| { |
| "meta_info": { |
| "case_id": "Physionet2018_02.edf", |
| "bench_subset": "NeuroBench-Sleep", |
| "difficult": 1.5, |
| "original_dataset": "Physionet2018" |
| }, |
| "agent_input": { |
| "data_path": "data/sleep/Physionet2018_02.edf", |
| "instruction": "Please analyze the EEG signals in the following three 5-minute windows of the provided sleep file: A: 315.0-320.0 minutes; B: 296.0-301.0 minutes; C: 224.5-229.5 minutes. Select EEG channels only. Determine which window is most slow-wave-rich, and report the delta-band relative power percentage for EEG channels in each of the three windows. Define delta relative power as power in 0.5-4Hz divided by total power in 0.5-40Hz, multiplied by 100. Report the selected window using only A, B, or C, and clearly state the delta relative power percentages for A, B, and C." |
| }, |
| "eval_config": { |
| "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for sleep EEG analysis reports. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract four final outputs from the agent's report:\n1) which one of windows A, B, and C is most slow-wave-rich\n2) the delta relative power percentage for window A\n3) the delta relative power percentage for window B\n4) the delta relative power percentage for window C\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include conversational text, explanations, or units.\n4. The keys must be EXACTLY \"slow_wave_rich_window\", \"delta_ratio_A\", \"delta_ratio_B\", and \"delta_ratio_C\".\n5. \"slow_wave_rich_window\" must be exactly one of: \"A\", \"B\", \"C\", or null.\n6. Each delta ratio value must be a FLOAT percentage without a percent sign.\n7. If a report gives a fraction or proportion between 0 and 1, convert it to a percentage by multiplying by 100.\n8. If a value is missing or unclear, return null for that value.\n\n### OUTPUT TEMPLATE\n{\"slow_wave_rich_window\": <\"A\"|\"B\"|\"C\"|null>, \"delta_ratio_A\": <float|null>, \"delta_ratio_B\": <float|null>, \"delta_ratio_C\": <float|null>}", |
| "metrics": [ |
| { |
| "metric_id": "slow_wave_rich_window_accuracy", |
| "type": "categorical_check", |
| "target_key": "slow_wave_rich_window", |
| "weight": 40, |
| "params": { |
| "gt_value": "A" |
| } |
| }, |
| { |
| "metric_id": "delta_ratio_A_accuracy", |
| "type": "numeric_check", |
| "target_key": "delta_ratio_A", |
| "weight": 20, |
| "params": { |
| "gt_value": 78.04769500659657, |
| "tolerance": 3.9023847503298286 |
| } |
| }, |
| { |
| "metric_id": "delta_ratio_B_accuracy", |
| "type": "numeric_check", |
| "target_key": "delta_ratio_B", |
| "weight": 20, |
| "params": { |
| "gt_value": 75.74397205594231, |
| "tolerance": 3.7871986027971154 |
| } |
| }, |
| { |
| "metric_id": "delta_ratio_C_accuracy", |
| "type": "numeric_check", |
| "target_key": "delta_ratio_C", |
| "weight": 20, |
| "params": { |
| "gt_value": 27.43508758259991, |
| "tolerance": 1.3717543791299955 |
| } |
| } |
| ] |
| } |
| } |
|
|
