{
  "families": [
    {
      "id": "baseline",
      "label": "Baseline"
    },
    {
      "id": "cnn",
      "label": "Convolutional"
    },
    {
      "id": "rnn",
      "label": "Recurrent"
    },
    {
      "id": "hybrid",
      "label": "Hybrid"
    },
    {
      "id": "transformer",
      "label": "Transformer"
    },
    {
      "id": "ssm",
      "label": "State-space"
    }
  ],
  "tasks": [
    {
      "id": "ptbxl-multilabel",
      "dataset": "ptbxl",
      "title": "PTB-XL — diagnostic superclasses",
      "short": "PTB-XL",
      "input_channels": 12,
      "output_size": 5,
      "summary": "Twelve-lead resting ECGs labelled with five diagnostic superclasses (NORM, MI, STTC, CD, HYP). Records carry more than one superclass, so this is a multi-label task and precision, recall and F1 are macro-averaged over labels.",
      "dataset_url": "https://physionet.org/content/ptb-xl/1.0.3/",
      "ecgbench_url": "https://vajira.info/ECGBench/datasets/ptb-xl.html",
      "note": null
    },
    {
      "id": "physionet2017-4class",
      "dataset": "af-classification",
      "title": "PhysioNet 2017 — four-class rhythm",
      "short": "PhysioNet 2017",
      "input_channels": 1,
      "output_size": 4,
      "summary": "Single-lead recordings from the 2017 PhysioNet/CinC Challenge, labelled normal, atrial fibrillation, other rhythm, or too noisy to classify.",
      "dataset_url": "https://physionet.org/content/challenge-2017/1.0.0/",
      "ecgbench_url": null,
      "note": null
    },
    {
      "id": "mitbih-afdb-binary",
      "dataset": "mitbih-afdb",
      "title": "MIT-BIH AFDB — binary AF detection",
      "short": "MIT-BIH AFDB",
      "input_channels": 1,
      "output_size": 1,
      "summary": "Long two-lead Holter recordings. The dataset registers four rhythm classes, but this task collapses them to AF versus non-AF, so precision, recall and F1 are positive-class values rather than macro averages.",
      "dataset_url": "https://physionet.org/content/afdb/1.0.0/",
      "ecgbench_url": null,
      "note": null
    },
    {
      "id": "ltafdb-binary",
      "dataset": "ltafdb",
      "title": "LTAFDB — binary AF detection",
      "short": "LTAFDB",
      "input_channels": 1,
      "output_size": 1,
      "summary": "The Long-Term AF Database. As for MIT-BIH AFDB, the four registered rhythm classes are collapsed to AF versus non-AF.",
      "dataset_url": "https://physionet.org/content/ltafdb/1.0.0/",
      "ecgbench_url": null,
      "note": null
    },
    {
      "id": "unified-af-4class",
      "dataset": "unified-af",
      "title": "Unified AF — cross-source four-class",
      "short": "Unified AF",
      "input_channels": 1,
      "output_size": 4,
      "summary": "A combined corpus over PhysioNet 2017, MIT-BIH AFDB and LTAFDB, with four rhythm classes.",
      "dataset_url": null,
      "ecgbench_url": null,
      "note": "Results from deepecg-kit 0.2.0 on this task are suspect: accuracy and MCC are implausibly high relative to macro-F1, which points to a leaking or severely imbalanced split."
    }
  ],
  "metrics": [
    {
      "id": "f1",
      "label": "F1",
      "name": "F1",
      "better": "higher",
      "group": null
    },
    {
      "id": "auroc",
      "label": "AUROC",
      "name": "AUROC",
      "better": "higher",
      "group": null
    },
    {
      "id": "accuracy",
      "label": "Acc.",
      "name": "Accuracy",
      "better": "higher",
      "group": null
    },
    {
      "id": "mcc",
      "label": "MCC",
      "name": "MCC",
      "better": "higher",
      "group": null
    },
    {
      "id": "precision",
      "label": "Prec.",
      "name": "Precision",
      "better": "higher",
      "group": null
    },
    {
      "id": "recall",
      "label": "Recall",
      "name": "Recall",
      "better": "higher",
      "group": null
    },
    {
      "id": "exact_match_accuracy",
      "label": "Exact match",
      "name": "Exact-match accuracy",
      "better": "higher",
      "group": null
    },
    {
      "id": "label_accuracy",
      "label": "Label acc.",
      "name": "Label accuracy",
      "better": "higher",
      "group": null
    },
    {
      "id": "ece",
      "label": "ECE",
      "name": "ECE",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "adaptive_ece",
      "label": "AECE",
      "name": "Adaptive ECE",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "mce",
      "label": "MCE",
      "name": "MCE",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "brier",
      "label": "Brier",
      "name": "Brier score",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "temperature",
      "label": "T",
      "name": "Temperature",
      "better": null,
      "group": "calibration"
    },
    {
      "id": "ece_temp",
      "label": "ECE (T)",
      "name": "ECE after temperature scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "adaptive_ece_temp",
      "label": "AECE (T)",
      "name": "Adaptive ECE after temperature scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "mce_temp",
      "label": "MCE (T)",
      "name": "MCE after temperature scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "brier_temp",
      "label": "Brier (T)",
      "name": "Brier score after temperature scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "ece_platt",
      "label": "ECE (P)",
      "name": "ECE after per-label scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "adaptive_ece_platt",
      "label": "AECE (P)",
      "name": "Adaptive ECE after per-label scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "mce_platt",
      "label": "MCE (P)",
      "name": "MCE after per-label scaling",
      "better": "lower",
      "group": "calibration"
    },
    {
      "id": "brier_platt",
      "label": "Brier (P)",
      "name": "Brier score after per-label scaling",
      "better": "lower",
      "group": "calibration"
    }
  ],
  "models": [
    {
      "name": "afmodel",
      "family": "cnn",
      "summary": "A convolutional classifier shaped for fixed 30-second single-lead segments.",
      "description": null,
      "paper": null,
      "code_url": null,
      "adapted_from": null,
      "notes": "Carries default_kwargs {'recording_length': 30} and a minimum input length of 7971 samples, so it is the one architecture that cannot accept an arbitrarily short window.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "convnext-v2",
      "family": "cnn",
      "summary": "Depthwise convolutions with global response normalisation, adapted to one dimension.",
      "description": null,
      "paper": {
        "title": "ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders",
        "authors": "Woo, Debnath, Hu, Chen, Liu, Kweon, Xie",
        "venue": "CVPR",
        "year": 2023,
        "id": "10.1109/CVPR52729.2023.01548",
        "url": "https://doi.org/10.1109/CVPR52729.2023.01548"
      },
      "code_url": "https://github.com/facebookresearch/ConvNeXt-V2",
      "adapted_from": null,
      "notes": "Adapted from the 2D vision architecture; the masked-autoencoder pretraining that motivates GRN is not used here, so the block is trained from scratch.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "crnn",
      "family": "hybrid",
      "summary": "Convolutional feature extraction followed by an LSTM that aggregates over time.",
      "description": null,
      "paper": {
        "title": "Convolutional Recurrent Neural Networks for Electrocardiogram Classification",
        "authors": "Zihlmann, Perekrestenko, Tschannen",
        "venue": "Computing in Cardiology",
        "year": 2017,
        "id": "10.22489/CinC.2017.070-060",
        "url": "https://doi.org/10.22489/CinC.2017.070-060"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": "Follows the convolutional-recurrent design of Zihlmann et al. without reproducing their network; the convolutional front end and recurrent aggregator are both standard.",
      "deviations": [
        "Convolves the raw signal, where the original network takes logarithmic spectrograms."
      ],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "deep-res-cnn",
      "family": "cnn",
      "summary": "A deep residual network that treats the multi-lead recording as a 2D image.",
      "description": null,
      "paper": {
        "title": "Deep residual 2D convolutional neural network for cardiovascular disease classification",
        "authors": "Elyamani, Salem, Melgani, Yhiea",
        "venue": "Scientific Reports",
        "year": 2024,
        "id": "10.1038/s41598-024-72382-3",
        "url": "https://doi.org/10.1038/s41598-024-72382-3"
      },
      "code_url": "https://github.com/HaneenElyamani/ECG-classification",
      "adapted_from": null,
      "notes": "Operates on a 2D (leads x time) view rather than 1D channels, so lead ordering is spatially meaningful to this model in a way it is not for the 1D trunks.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "dualnet",
      "family": "hybrid",
      "summary": "Two parallel branches -- a CNN-LSTM path and a transformer path -- fused before the head.",
      "description": null,
      "paper": null,
      "code_url": null,
      "adapted_from": null,
      "notes": "Bounded above at 5000 input samples by the transformer branch's positional encoding.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "fcn-wang",
      "family": "cnn",
      "summary": "Fully convolutional network with global average pooling and no fully-connected trunk.",
      "description": null,
      "paper": {
        "title": "Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline",
        "authors": "Wang, Yan, Oates",
        "venue": "IJCNN",
        "year": 2017,
        "id": "10.1109/IJCNN.2017.7966039",
        "url": "https://doi.org/10.1109/IJCNN.2017.7966039"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "gru",
      "family": "rnn",
      "summary": "A gated recurrent baseline; cheaper per step than LSTM with a comparable role.",
      "description": null,
      "paper": {
        "title": "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation",
        "authors": "Cho, van Merrienboer, Gulcehre, Bahdanau, Bougares, Schwenk, Bengio",
        "venue": "EMNLP",
        "year": 2014,
        "id": "arXiv:1406.1078",
        "url": "https://arxiv.org/abs/1406.1078"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "inception-time",
      "family": "cnn",
      "summary": "Parallel convolutions at several kernel widths, capturing multi-scale temporal structure.",
      "description": null,
      "paper": {
        "title": "InceptionTime: Finding AlexNet for Time Series Classification",
        "authors": "Fawaz, Lucas, Forestier, Pelletier, Schmidt, Weber, Webb, Idoumghar, Muller, Petitjean",
        "venue": "Data Mining and Knowledge Discovery",
        "year": 2020,
        "id": "10.1007/s10618-020-00710-y",
        "url": "https://doi.org/10.1007/s10618-020-00710-y"
      },
      "code_url": "https://github.com/hfawaz/InceptionTime",
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "kanres-deep",
      "family": "cnn",
      "summary": "The deep KanRes variant: more residual blocks, a narrower final feature dimension.",
      "description": null,
      "paper": {
        "title": "Explaining deep neural networks for knowledge discovery in electrocardiogram analysis",
        "authors": "Hicks, Isaksen, Thambawita, Ghouse, Ahlberg, Linneberg, Grarup, Strümke, Ellervik, Olesen, Hansen, Graff, Holstein-Rathlou, Halvorsen, Maleckar, Riegler, Kanters",
        "venue": "Scientific Reports",
        "year": 2021,
        "id": "10.1038/s41598-021-90285-5",
        "url": "https://doi.org/10.1038/s41598-021-90285-5"
      },
      "code_url": null,
      "adapted_from": "kanres",
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "kanres",
      "family": "cnn",
      "summary": "A compact residual convolutional network with three wide residual blocks.",
      "description": null,
      "paper": {
        "title": "Explaining deep neural networks for knowledge discovery in electrocardiogram analysis",
        "authors": "Hicks, Isaksen, Thambawita, Ghouse, Ahlberg, Linneberg, Grarup, Strümke, Ellervik, Olesen, Hansen, Graff, Holstein-Rathlou, Halvorsen, Maleckar, Riegler, Kanters",
        "venue": "Scientific Reports",
        "year": 2021,
        "id": "10.1038/s41598-021-90285-5",
        "url": "https://doi.org/10.1038/s41598-021-90285-5"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "lstm",
      "family": "rnn",
      "summary": "A recurrent baseline reading the downsampled signal as a sequence.",
      "description": null,
      "paper": {
        "title": "Long Short-Term Memory",
        "authors": "Hochreiter, Schmidhuber",
        "venue": "Neural Computation",
        "year": 1997,
        "id": "10.1162/neco.1997.9.8.1735",
        "url": "https://doi.org/10.1162/neco.1997.9.8.1735"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "mamba",
      "family": "ssm",
      "summary": "Bidirectional selective state-space model with sub-quadratic mixing over long sequences.",
      "description": null,
      "paper": {
        "title": "Mamba: Linear-Time Sequence Modeling with Selective State Spaces",
        "authors": "Gu, Dao",
        "venue": null,
        "year": 2023,
        "id": "arXiv:2312.00752",
        "url": "https://arxiv.org/abs/2312.00752"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": "Included here because it is a registered architecture with measured results. Note the conference paper excludes Mamba; this site documents the library, not the paper.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "medformer",
      "family": "transformer",
      "summary": "Multi-granularity patching transformer built for medical time series.",
      "description": null,
      "paper": {
        "title": "Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification",
        "authors": "Wang, Chen, Fu, Song, Zhang, Wang, Yang, Wang",
        "venue": "NeurIPS",
        "year": 2024,
        "id": "arXiv:2405.19363",
        "url": "https://arxiv.org/abs/2405.19363"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "mlp-wang",
      "family": "baseline",
      "summary": "The fully connected baseline from the same paper as FCN-Wang: three hidden layers over the flattened signal.",
      "description": "The fully connected baseline of Wang et al.: the signal is flattened and passed through three hidden layers with ReLU activations and dropout.",
      "paper": {
        "title": "Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline",
        "authors": "Wang, Yan, Oates",
        "venue": "IJCNN",
        "year": 2017,
        "id": "10.1109/IJCNN.2017.7966039",
        "url": "https://doi.org/10.1109/IJCNN.2017.7966039"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [
        "The signal is averaged over time to a fixed number of samples per lead before it is flattened, so that any signal length is accepted; the original flattens the series as it is."
      ],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "resnet-hannun",
      "family": "cnn",
      "summary": "The 34-layer pre-activation residual network of Hannun et al. for rhythm classification.",
      "description": "The 34-layer residual network of Hannun et al.: a stem convolution followed by sixteen pre-activation residual blocks of two convolutions each, with long kernels and repeated subsampling.",
      "paper": {
        "title": "Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network",
        "authors": "Hannun, Rajpurkar, Haghpanahi, Tison, Bourn, Turakhia, Ng",
        "venue": "Nature Medicine",
        "year": 2019,
        "id": "10.1038/s41591-018-0268-3",
        "url": "https://doi.org/10.1038/s41591-018-0268-3"
      },
      "code_url": "https://github.com/awni/ecg",
      "adapted_from": null,
      "notes": null,
      "deviations": [
        "The original emits one prediction per output step; here the final feature map is pooled over time and a single classifier is applied."
      ],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "resnet-wang",
      "family": "cnn",
      "summary": "The residual baseline from the same paper as FCN-Wang: three residual blocks over raw signal.",
      "description": null,
      "paper": {
        "title": "Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline",
        "authors": "Wang, Yan, Oates",
        "venue": "IJCNN",
        "year": 2017,
        "id": "10.1109/IJCNN.2017.7966039",
        "url": "https://doi.org/10.1109/IJCNN.2017.7966039"
      },
      "code_url": null,
      "adapted_from": "fcn-wang",
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "resnet",
      "family": "cnn",
      "summary": "A 1D adaptation of the standard residual network, and the project's default trunk.",
      "description": null,
      "paper": {
        "title": "Deep Residual Learning for Image Recognition",
        "authors": "He, Zhang, Ren, Sun",
        "venue": "CVPR",
        "year": 2016,
        "id": "10.1109/CVPR.2016.90",
        "url": "https://doi.org/10.1109/CVPR.2016.90"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "rocket",
      "family": "baseline",
      "summary": "Frozen random convolution kernels summarised by two statistics each, with a linear classifier on top.",
      "description": "A non-deep baseline. Random convolution kernels are drawn once and never trained; each contributes the proportion of positive values and the maximum of its output, and a linear layer classifies the resulting feature vector.",
      "paper": {
        "title": "ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels",
        "authors": "Dempster, Petitjean, Webb",
        "venue": "Data Mining and Knowledge Discovery",
        "year": 2020,
        "id": "10.1007/s10618-020-00701-z",
        "url": "https://doi.org/10.1007/s10618-020-00701-z"
      },
      "code_url": "https://github.com/angus924/rocket",
      "adapted_from": null,
      "notes": "Only the linear classifier is trained, so the parameter count covers that layer alone. The random kernels are frozen buffers saved with the checkpoint, which keeps a run reproducible.",
      "deviations": [
        "Every kernel sees all input channels.",
        "Every kernel uses \"same\" padding, where the original draws the padding at random.",
        "The linear classifier is fitted by gradient descent on the training loss, where the original fits a ridge regression."
      ],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "se-resnet",
      "family": "cnn",
      "summary": "ResNet1D with squeeze-and-excitation blocks recalibrating each channel.",
      "description": null,
      "paper": {
        "title": "Squeeze-and-Excitation Networks",
        "authors": "Hu, Shen, Sun",
        "venue": "CVPR",
        "year": 2018,
        "id": "10.1109/CVPR.2018.00745",
        "url": "https://doi.org/10.1109/CVPR.2018.00745"
      },
      "code_url": null,
      "adapted_from": "resnet",
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "simple-cnn",
      "family": "cnn",
      "summary": "A short stack of convolution-pooling blocks, used as the cheapest sanity baseline.",
      "description": null,
      "paper": null,
      "code_url": null,
      "adapted_from": null,
      "notes": "No published origin: a minimal in-house baseline whose purpose is to make a training pipeline fail loudly rather than to compete.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "spectrogram-cnn",
      "family": "cnn",
      "summary": "A 2-D convolutional network over the log-magnitude spectrogram of each lead.",
      "description": "A time-frequency baseline. Each lead is transformed by a short-time Fourier transform and its log-magnitude spectrogram forms one image channel; four 2-D convolution blocks and global average pooling follow.",
      "paper": null,
      "code_url": null,
      "adapted_from": null,
      "notes": "An in-house design without a published reference implementation. It follows the spectrogram-input approach common among entries to the PhysioNet/CinC 2017 challenge.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "tcn",
      "family": "cnn",
      "summary": "Dilated causal convolutions giving an exponentially growing receptive field.",
      "description": null,
      "paper": {
        "title": "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling",
        "authors": "Bai, Kolter, Koltun",
        "venue": null,
        "year": 2018,
        "id": "arXiv:1803.01271",
        "url": "https://arxiv.org/abs/1803.01271"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "transformer",
      "family": "transformer",
      "summary": "A transformer encoder over timesteps with learned positional encoding.",
      "description": null,
      "paper": {
        "title": "Attention Is All You Need",
        "authors": "Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, Polosukhin",
        "venue": "NeurIPS",
        "year": 2017,
        "id": "arXiv:1706.03762",
        "url": "https://arxiv.org/abs/1706.03762"
      },
      "code_url": null,
      "adapted_from": null,
      "notes": "Positional encoding caps the input at 5000 samples, which is the whole 10-second PTB- XL record at 500 Hz and a fraction of a Holter recording.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "xresnet",
      "family": "cnn",
      "summary": "ResNet with the bag-of-tricks refinements: Mish activation and blur-pool downsampling.",
      "description": null,
      "paper": {
        "title": "Bag of Tricks for Image Classification with Convolutional Neural Networks",
        "authors": "He, Zhang, Zhang, Zhang, Xie, Li",
        "venue": "CVPR",
        "year": 2019,
        "id": "10.1109/CVPR.2019.00065",
        "url": "https://doi.org/10.1109/CVPR.2019.00065"
      },
      "code_url": null,
      "adapted_from": "resnet",
      "notes": null,
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "xresnet1d-benchmark",
      "family": "cnn",
      "summary": "The xResNet1d variant used by the PTB-XL benchmark, with a fixed feature dim and concat pooling.",
      "description": null,
      "paper": {
        "title": "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL",
        "authors": "Strodthoff, Wagner, Schaeffter, Samek",
        "venue": "IEEE JBHI",
        "year": 2021,
        "id": "10.1109/JBHI.2020.3022989",
        "url": "https://doi.org/10.1109/JBHI.2020.3022989"
      },
      "code_url": "https://github.com/helme/ecg_ptbxl_benchmarking",
      "adapted_from": "xresnet",
      "notes": "Kept deliberately faithful to the published benchmark configuration so PTB-XL numbers stay comparable with Strodthoff et al.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    },
    {
      "name": "xresnet1d101",
      "family": "cnn",
      "summary": "The deep xResNet1d of the PTB-XL benchmark, built from bottleneck blocks.",
      "description": "The deep xResNet1d of the PTB-XL benchmark: bottleneck residual blocks, a three-convolution stem and a concatenated average-and-maximum pooling head.",
      "paper": {
        "title": "Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL",
        "authors": "Strodthoff, Wagner, Schaeffter, Samek",
        "venue": "IEEE JBHI",
        "year": 2021,
        "id": "10.1109/JBHI.2020.3022989",
        "url": "https://doi.org/10.1109/JBHI.2020.3022989"
      },
      "code_url": "https://github.com/helme/ecg_ptbxl_benchmarking",
      "adapted_from": "xresnet1d-benchmark",
      "notes": "Follows the xresnet1d101 constructor of the PTB-XL benchmark code, so PTB-XL numbers stay comparable with Strodthoff et al. The fixed-width xresnet1d-benchmark is its shallow counterpart.",
      "deviations": [],
      "explain": {
        "gradient_attribution": true,
        "occlusion": true,
        "class_activation": false
      }
    }
  ]
}