`](benchmarks/train_hybrid_cpsc2021/dataset.py) to fit the training data for the model(s) and the training workflow. Or directly use the built-in `Dataset`s in [`torch_ecg.databases.datasets`](torch_ecg/databases/datasets). In this example, 3 tasks are considered, 2 of which use a [`MaskedBCEWithLogitsLoss`](torch_ecg/models/loss.py) function, hence the `Dataset` produces an extra tensor for these 2 tasks ```python def __getitem__(self, index:int) -> Tuple[np.ndarray, ...]: if self.lazy: if self.task in ["qrs_detection"]: return self.fdr[index][:2] else: return self.fdr[index] else: if self.task in ["qrs_detection"]: return self._all_data[index], self._all_labels[index] else: return self._all_data[index], self._all_labels[index], self._all_masks[index] ``` 2. Inherit a [base model](torch_ecg/models/ecg_seq_lab_net.py) to create [task specific models](benchmarks/train_hybrid_cpsc2021/model.py), along with [tailored model configs](benchmarks/train_hybrid_cpsc2021/cfg.py) 3. Inherit the [`BaseTrainer`](torch_ecg/components/trainer.py) to build the [training pipeline](benchmarks/train_hybrid_cpsc2021/trainer.py), with the `abstractmethod`s (`_setup_dataloaders`, `run_one_step`, `evaluate`, `batch_dim`, etc.) implemented. :point_right: [Back to TOC](#torch_ecg)

CAUTION

For the most of the time, but not always, after updates, I will run the notebooks in the benchmarks manually. If someone finds some bug, please raise an issue. The test workflow is to be enhanced and automated, see this project.

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Work in progress

See the projects page.

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Citation

@misc{torch_ecg,
      title = {{torch\_ecg: An ECG Deep Learning Framework Implemented using PyTorch}},
     author = {WEN, Hao and KANG, Jingsu},
        doi = {10.5281/ZENODO.6435048},
        url = {https://zenodo.org/record/6435048},
  publisher = {Zenodo},
       year = {2022},
  copyright = {{MIT License}}
}
@article{torch_ecg_paper,
      title = {{A Novel Deep Learning Package for Electrocardiography Research}},
     author = {Hao Wen and Jingsu Kang},
    journal = {{Physiological Measurement}},
        doi = {10.1088/1361-6579/ac9451},
       year = {2022},
      month = {11},
  publisher = {{IOP Publishing}},
     volume = {43},
     number = {11},
      pages = {115006}
}

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Thanks

Much is learned, especially the modular design, from the adversarial NLP library TextAttack and from Hugging Face transformers.

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