Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

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Authors Jiri Spilka, Jordan Frecon, Roberto Leonarduzzi, Nelly Pustelnik, Patrice Abry, Muriel Doret
Journal/Conference Name IEEE Journal of Biomedical and Health Informatics
Paper Category
Paper Abstract FETAL heart rate (FHR) is routinely monitored to assist obstetricians with the evaluation of the oxygenation status of fetuses during delivery. Notably, they are used for early fetal acidosis detection and hence for taking timely and relevant decisions for operative deliveries, to prevent adverse asphyxia outcomes, such as neural development disability, neonatal encephalopathy, and cerebral palsy [1]. In daily clinical practice, FHR is mostly examined visually following guidelines edited by national and international scientific societies, such as the International Federation of Gynecology and Obstetrics (FIGO) [2], that mostly focus on decelerations and accelerations (frequency of occurrence, shape and depth, synchronization with uterine contractions), long-term variabilities, and baseline levels and trends. However, it is well known that FHR is regulated by multiple and diverse neurological feedback loops (baroreceptors, chemoreceptors), by hormones and influenced with various external factors, such as infections, resulting into complex temporal dynamics, that cannot be easily accounted for by criteria used under visual inspection. Such complexities result in large inter- and even intraobserver variabilities in FHR analysis (cf., e.g., [3]) and are also believed to be the direct cause for a high rate of cesarean sections for suspected fetal acidosis that are a posteriori found to have been unnecessary [4]. These issues have triggered numerous research efforts to enhance and automatize FHR analysis and fetal acidosis early detection.
Date of publication 2016
Code Programming Language MATLAB

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