IOT-BASED SMART HEALTH SYSTEM FOR AMBULATORY MATERNAL AND FETAL MONITORING

Abstract

THE ADOPTION OF IOT FOR SMART HEALTH APPLICATIONS IS A RELEVANT TOOL FOR DISTRIBUTED AND INTELLIGENT AUTOMATIC DIAGNOSTIC SYSTEMS. THIS WORK PROPOSES THE DEVELOPMENT OF AN INTEGRATED SOLUTION TO MONITOR MATERNAL AND FETAL SIGNALS FOR HIGH-RISK PREGNANCIES BASED ON IOT SENSORS, FEATURE EXTRACTION BASED ON DATA ANALYTICS, AND AN INTELLIGENT DIAGNOSTIC AID SYSTEM BASED ON A 1-D CONVOLUTIONAL NEURAL NETWORK (CNN) CLASSIFIER. THE FETAL HEART RATE AND A GROUP OF MATERNAL CLINICAL INDICATORS, SUCH AS THE UTERINE TONUS ACTIVITY, BLOOD PRESSURE, HEART RATE, TEMPERATURE, AND OXYGEN SATURATION ARE MONITORED. MULTIPLE DATA SOURCES GENERATE A SIGNIFICANT AMOUNT OF DATA IN DIFFERENT FORMATS AND RATES. AN EMERGENCY DIAGNOSTIC SUBSYSTEM IS PROPOSED BASED ON A FOG COMPUTING LAYER AND THE BEST ACCURACY WAS 92.59% FOR BOTH MATERNAL AND FETAL EMERGENCY. A SMART HEALTH ANALYTICS SYSTEM IS PROPOSED FOR MULTIPLE FEATURE EXTRACTION AND THE CALCULATION OF LINEAR AND NONLINEAR MEASURES. FINALLY, A CLASSIFICATION TECHNIQUE IS PROPOSED AS A PREDICTION SYSTEM FOR MATERNAL, FETAL, AND SIMULTANEOUS HEALTH STATUS CLASSIFICATION, CONSIDERING SIX POSSIBLE OUTPUTS. DIFFERENT CLASSIFIERS ARE EVALUATED AND A PROPOSED CNN PRESENTED THE BEST RESULTS, WITH THE F1-SCORE RANGING FROM 0.74 TO 0.91. THE RESULTS ARE VALIDATED BASED ON THE DIAGNOSIS PROVIDED BY TWO SPECIALISTS. THE RESULTS SHOW THAT THE PROPOSED SYSTEM IS A VIABLE SOLUTION FOR MATERNAL AND FETAL AMBULATORY MONITORING BASED ON IOT.