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cr-rvs-radar-ecg-processed

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
ts: string
run: string
event: string
repo: string
private: bool
path: string
stage: string
kept: int64
total: int64
n: int64
msg: string
missing: int64
no_overlap: int64
experiments: struct<A_resting: list<item: string>, A_valsalva: list<item: string>, A_apnea: list<item: string>, B (... 53 chars omitted)
  child 0, A_resting: list<item: string>
      child 0, item: string
  child 1, A_valsalva: list<item: string>
      child 0, item: string
  child 2, A_apnea: list<item: string>
      child 0, item: string
  child 3, B_rva: list<item: string>
      child 0, item: string
  child 4, C_all5: list<item: string>
      child 0, item: string
fold_groups: struct<GDN0025_no02: int64, GDN0023: int64, GDN0027: int64, GDN0028: int64, GDN0029: int64, GDN0011: (... 343 chars omitted)
  child 0, GDN0025_no02: int64
  child 1, GDN0023: int64
  child 2, GDN0027: int64
  child 3, GDN0028: int64
  child 4, GDN0029: int64
  child 5, GDN0011: int64
  child 6, GDN0009: int64
  child 7, GDN0020: int64
  child 8, GDN0021: int64
  child 9, GDN0012: int64
  child 10, GDN0013: int64
  child 11, GDN0016: int64
  child 12, GDN0002: int64
  child 13, GDN0030: int64
  child 14, GDN0022: int64
  child 15, GDN0019: int64
  child 16, GDN0003: int64
  child 17, GDN0010: int64
  child 18, GDN0026: int64
  child 19, GDN0004: int64
  child 20, GDN0008: int64
  child 21, GDN0018: int64
  child 22, GDN0014: int64
  child 23, GND0006: int64
  child 24, GDN0024: int64
  child 25, GDN0005: int64
  child 26, GDN0001: int64
channels: list<item: string>
  child 0, item: string
fs: int64
hop_train: int64
n_folds: int64
loso_ids: struct<GDN0001: int64, GDN0002: int64, GDN0003: int64, GDN0004: int64, GDN0005: int64, GDN0008: int6 (... 343 chars omitted)
  child 0, GDN0001: int64
  child 1, GDN0002: int64
  child 2, GDN0003: int64
  child 3, GDN0004: int64
  child 4, GDN0005: int64
  child 5, GDN0008: int64
  child 6, GDN0009: int64
  child 7, GDN0010: int64
  child 8, GDN0011: int64
  child 9, GDN0012: int64
  child 10, GDN0013: int64
  child 11, GDN0014: int64
  child 12, GDN0016: int64
  child 13, GDN0018: int64
  child 14, GDN0019: int64
  child 15, GDN0020: int64
  child 16, GDN0021: int64
  child 17, GDN0022: int64
  child 18, GDN0023: int64
  child 19, GDN0024: int64
  child 20, GDN0025_no02: int64
  child 21, GDN0026: int64
  child 22, GDN0027: int64
  child 23, GDN0028: int64
  child 24, GDN0029: int64
  child 25, GDN0030: int64
  child 26, GND0006: int64
window: int64
to
{'experiments': {'A_resting': List(Value('string')), 'A_valsalva': List(Value('string')), 'A_apnea': List(Value('string')), 'B_rva': List(Value('string')), 'C_all5': List(Value('string'))}, 'n_folds': Value('int64'), 'fold_groups': {'GDN0025_no02': Value('int64'), 'GDN0023': Value('int64'), 'GDN0027': Value('int64'), 'GDN0028': Value('int64'), 'GDN0029': Value('int64'), 'GDN0011': Value('int64'), 'GDN0009': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0016': Value('int64'), 'GDN0002': Value('int64'), 'GDN0030': Value('int64'), 'GDN0022': Value('int64'), 'GDN0019': Value('int64'), 'GDN0003': Value('int64'), 'GDN0010': Value('int64'), 'GDN0026': Value('int64'), 'GDN0004': Value('int64'), 'GDN0008': Value('int64'), 'GDN0018': Value('int64'), 'GDN0014': Value('int64'), 'GND0006': Value('int64'), 'GDN0024': Value('int64'), 'GDN0005': Value('int64'), 'GDN0001': Value('int64')}, 'loso_ids': {'GDN0001': Value('int64'), 'GDN0002': Value('int64'), 'GDN0003': Value('int64'), 'GDN0004': Value('int64'), 'GDN0005': Value('int64'), 'GDN0008': Value('int64'), 'GDN0009': Value('int64'), 'GDN0010': Value('int64'), 'GDN0011': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0014': Value('int64'), 'GDN0016': Value('int64'), 'GDN0018': Value('int64'), 'GDN0019': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0022': Value('int64'), 'GDN0023': Value('int64'), 'GDN0024': Value('int64'), 'GDN0025_no02': Value('int64'), 'GDN0026': Value('int64'), 'GDN0027': Value('int64'), 'GDN0028': Value('int64'), 'GDN0029': Value('int64'), 'GDN0030': Value('int64'), 'GND0006': Value('int64')}, 'window': Value('int64'), 'hop_train': Value('int64'), 'fs': Value('int64'), 'channels': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              ts: string
              run: string
              event: string
              repo: string
              private: bool
              path: string
              stage: string
              kept: int64
              total: int64
              n: int64
              msg: string
              missing: int64
              no_overlap: int64
              experiments: struct<A_resting: list<item: string>, A_valsalva: list<item: string>, A_apnea: list<item: string>, B (... 53 chars omitted)
                child 0, A_resting: list<item: string>
                    child 0, item: string
                child 1, A_valsalva: list<item: string>
                    child 0, item: string
                child 2, A_apnea: list<item: string>
                    child 0, item: string
                child 3, B_rva: list<item: string>
                    child 0, item: string
                child 4, C_all5: list<item: string>
                    child 0, item: string
              fold_groups: struct<GDN0025_no02: int64, GDN0023: int64, GDN0027: int64, GDN0028: int64, GDN0029: int64, GDN0011: (... 343 chars omitted)
                child 0, GDN0025_no02: int64
                child 1, GDN0023: int64
                child 2, GDN0027: int64
                child 3, GDN0028: int64
                child 4, GDN0029: int64
                child 5, GDN0011: int64
                child 6, GDN0009: int64
                child 7, GDN0020: int64
                child 8, GDN0021: int64
                child 9, GDN0012: int64
                child 10, GDN0013: int64
                child 11, GDN0016: int64
                child 12, GDN0002: int64
                child 13, GDN0030: int64
                child 14, GDN0022: int64
                child 15, GDN0019: int64
                child 16, GDN0003: int64
                child 17, GDN0010: int64
                child 18, GDN0026: int64
                child 19, GDN0004: int64
                child 20, GDN0008: int64
                child 21, GDN0018: int64
                child 22, GDN0014: int64
                child 23, GND0006: int64
                child 24, GDN0024: int64
                child 25, GDN0005: int64
                child 26, GDN0001: int64
              channels: list<item: string>
                child 0, item: string
              fs: int64
              hop_train: int64
              n_folds: int64
              loso_ids: struct<GDN0001: int64, GDN0002: int64, GDN0003: int64, GDN0004: int64, GDN0005: int64, GDN0008: int6 (... 343 chars omitted)
                child 0, GDN0001: int64
                child 1, GDN0002: int64
                child 2, GDN0003: int64
                child 3, GDN0004: int64
                child 4, GDN0005: int64
                child 5, GDN0008: int64
                child 6, GDN0009: int64
                child 7, GDN0010: int64
                child 8, GDN0011: int64
                child 9, GDN0012: int64
                child 10, GDN0013: int64
                child 11, GDN0014: int64
                child 12, GDN0016: int64
                child 13, GDN0018: int64
                child 14, GDN0019: int64
                child 15, GDN0020: int64
                child 16, GDN0021: int64
                child 17, GDN0022: int64
                child 18, GDN0023: int64
                child 19, GDN0024: int64
                child 20, GDN0025_no02: int64
                child 21, GDN0026: int64
                child 22, GDN0027: int64
                child 23, GDN0028: int64
                child 24, GDN0029: int64
                child 25, GDN0030: int64
                child 26, GND0006: int64
              window: int64
              to
              {'experiments': {'A_resting': List(Value('string')), 'A_valsalva': List(Value('string')), 'A_apnea': List(Value('string')), 'B_rva': List(Value('string')), 'C_all5': List(Value('string'))}, 'n_folds': Value('int64'), 'fold_groups': {'GDN0025_no02': Value('int64'), 'GDN0023': Value('int64'), 'GDN0027': Value('int64'), 'GDN0028': Value('int64'), 'GDN0029': Value('int64'), 'GDN0011': Value('int64'), 'GDN0009': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0016': Value('int64'), 'GDN0002': Value('int64'), 'GDN0030': Value('int64'), 'GDN0022': Value('int64'), 'GDN0019': Value('int64'), 'GDN0003': Value('int64'), 'GDN0010': Value('int64'), 'GDN0026': Value('int64'), 'GDN0004': Value('int64'), 'GDN0008': Value('int64'), 'GDN0018': Value('int64'), 'GDN0014': Value('int64'), 'GND0006': Value('int64'), 'GDN0024': Value('int64'), 'GDN0005': Value('int64'), 'GDN0001': Value('int64')}, 'loso_ids': {'GDN0001': Value('int64'), 'GDN0002': Value('int64'), 'GDN0003': Value('int64'), 'GDN0004': Value('int64'), 'GDN0005': Value('int64'), 'GDN0008': Value('int64'), 'GDN0009': Value('int64'), 'GDN0010': Value('int64'), 'GDN0011': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0014': Value('int64'), 'GDN0016': Value('int64'), 'GDN0018': Value('int64'), 'GDN0019': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0022': Value('int64'), 'GDN0023': Value('int64'), 'GDN0024': Value('int64'), 'GDN0025_no02': Value('int64'), 'GDN0026': Value('int64'), 'GDN0027': Value('int64'), 'GDN0028': Value('int64'), 'GDN0029': Value('int64'), 'GDN0030': Value('int64'), 'GND0006': Value('int64')}, 'window': Value('int64'), 'hop_train': Value('int64'), 'fs': Value('int64'), 'channels': List(Value('string'))}
              because column names don't match

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CR-RVS Radar-to-ECG — training corpus

Windowed, fold-assigned training corpus for CardioMamba-Net, derived from the CR-RVS dataset (Schellenberger et al., Sci Data 7:291, 2020) via 01_verify_and_download and 02_preprocess_to_hf. Built 2026-09-01 16:38 UTC, run nb02_corpus_v1.

Path	What
`recordings/<subject>__<scenario>.npz`	8 radar channels + 3 targets at 128 Hz, per recording
`windows.parquet`	One row per window: `rec_id, subject, scenario_canon, start, no_overlap, fold_group, loso_id`
`recordings.csv`	Per-recording metadata, HR/HRV, beat coupling, warning flags
`inventory_gated.csv`	Full NB01 inventory plus `keep` / `exclude_reason`
`norm_stats.json`	Per experiment x fold channel mean/std, train windows only
`experiments.json`	Experiment definitions, fold groups, LOSO ids, channel order
`crvs_sync.py`, `crvs_data.py`, `crvs_metrics.py`	Shared library used by NB03-NB05
`figures/`	Sanity figures

Arrays in each recording

I, Q, phi, dy, vel, acc, amp, cardiac — the 8 physics-informed input channels (unnormalised), plus ecg_norm (target, [-1,1]), peak_map (Gaussian R-peak heatmap, sigma=3 samples), rr_ms (per-sample RR interval in real milliseconds), r_peaks (indices).

Conventions

128 Hz, 1024-sample (8 s) windows, frozen to Chowdhury et al. 2024 section 2.3.
Train windows overlap 50 %; validation and test windows do not overlap (no_overlap).
Splits are always by subject. Fold f: test = group f, val = group (f+1) mod 5, train = rest.
Normalisation statistics are per fold and computed on training windows only.
ECG target is [-1, 1], not [0, 1] as in the baseline; this is a declared deviation.

Quality gate

Recordings are excluded when the radar cannot be shown to see the heartbeat (beat_coupling < 1.3), when the ECG is flat or unreadable, when radar channels are missing, or when the recording is under 60 s. Every exclusion and its reason is in inventory_gated.csv.

Cite

Schellenberger et al., Scientific Data 7:291 (2020), doi:10.1038/s41597-020-00629-5 · Chowdhury et al., Computers in Biology and Medicine 176:108555 (2024), doi:10.1016/j.compbiomed.2024.108555

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Total file size:

223 MB

CR-RVS Radar-to-ECG — training corpus

Contents

Arrays in each recording

Conventions

Quality gate

Cite