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Cleaned and Updated SpaRCNET examples for EEG event classification and abnormal prediction #839

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Merged
jhnwu3 merged 2 commits into from
Feb 10, 2026
Merged

Cleaned and Updated SpaRCNET examples for EEG event classification and abnormal prediction #839

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449916d
Updated TUEVDataset and EEGEventsTUEV to latest version
Jathurshan0330 Feb 10, 2026
3e68380
Cleaned and updated example files for SpaRCNet
Jathurshan0330 Feb 10, 2026
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20 changes: 16 additions & 4 deletions ...zure_detection/EEG_isAbnormal_SparcNet.py → ...t/EEG_abnormal_classification_SparcNet.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,30 @@
from pyhealth.datasets import split_by_visit, get_dataloader
from pyhealth.trainer import Trainer
from pyhealth.datasets import TUABDataset
from pyhealth.tasks import EEG_isAbnormal_fn
from pyhealth.tasks import EEGAbnormalTUAB
from pyhealth.models import SparcNet

# step 1: load signal data
dataset = TUABDataset(root="/srv/local/data/TUH/tuh_eeg_abnormal/v3.0.0/edf/",
dev=True,
refresh_cache=True,
)

print(dataset.stats())
# step 2: set task
TUAB_ds = dataset.set_task(EEG_isAbnormal_fn)
TUAB_ds.stat()
TUAB_ds = dataset.set_task(EEGAbnormalTUAB(
resample_rate=200,
bandpass_filter=(0.1, 75.0),
notch_filter=50.0,
))
print(f"Total task samples: {len(TUAB_ds)}")
print(f"Input schema: {TUAB_ds.input_schema}")
print(f"Output schema: {TUAB_ds.output_schema}")

# Inspect a sample
sample = TUAB_ds[0]
print(f"\nSample keys: {sample.keys()}")
print(f"Signal shape: {sample['signal'].shape}")
print(f"Label: {sample['label']}")

# split dataset
train_dataset, val_dataset, test_dataset = split_by_visit(
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22 changes: 18 additions & 4 deletions .../seizure_detection/EEG_events_SparcNet.py → ...Net/EEG_events_classification_SparcNet.py
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Original file line number Diff line number Diff line change
@@ -1,18 +1,32 @@
from pyhealth.datasets import split_by_visit, get_dataloader
from pyhealth.trainer import Trainer
from pyhealth.datasets import TUEVDataset
from pyhealth.tasks import EEG_events_fn
from pyhealth.tasks import EEGEventsTUEV
from pyhealth.models import SparcNet

# step 1: load signal data
dataset = TUEVDataset(root="/srv/local/data/TUH/tuh_eeg_events/v2.0.0/edf/",
dev=True,
refresh_cache=True,
)

print(dataset.stats())
# step 2: set task
TUEV_ds = dataset.set_task(EEG_events_fn)
TUEV_ds.stat()
TUEV_ds = dataset.set_task(EEGEventsTUEV(
resample_rate=200, # Resample rate
bandpass_filter=(0.1, 75.0), # Bandpass filter
notch_filter=50.0, # Notch filter
))

print(f"Total task samples: {len(TUEV_ds)}")
print(f"Input schema: {TUEV_ds.input_schema}")
print(f"Output schema: {TUEV_ds.output_schema}")

# Inspect a sample
sample = TUEV_ds[0]
print(f"\nSample keys: {sample.keys()}")
print(f"Signal shape: {sample['signal'].shape}")
print(f"Label: {sample['label']}")


# split dataset
train_dataset, val_dataset, test_dataset = split_by_visit(
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