title: An algorithm for detecting features of the hormone profiles of the human menstrual cycle.
abstract: An algorithm for detecting features of the cycles of the gonadotropic and ovarian hormones in women is described. The algorithm can detect hormone peaks and normal cycles defined in terms of the peaks in sequences of measurements that have an arbitrary starting point in the menstrual cycle and are of arbitrary length. The algorithm makes use of fuzzy set theory and is optimized using signal detection theory.
authors.affiliation: Division of Biomedical and Behavioral Science, National Institute for Occupational Safety and Health, Cincinnati, Ohio 45226, USA. erk3@cdc.gov
authors.forename: E F
authors.initials: EF
authors.lastname: Krieg
authors.forename: J S
authors.initials: JS
authors.lastname: Kesner
authors.forename: E A
authors.initials: EA
authors.lastname: Knecht
mesh_terms: D000465:Algorithms; D004967:Estrogens / Q000378:metabolism / Q000652:urine; D005260:Female; D005640:Follicle Stimulating Hormone / Q000378:metabolism / Q000652:urine; D017143:Fuzzy Logic; D006728:Hormones / Q000378:metabolism*; D006801:Humans; D007986:Luteinizing Hormone / Q000378:metabolism / Q000652:urine; D008597:Menstrual Cycle / Q000502:physiology* / Q000652:urine; D011374:Progesterone / Q000378:metabolism / Q000652:urine; D012372:ROC Curve; D012016:Reference Values
chemical_list: D004967:Estrogens; D006728:Hormones; D011374:Progesterone; D007986:Luteinizing Hormone; D005640:Follicle Stimulating Hormone