{"id":"W2142074606","doi":"10.1109/iembs.2007.4352686","title":"BSeg++: A modified Blind Segmentation Method for Ballistocardiogram Cycle Extraction","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Cardiac cycle; Feature extraction; Computer science; SIGNAL (programming language); Motion (physics); Synchronization (alternating current); Ballistocardiography; Feature (linguistics); Computer vision; Channel (broadcasting); Medicine; Cardiology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000650358,0.001015067,0.0008919108,0.002036685,0.0004176477,0.0006998552,0.001296299,0.001280323,0.004558569],"category_scores_gemma":[0.001284796,0.0005238231,0.0006417008,0.0009043618,0.0004375669,0.0009851097,0.0007214362,0.0006645283,0.003058532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002857796,"about_ca_system_score_gemma":0.0005091441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287034,"about_ca_topic_score_gemma":0.002125344,"domain_scores_codex":[0.9994036,0.0001132982,0.00005009306,0.0001146112,0.0002796021,0.00003886064],"domain_scores_gemma":[0.9994712,0.0001831582,0.00005462315,0.00008498311,0.0001671741,0.00003892339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005584263,0.00007897978,0.000763422,0.000284109,0.00009495442,0.0001542911,0.00009818512,0.00890711,0.1625668,0.002213734,0.007029751,0.8172503],"study_design_scores_gemma":[0.0003043688,0.0005681085,0.0096203,0.00008390515,0.0001967088,0.002779744,0.00005990766,0.587373,0.2803201,0.008587124,0.1097746,0.0003320884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003285218,0.0003339065,0.9944704,0.00004129709,0.00007137533,0.00005011385,0.00008790609,0.001379117,0.0002806336],"genre_scores_gemma":[0.0194922,0.0002831109,0.9769209,0.0001080733,0.00009742504,0.0001145976,0.0003513723,0.000390328,0.002241907],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004558569,"threshold_uncertainty_score":0.01524991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05792960522442245,"score_gpt":0.393200332095383,"score_spread":0.3352707268709605,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}