Datasets:

4rooms
/

chambon2018-embeddings

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model_name: string
dataset_name: string
embedding_dim: int64
n_subjects: int64
mean_std: struct<accuracy: struct<mean: double, std: double>, macro_f1: struct<mean: double, std: double>, kap (... 38 chars omitted)
  child 0, accuracy: struct<mean: double, std: double>
      child 0, mean: double
      child 1, std: double
  child 1, macro_f1: struct<mean: double, std: double>
      child 0, mean: double
      child 1, std: double
  child 2, kappa: struct<mean: double, std: double>
      child 0, mean: double
      child 1, std: double
pooled: struct<accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: double, N1: double (... 160 chars omitted)
  child 0, accuracy: double
  child 1, macro_f1: double
  child 2, kappa: double
  child 3, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
      child 0, W: double
      child 1, N1: double
      child 2, N2: double
      child 3, N3: double
      child 4, REM: double
  child 4, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
      child 0, W: int64
      child 1, N1: int64
      child 2, N2: int64
      child 3, N3: int64
      child 4, REM: int64
  child 5, confusion_matrix: list<item: list<item: int64>>
      child 0, item: list<item: int64>
          child 0, item: int64
probe_config: struct<max_epochs: int64, lr: double, weight_decay: double, batch_size: int64>
  child 0, max_epochs: int64
  child 1, lr: double
  child 2, weight_decay: double
  child 3, batch_size: int64
n_folds: int64
n_classes: int64
per_fold: list<item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: stru (... 279 chars omitted)
  child 0, item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: doubl (... 267 chars omitted)
      child 0, fold: int64
      child 1, accuracy: double
      child 2, macro_f1: double
      child 3, kappa: double
      child 4, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
          child 0, W: double
          child 1, N1: double
          child 2, N2: double
          child 3, N3: double
          child 4, REM: double
      child 5, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
          child 0, W: int64
          child 1, N1: int64
          child 2, N2: int64
          child 3, N3: int64
          child 4, REM: int64
      child 6, n_train_subjects: int64
      child 7, n_test_subjects: int64
      child 8, n_train_epochs: int64
      child 9, n_test_epochs: int64
      child 10, confusion_matrix: list<item: list<item: int64>>
          child 0, item: list<item: int64>
              child 0, item: int64
class_names: list<item: string>
  child 0, item: string
to
{'model_name': Value('string'), 'dataset_name': Value('string'), 'n_folds': Value('int64'), 'n_classes': Value('int64'), 'class_names': List(Value('string')), 'n_subjects': Value('int64'), 'probe_config': {'max_epochs': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64')}, 'per_fold': List({'fold': Value('int64'), 'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'n_train_subjects': Value('int64'), 'n_test_subjects': Value('int64'), 'n_train_epochs': Value('int64'), 'n_test_epochs': Value('int64'), 'confusion_matrix': List(List(Value('int64')))}), 'pooled': {'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'confusion_matrix': List(List(Value('int64')))}, 'mean_std': {'accuracy': {'mean': Value('float64'), 'std': Value('float64')}, 'macro_f1': {'mean': Value('float64'), 'std': Value('float64')}, 'kappa': {'mean': Value('float64'), 'std': Value('float64')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              model_name: string
              dataset_name: string
              embedding_dim: int64
              n_subjects: int64
              mean_std: struct<accuracy: struct<mean: double, std: double>, macro_f1: struct<mean: double, std: double>, kap (... 38 chars omitted)
                child 0, accuracy: struct<mean: double, std: double>
                    child 0, mean: double
                    child 1, std: double
                child 1, macro_f1: struct<mean: double, std: double>
                    child 0, mean: double
                    child 1, std: double
                child 2, kappa: struct<mean: double, std: double>
                    child 0, mean: double
                    child 1, std: double
              pooled: struct<accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: double, N1: double (... 160 chars omitted)
                child 0, accuracy: double
                child 1, macro_f1: double
                child 2, kappa: double
                child 3, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
                    child 0, W: double
                    child 1, N1: double
                    child 2, N2: double
                    child 3, N3: double
                    child 4, REM: double
                child 4, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
                    child 0, W: int64
                    child 1, N1: int64
                    child 2, N2: int64
                    child 3, N3: int64
                    child 4, REM: int64
                child 5, confusion_matrix: list<item: list<item: int64>>
                    child 0, item: list<item: int64>
                        child 0, item: int64
              probe_config: struct<max_epochs: int64, lr: double, weight_decay: double, batch_size: int64>
                child 0, max_epochs: int64
                child 1, lr: double
                child 2, weight_decay: double
                child 3, batch_size: int64
              n_folds: int64
              n_classes: int64
              per_fold: list<item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: stru (... 279 chars omitted)
                child 0, item: struct<fold: int64, accuracy: double, macro_f1: double, kappa: double, per_class_f1: struct<W: doubl (... 267 chars omitted)
                    child 0, fold: int64
                    child 1, accuracy: double
                    child 2, macro_f1: double
                    child 3, kappa: double
                    child 4, per_class_f1: struct<W: double, N1: double, N2: double, N3: double, REM: double>
                        child 0, W: double
                        child 1, N1: double
                        child 2, N2: double
                        child 3, N3: double
                        child 4, REM: double
                    child 5, support: struct<W: int64, N1: int64, N2: int64, N3: int64, REM: int64>
                        child 0, W: int64
                        child 1, N1: int64
                        child 2, N2: int64
                        child 3, N3: int64
                        child 4, REM: int64
                    child 6, n_train_subjects: int64
                    child 7, n_test_subjects: int64
                    child 8, n_train_epochs: int64
                    child 9, n_test_epochs: int64
                    child 10, confusion_matrix: list<item: list<item: int64>>
                        child 0, item: list<item: int64>
                            child 0, item: int64
              class_names: list<item: string>
                child 0, item: string
              to
              {'model_name': Value('string'), 'dataset_name': Value('string'), 'n_folds': Value('int64'), 'n_classes': Value('int64'), 'class_names': List(Value('string')), 'n_subjects': Value('int64'), 'probe_config': {'max_epochs': Value('int64'), 'lr': Value('float64'), 'weight_decay': Value('float64'), 'batch_size': Value('int64')}, 'per_fold': List({'fold': Value('int64'), 'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'n_train_subjects': Value('int64'), 'n_test_subjects': Value('int64'), 'n_train_epochs': Value('int64'), 'n_test_epochs': Value('int64'), 'confusion_matrix': List(List(Value('int64')))}), 'pooled': {'accuracy': Value('float64'), 'macro_f1': Value('float64'), 'kappa': Value('float64'), 'per_class_f1': {'W': Value('float64'), 'N1': Value('float64'), 'N2': Value('float64'), 'N3': Value('float64'), 'REM': Value('float64')}, 'support': {'W': Value('int64'), 'N1': Value('int64'), 'N2': Value('int64'), 'N3': Value('int64'), 'REM': Value('int64')}, 'confusion_matrix': List(List(Value('int64')))}, 'mean_std': {'accuracy': {'mean': Value('float64'), 'std': Value('float64')}, 'macro_f1': {'mean': Value('float64'), 'std': Value('float64')}, 'kappa': {'mean': Value('float64'), 'std': Value('float64')}}}
              because column names don't match

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Chambon2018 Embeddings

Pre-extracted per-epoch embeddings from Chambon2018 (Chambon et al. 2018), trained on MASS SS3 via PhysioEx.

Each subject directory contains:

embeddings.npy — (n_epochs, 120) per-epoch embeddings (bfloat16)
labels.npy — (n_epochs,) AASM sleep stage labels

Note: Chambon2018 is a per-epoch CNN encoder (no inter-epoch context). Embeddings are extracted directly without sliding-window voting.

Usage

from physioex.models import load_embeddings
path = load_embeddings("chambon2018", "hmc", verbose=True)

Linear Probe Results (5-fold subject-wise CV)

Dataset	Subjects	ACC	MF1	κ	F1-W	F1-N1	F1-N2	F1-N3	F1-REM
mass_ss05	26	0.8146	0.6653	0.7284	0.74	0.08	0.88	0.83	0.80
mass_ss02	19	0.7902	0.6397	0.6919	0.74	0.05	0.86	0.79	0.75
mass_ss03	62	0.7869	0.6638	0.6806	0.79	0.15	0.87	0.75	0.76
mass_ss04	40	0.7777	0.6353	0.6785	0.73	0.06	0.87	0.78	0.75
dcsm	255	0.7218	0.5239	0.5706	0.82	0.00	0.74	0.72	0.34
mass_ss01	53	0.7134	0.5899	0.5882	0.81	0.23	0.82	0.48	0.61
sleepedf	153	0.6001	0.4502	0.4198	0.65	0.00	0.72	0.52	0.36
hmc	151	0.5980	0.5099	0.4581	0.66	0.03	0.65	0.67	0.54

SHHS, MESA, HPAP, STAGES results pending (extraction running on Sofia HPC).

Model Details

Architecture: Chambon2018 (Chambon et al. 2018) — braindecode SleepStagerChambon2018 per-epoch CNN
Training data: MASS SS3 (62 subjects, single EEG channel at 128 Hz)
Pipeline: raw (bandpass 0.3-40 Hz, resample 128 Hz)
Sequence length: L=3 (central epoch classification)
Embedding dim: 120

Datasets

Dataset	Source	URL
Sleep-EDF	PhysioNet	https://physionet.org/content/sleep-edfx/1.0.0/
HMC	PhysioNet	https://physionet.org/content/hmc-sleep-staging/1.1/
DCSM	ERDA/KU	https://erda.ku.dk/public/archives/db553715ecbe1f3ac66c1dc569826eef/published-archive.html
MASS	CEAMS	http://ceams-carsm.ca/mass/

Citations

@article{gagliardi2025physioex,
    author={Gagliardi, Guido and Alfeo, Luca and Cimino, Mario G C A and Valenza, Gaetano and De Vos, Maarten},
    title={PhysioEx, a new Python library for explainable sleep staging through deep learning},
    journal={Physiological Measurement},
    url={http://iopscience.iop.org/article/10.1088/1361-6579/adaf73},
    year={2025},
}

@article{chambon2018deep,
    title={A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series},
    author={Chambon, Stanislas and Galtier, Mathieu and Arnal, Pierrick and Wainrib, Gilles and Gramfort, Alexandre},
    journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
    year={2018},
}

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