pytorch/iednet_lite/config.json

{
  "binary_labels": [
    "Non-IED",
    "IED"
  ],
  "channels": [
    "Fp1",
    "Fp2",
    "F3",
    "F4",
    "C3",
    "C4",
    "P3",
    "P4",
    "O1",
    "O2",
    "F7",
    "F8",
    "T3",
    "T4",
    "T5",
    "T6",
    "Fz",
    "Cz",
    "Pz"
  ],
  "epoch": 13,
  "format_version": 1,
  "model_name": "iednet_lite",
  "parameters": 245260,
  "preprocessing": {
    "bandpass_hz": [
      1.0,
      45.0
    ],
    "normalization": "global_window_zscore_clip8",
    "reference": "average"
  },
  "release_name": "iednet_lite",
  "sfreq": 250,
  "split_seed": 2026,
  "subtype_labels": [
    "Centro-Parietal-IED",
    "Frontal-IED",
    "Generalized-IED",
    "Occipital-IED",
    "Temporal-IED"
  ],
  "test_metrics": {
    "accuracy": 0.9201637666325486,
    "auprc": 0.6847588452968619,
    "auroc": 0.8793068455863041,
    "balanced_accuracy": 0.7937914036861053,
    "f1": 0.6372093023255814,
    "false_positives_per_minute": 0.6563039723661486,
    "fn": 160,
    "fp": 152,
    "n": 3908,
    "precision": 0.6431924882629108,
    "prevalence": 0.11105424769703173,
    "sensitivity": 0.631336405529954,
    "specificity": 0.9562464018422567,
    "threshold": 0.6004377007484436,
    "tn": 3322,
    "tp": 274
  },
  "validation_metrics": {
    "accuracy": 0.9502308876346844,
    "auprc": 0.6136477369244443,
    "auroc": 0.8519717080479754,
    "balanced_accuracy": 0.752188332661903,
    "f1": 0.6269230769230769,
    "false_positives_per_minute": 0.17169179229480735,
    "fn": 153,
    "fp": 41,
    "n": 3898,
    "precision": 0.7990196078431373,
    "prevalence": 0.08106721395587481,
    "sensitivity": 0.5158227848101266,
    "specificity": 0.9885538805136795,
    "threshold": 0.6004377007484436,
    "tn": 3541,
    "tp": 163
  },
  "validation_threshold_f1": 0.6004377007484436,
  "validation_threshold_sensitivity80": 0.017428677529096603,
  "window_samples": 1000,
  "window_seconds": 4.0
}