Datasets:

Shanmuk4622
/

cr-rvs-radar-ecg-processed-v2

Tags:

License:

Dataset card Files Files and versions

xet

Community

Dataset Viewer

Duplicate

Split (1)

train

The dataset viewer is not available for this split.

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

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
last_push_utc: string
pushes_this_session: int64
last_commit: string
message: string
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
loso_ids: struct<GDN0001: int64, GDN0002: int64, GDN0003: int64, GDN0004: int64, GDN0005: int64, GDN0007: int6 (... 391 chars omitted)
  child 0, GDN0001: int64
  child 1, GDN0002: int64
  child 2, GDN0003: int64
  child 3, GDN0004: int64
  child 4, GDN0005: int64
  child 5, GDN0007: int64
  child 6, GDN0008: int64
  child 7, GDN0009: int64
  child 8, GDN0010: int64
  child 9, GDN0011: int64
  child 10, GDN0012: int64
  child 11, GDN0013: int64
  child 12, GDN0014: int64
  child 13, GDN0015: int64
  child 14, GDN0016: int64
  child 15, GDN0017: int64
  child 16, GDN0018: int64
  child 17, GDN0019: int64
  child 18, GDN0020: int64
  child 19, GDN0021: int64
  child 20, GDN0022: int64
  child 21, GDN0023: int64
  child 22, GDN0024: int64
  child 23, GDN0025_no02: int64
  child 24, GDN0026: int64
  child 25, GDN0027: int64
  child 26, GDN0028: int64
  child 27, GDN0029: int64
  child 28, GDN0030: int64
  child 29, GND0006: int64
cross_scenarios: list<item: string>
  child 0, item: string
n_folds: int64
loso_values: list<item: int64>
  child 0, item: int64
fold_groups: struct<GND0006: int64, GDN0012: int64, GDN0025_no02: int64, GDN0014: int64, GDN0023: int64, GDN0022: (... 391 chars omitted)
  child 0, GND0006: int64
  child 1, GDN0012: int64
  child 2, GDN0025_no02: int64
  child 3, GDN0014: int64
  child 4, GDN0023: int64
  child 5, GDN0022: int64
  child 6, GDN0004: int64
  child 7, GDN0020: int64
  child 8, GDN0021: int64
  child 9, GDN0019: int64
  child 10, GDN0010: int64
  child 11, GDN0027: int64
  child 12, GDN0013: int64
  child 13, GDN0009: int64
  child 14, GDN0028: int64
  child 15, GDN0017: int64
  child 16, GDN0016: int64
  child 17, GDN0007: int64
  child 18, GDN0011: int64
  child 19, GDN0008: int64
  child 20, GDN0029: int64
  child 21, GDN0005: int64
  child 22, GDN0003: int64
  child 23, GDN0002: int64
  child 24, GDN0030: int64
  child 25, GDN0001: int64
  child 26, GDN0026: int64
  child 27, GDN0018: int64
  child 28, GDN0015: int64
  child 29, GDN0024: int64
window: int64
hop_train: int64
channels: list<item: string>
  child 0, item: string
fs: 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': {'GND0006': Value('int64'), 'GDN0012': Value('int64'), 'GDN0025_no02': Value('int64'), 'GDN0014': Value('int64'), 'GDN0023': Value('int64'), 'GDN0022': Value('int64'), 'GDN0004': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0019': Value('int64'), 'GDN0010': Value('int64'), 'GDN0027': Value('int64'), 'GDN0013': Value('int64'), 'GDN0009': Value('int64'), 'GDN0028': Value('int64'), 'GDN0017': Value('int64'), 'GDN0016': Value('int64'), 'GDN0007': Value('int64'), 'GDN0011': Value('int64'), 'GDN0008': Value('int64'), 'GDN0029': Value('int64'), 'GDN0005': Value('int64'), 'GDN0003': Value('int64'), 'GDN0002': Value('int64'), 'GDN0030': Value('int64'), 'GDN0001': Value('int64'), 'GDN0026': Value('int64'), 'GDN0018': Value('int64'), 'GDN0015': Value('int64'), 'GDN0024': Value('int64')}, 'loso_ids': {'GDN0001': Value('int64'), 'GDN0002': Value('int64'), 'GDN0003': Value('int64'), 'GDN0004': Value('int64'), 'GDN0005': Value('int64'), 'GDN0007': Value('int64'), 'GDN0008': Value('int64'), 'GDN0009': Value('int64'), 'GDN0010': Value('int64'), 'GDN0011': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0014': Value('int64'), 'GDN0015': Value('int64'), 'GDN0016': Value('int64'), 'GDN0017': 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')}, 'loso_values': List(Value('int64')), 'cross_scenarios': List(Value('string')), '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
              last_push_utc: string
              pushes_this_session: int64
              last_commit: string
              message: string
              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
              loso_ids: struct<GDN0001: int64, GDN0002: int64, GDN0003: int64, GDN0004: int64, GDN0005: int64, GDN0007: int6 (... 391 chars omitted)
                child 0, GDN0001: int64
                child 1, GDN0002: int64
                child 2, GDN0003: int64
                child 3, GDN0004: int64
                child 4, GDN0005: int64
                child 5, GDN0007: int64
                child 6, GDN0008: int64
                child 7, GDN0009: int64
                child 8, GDN0010: int64
                child 9, GDN0011: int64
                child 10, GDN0012: int64
                child 11, GDN0013: int64
                child 12, GDN0014: int64
                child 13, GDN0015: int64
                child 14, GDN0016: int64
                child 15, GDN0017: int64
                child 16, GDN0018: int64
                child 17, GDN0019: int64
                child 18, GDN0020: int64
                child 19, GDN0021: int64
                child 20, GDN0022: int64
                child 21, GDN0023: int64
                child 22, GDN0024: int64
                child 23, GDN0025_no02: int64
                child 24, GDN0026: int64
                child 25, GDN0027: int64
                child 26, GDN0028: int64
                child 27, GDN0029: int64
                child 28, GDN0030: int64
                child 29, GND0006: int64
              cross_scenarios: list<item: string>
                child 0, item: string
              n_folds: int64
              loso_values: list<item: int64>
                child 0, item: int64
              fold_groups: struct<GND0006: int64, GDN0012: int64, GDN0025_no02: int64, GDN0014: int64, GDN0023: int64, GDN0022: (... 391 chars omitted)
                child 0, GND0006: int64
                child 1, GDN0012: int64
                child 2, GDN0025_no02: int64
                child 3, GDN0014: int64
                child 4, GDN0023: int64
                child 5, GDN0022: int64
                child 6, GDN0004: int64
                child 7, GDN0020: int64
                child 8, GDN0021: int64
                child 9, GDN0019: int64
                child 10, GDN0010: int64
                child 11, GDN0027: int64
                child 12, GDN0013: int64
                child 13, GDN0009: int64
                child 14, GDN0028: int64
                child 15, GDN0017: int64
                child 16, GDN0016: int64
                child 17, GDN0007: int64
                child 18, GDN0011: int64
                child 19, GDN0008: int64
                child 20, GDN0029: int64
                child 21, GDN0005: int64
                child 22, GDN0003: int64
                child 23, GDN0002: int64
                child 24, GDN0030: int64
                child 25, GDN0001: int64
                child 26, GDN0026: int64
                child 27, GDN0018: int64
                child 28, GDN0015: int64
                child 29, GDN0024: int64
              window: int64
              hop_train: int64
              channels: list<item: string>
                child 0, item: string
              fs: 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': {'GND0006': Value('int64'), 'GDN0012': Value('int64'), 'GDN0025_no02': Value('int64'), 'GDN0014': Value('int64'), 'GDN0023': Value('int64'), 'GDN0022': Value('int64'), 'GDN0004': Value('int64'), 'GDN0020': Value('int64'), 'GDN0021': Value('int64'), 'GDN0019': Value('int64'), 'GDN0010': Value('int64'), 'GDN0027': Value('int64'), 'GDN0013': Value('int64'), 'GDN0009': Value('int64'), 'GDN0028': Value('int64'), 'GDN0017': Value('int64'), 'GDN0016': Value('int64'), 'GDN0007': Value('int64'), 'GDN0011': Value('int64'), 'GDN0008': Value('int64'), 'GDN0029': Value('int64'), 'GDN0005': Value('int64'), 'GDN0003': Value('int64'), 'GDN0002': Value('int64'), 'GDN0030': Value('int64'), 'GDN0001': Value('int64'), 'GDN0026': Value('int64'), 'GDN0018': Value('int64'), 'GDN0015': Value('int64'), 'GDN0024': Value('int64')}, 'loso_ids': {'GDN0001': Value('int64'), 'GDN0002': Value('int64'), 'GDN0003': Value('int64'), 'GDN0004': Value('int64'), 'GDN0005': Value('int64'), 'GDN0007': Value('int64'), 'GDN0008': Value('int64'), 'GDN0009': Value('int64'), 'GDN0010': Value('int64'), 'GDN0011': Value('int64'), 'GDN0012': Value('int64'), 'GDN0013': Value('int64'), 'GDN0014': Value('int64'), 'GDN0015': Value('int64'), 'GDN0016': Value('int64'), 'GDN0017': 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')}, 'loso_values': List(Value('int64')), 'cross_scenarios': List(Value('string')), 'window': Value('int64'), 'hop_train': Value('int64'), 'fs': Value('int64'), 'channels': List(Value('string'))}
              because column names don't match

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

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 2026-09-02 13:11 UTC, run nb02_corpus_v2.

Path	What
`recordings/<subject>__<scenario>.npy`	Uncompressed `(11, n)` float32 at 128 Hz — 8 radar channels + 3 targets, row order in `rows`
`recordings/<subject>__<scenario>.json`	Per-recording metadata: `n`, `fs`, `subject`, `scenario`, `rows`, `r_peaks`
`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

Row order of the (11, n) array: I, Q, phi, dy, vel, acc, amp, cardiac — the 8 physics-informed input channels, unnormalised — then ecg_norm (target, [-1,1]), peak_map (Gaussian R-peak heatmap, sigma = 3 samples), rr_ms (per-sample RR interval in real milliseconds). R-peak indices are in the JSON sidecar.

Stored uncompressed on purpose: np.load(..., mmap_mode="r") silently ignores mmap_mode on an .npz, so a compressed archive would force a full decompression of all 11 arrays for every 1024-sample window.

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 only when ECG/radar inputs are unreadable or missing, contain invalid values, or are under 60 s. Beat coupling is retained as a continuous difficulty covariate and is not used to select an easier cohort. 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

Downloads last month: -

Total file size:

474 MB

CR-RVS Radar-to-ECG — training corpus

Contents

Arrays in each recording

Conventions

Quality gate

Cite