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Updated TUH dataset EEG tasks with preprocessing parameters #838

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Merged
jhnwu3 merged 4 commits into from
Feb 10, 2026
Merged

Updated TUH dataset EEG tasks with preprocessing parameters #838

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6633571
Fix to TUH tasks - flexibility in preprocessing parameters
Jathurshan0330 Feb 10, 2026
534ce4c
Fix: tuev_test for default preprocessing parameters, resampling rate …
Jathurshan0330 Feb 10, 2026
40d904d
Fix: updated examples for TUH tasks
Jathurshan0330 Feb 10, 2026
baae593
Updated docs for temple university EEG tasks
Jathurshan0330 Feb 10, 2026
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5 changes: 5 additions & 0 deletions docs/api/tasks/pyhealth.tasks.temple_university_EEG_tasks.rst
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Expand Up @@ -7,6 +7,11 @@ The tasks are:
- EEGEventsTUEV: EEG event classification task for the TUEV dataset.
- EEGAbnormalTUAB: Binary classification task for the TUAB dataset (abnormal vs normal).

Tasks Parameters:
- resample_rate: int, default=200 # Resample rate
- bandpass_filter: tuple, default=(0.1, 75.0) # Bandpass filter
- notch_filter: float, default=50.0 # Notch filter

.. autoclass:: pyhealth.tasks.temple_university_EEG_tasks.EEGEventsTUEV
:members:
:undoc-members:
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84 changes: 39 additions & 45 deletions examples/eeg/tuh_eeg/tuab_abnormal_detection.ipynb
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Expand Up @@ -56,7 +56,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 2,
"id": "d1230c58",
"metadata": {},
"outputs": [
Expand All @@ -69,10 +69,6 @@
"Using cached metadata from /home/jp65/.cache/pyhealth/tuab\n",
"Initializing tuab dataset from /home/jp65/.cache/pyhealth/tuab (dev mode: True)\n",
"No cache_dir provided. Using default cache dir: /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2\n",
"Scanning table: train from /home/jp65/.cache/pyhealth/tuab/tuab-train-pyhealth.csv\n",
"Scanning table: eval from /home/jp65/.cache/pyhealth/tuab/tuab-eval-pyhealth.csv\n",
"Dev mode enabled: limiting to 1000 patients\n",
"Caching event dataframe to /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2/global_event_df.parquet...\n",
"Dataset: tuab\n",
"Dev mode: True\n",
"Number of patients: 1000\n",
Expand All @@ -99,7 +95,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 3,
"id": "66f68916",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -132,9 +128,9 @@
"name": "stderr",
"output_type": "stream",
"text": [
" 51%|█████ | 512/1000 [15:10<14:20, 1.76s/it]/home/jp65/miniconda3/envs/pyhealth/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
" 90%|████████▉ | 896/1000 [26:01<02:47, 1.61s/it]/home/jp65/miniconda3/envs/pyhealth/lib/python3.12/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
" warnings.warn(\n",
"100%|██████████| 1000/1000 [28:09<00:00, 1.69s/it]"
"100%|██████████| 1000/1000 [28:39<00:00, 1.72s/it]"
]
},
{
Expand All @@ -157,7 +153,7 @@
"output_type": "stream",
"text": [
"Label label vocab: {0: 0, 1: 1}\n",
"Processing samples and saving to /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2/tasks/EEG_abnormal_f8cedbe4-72a8-53c3-922d-4cc8730f4c2d/samples_160cf897-aed5-541d-8dff-f65c7676d862.ld...\n",
"Processing samples and saving to /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2/tasks/EEG_abnormal_d595851a-c8f6-5f1d-bdfb-b3d527c2deb0/samples_160cf897-aed5-541d-8dff-f65c7676d862.ld...\n",
"Applying processors on data with 1 workers...\n",
"Detected Jupyter notebook environment, setting num_workers to 1\n",
"Single worker mode, processing sequentially\n",
Expand All @@ -182,28 +178,15 @@
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 178760/178760 [05:07<00:00, 580.75it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Worker 0 finished processing samples.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
"100%|██████████| 178760/178760 [05:16<00:00, 564.46it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cached processed samples to /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2/tasks/EEG_abnormal_f8cedbe4-72a8-53c3-922d-4cc8730f4c2d/samples_160cf897-aed5-541d-8dff-f65c7676d862.ld\n",
"Worker 0 finished processing samples.\n",
"Cached processed samples to /home/jp65/.cache/pyhealth/9e59c95b-42bb-596b-a667-dd694bc64ac2/tasks/EEG_abnormal_d595851a-c8f6-5f1d-bdfb-b3d527c2deb0/samples_160cf897-aed5-541d-8dff-f65c7676d862.ld\n",
"Total task samples: 178760\n",
"Input schema: {'signal': 'tensor'}\n",
"Output schema: {'label': 'binary'}\n",
Expand All @@ -212,10 +195,21 @@
"Signal shape: torch.Size([16, 2000])\n",
"Label: tensor([1.])\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"sample_dataset = dataset.set_task(EEGAbnormalTUAB())\n",
"sample_dataset = dataset.set_task(EEGAbnormalTUAB(\n",
" resample_rate=200, # Resample rate\n",
" bandpass_filter=(0.1, 75.0), # Bandpass filter\n",
" notch_filter=50.0, # Notch filter\n",
"))\n",
"\n",
"print(f\"Total task samples: {len(sample_dataset)}\")\n",
"print(f\"Input schema: {sample_dataset.input_schema}\")\n",
Expand All @@ -239,7 +233,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 4,
"id": "c01a076f",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -276,7 +270,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 5,
"id": "1d490449",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -322,7 +316,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 6,
"id": "7236ddc0",
"metadata": {},
"outputs": [
Expand Down Expand Up @@ -380,7 +374,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 7,
"id": "11d7f9c5",
"metadata": {},
"outputs": [
Expand All @@ -389,7 +383,7 @@
"output_type": "stream",
"text": [
"Model output shape: torch.Size([32, 1])\n",
"Sample output: tensor([-0.9347], device='cuda:0')\n"
"Sample output: tensor([0.1072], device='cuda:0')\n"
]
}
],
Expand Down Expand Up @@ -417,7 +411,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 8,
"id": "5521de25",
"metadata": {},
"outputs": [],
Expand All @@ -437,24 +431,24 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 9,
"id": "0c14a78d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5, Loss: 0.6952\n",
"Validation Loss: 0.6931, Accuracy: 50.77%\n",
"Epoch 2/5, Loss: 0.6931\n",
"Validation Loss: 0.6932, Accuracy: 49.23%\n",
"Epoch 3/5, Loss: 0.6932\n",
"Validation Loss: 0.6931, Accuracy: 50.76%\n",
"Epoch 4/5, Loss: 0.6935\n",
"Validation Loss: 0.6930, Accuracy: 50.77%\n",
"Epoch 5/5, Loss: 0.7017\n",
"Validation Loss: 0.6933, Accuracy: 49.24%\n"
"Epoch 1/5, Loss: 0.6092\n",
"Validation Loss: 0.5229, Accuracy: 76.95%\n",
"Epoch 2/5, Loss: 0.4883\n",
"Validation Loss: 0.5344, Accuracy: 72.80%\n",
"Epoch 3/5, Loss: 0.4586\n",
"Validation Loss: 0.7649, Accuracy: 62.00%\n",
"Epoch 4/5, Loss: 0.4442\n",
"Validation Loss: 0.4706, Accuracy: 77.78%\n",
"Epoch 5/5, Loss: 0.4250\n",
"Validation Loss: 0.4473, Accuracy: 79.42%\n"
]
}
],
Expand Down Expand Up @@ -509,15 +503,15 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 10,
"id": "bbd0eb33",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Loss: 0.6932, Accuracy: 49.61%\n"
"Test Loss: 0.4406, Accuracy: 80.17%\n"
]
}
],
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