TOWARDS AN EFFICIENT PROGNOSTIC MODEL FOR FETAL STATE ASSESSMENT

Abstract

MONITORING SIGNALS SUCH AS FETAL HEART RATE (FHR) ARE IMPORTANT INDICATORS OF FETAL WELL-BEING. COMPUTER-ASSISTED ANALYSIS OF FHR PATTERNS HAS BEEN SUCCESSFULLY USED AS A DECISION SUPPORT TOOL. HOWEVER, THE ABSENCE OF A GOLD STANDARD FOR THE BUILDING BLOCKS DECISION-MAKING IN THE SYSTEMS DESIGN PROCESS IMPAIRS THE DEVELOPMENT OF NEW SOLUTIONS. HERE WE PROPOSE A PROGNOSTIC MODEL BASED ON ADVANCED SIGNAL PROCESSING TECHNIQUES AND MACHINE LEARNING ALGORITHMS FOR THE FETAL STATE ASSESSMENT WITHIN A COMPREHENSIVE EVALUATION PROCESS. FEATURE-ENGINEERING-BASED AND TIME-SERIES-BASED MACHINE LEARNING CLASSIFIERS WERE MODELED INTO THREE DATA SEGMENTATION SCHEMAS FOR CTU-UHB, HUFA, AND DB-TRIUM DATASETS AND THE GENERALIZATION PERFORMANCE WAS ASSESSED BY A TWO-WAY CROSS-DATASET EVALUATION. IT HAS BEEN SHOWN THAT THE FEATURE-BASED ALGORITHMS OUTPERFORMED THE TIME-SERIES ONES ON DATA-LIMITED SCENARIOS. THE SUPPORT VECTOR MACHINES (SVM) OBTAINED THE BEST RESULTS ON THE DATASETS INDIVIDUALLY: SPECIFICITY (85.6% ) AND SENSITIVITY (67.5%). ON THE OTHER HAND, THE MOST EFFECTIVE GENERALIZATION RESULTS WERE ACHIEVED BY THE MULTI-LAYER PERCEPTRON (MLP) WITH A SPECIFICITY OF 71.6% AND SENSITIVITY OF 61.7%. THE OVERALL PROCESS PROVIDED A COMBINATION OF TECHNIQUES AND METHODS THAT INCREASED THE FINAL PROGNOSTIC MODEL PERFORMANCE, ACHIEVING RELEVANT RESULTS AND REQUIRING A SMALLER AMOUNT OF DATA WHEN COMPARED TO THE STATE-OF-THE-ART FETAL STATUS ASSESSMENT SOLUTIONS.