| { |
| "meta_info": { |
| "case_id": "ISRUC_01.edf", |
| "bench_subset": "NeuroBench-Sleep", |
| "difficult": 1.5, |
| "original_dataset": "ISRUC" |
| }, |
| "agent_input": { |
| "data_path": "data/sleep/ISRUC_01.edf", |
| "label_path": "data/sleep/ISRUC_01.npy", |
| "instruction": "Given the sleep file and paired sleep-stage label file, determine the specific apnea subtype occurring in each 60-second segment: A: 261-262 minutes; B: 53.75-54.75 minutes. Return one subtype for each segment using only CA, OA, or MA, where CA = Central apnea, OA = Obstructive apnea, and MA = Mixed apnea. Respiratory channels for ISRUC: X5 = snore; X6 = Flow-1 (pressure-based airflow); DC3 = Flow-2 airflow; X7 and X8 = abdominal respiratory effort." |
| }, |
| "eval_config": { |
| "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep medicine reports.\n\n### TASK\nExtract the final apnea subtype for segments A and B.\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. Do not include Markdown, explanations, or extra fields.\n3. The keys must be EXACTLY \"A\" and \"B\".\n4. Each value must be exactly \"CA\", \"OA\", \"MA\", or null.\n5. Normalize Central apnea to CA, Obstructive apnea to OA, and Mixed apnea to MA.\n6. If a segment subtype is missing or unclear, return null for that segment.\n\n### OUTPUT TEMPLATE\n{\"A\":\"CA\",\"B\":\"OA\"}", |
| "metrics": [ |
| { |
| "metric_id": "apnea_type_A_accuracy", |
| "type": "categorical_check", |
| "target_key": "A", |
| "weight": 50, |
| "params": { |
| "gt_value": "MA" |
| } |
| }, |
| { |
| "metric_id": "apnea_type_B_accuracy", |
| "type": "categorical_check", |
| "target_key": "B", |
| "weight": 50, |
| "params": { |
| "gt_value": "CA" |
| } |
| } |
| ] |
| } |
| } |
|
|
