Datasets:

smam
/

chb-mit-seizure-windows-2to1

Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      Mismatching child array lengths
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 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2060, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2083, 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 544, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 383, 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/hdf5/hdf5.py", line 87, in _generate_tables
                  pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
                  arr = _recursive_load_arrays(dset, features[path], start, end)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 294, in _recursive_load_arrays
                  sarr = pa.StructArray.from_arrays(values, names=keys)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 4294, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Mismatching child array lengths

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YAML Metadata Warning:The task_categories "time-series-classification" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

CHB-MIT Seizure Detection - Balanced Binary Classification Dataset

Dataset Description

Preprocessed, balanced version of the CHB-MIT Scalp EEG Database for binary seizure classification tasks.

Key Features

Window Size: 8 seconds
Sample Rate: 200 Hz
Channels: 18 (standardized from original 23)
Samples per Window: 1600
Non-seizure ratio: 2:1 (non-seizure:seizure)

Balance Strategy

Only full seizure windows where the entire 8s is within a seizure
50% overlap for seizure windows, random sampling for non-seizure
2x non-seizure windows per seizure window per patient

Data Format

HDF5 file with structure:

/chbXX/
    seizure: (n_seizure, 18, 1600) float32
    non_seizure: (n_non_seizure, 18, 1600) float32
    attrs: n_seizure, n_non_seizure

Usage

import h5py
import numpy as np
from huggingface_hub import hf_hub_download

file_path = hf_hub_download(
    repo_id="smam/chb-mit-seizure-balanced-8s",
    filename="chb-mit-seizure-balanced-8s-18ch.h5",
    repo_type="dataset"
)

with h5py.File(file_path, 'r') as f:
    print(f.attrs['description'])
    seizure_data = f['chb01/seizure'][:]       # (N, 18, 1600)
    non_seizure_data = f['chb01/non_seizure'][:] # (M, 18, 1600)

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Total file size:

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