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 one-dimensional 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 format 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.

IoT-based Smart Health System for Ambulatory Maternal and Fetal Monitoring

Gravina R.;Fortino G.;
2020

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 one-dimensional 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 format 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.
Artificial Intelligence
CNNs
Feature Extraction
Fetal Monitoring
Health Analytics
Maternal Monitoring.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/311533
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