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

• Published in: IEEE internet of things journal Vol. 8; no. 23; pp. 16814 - 16824
• Main Authors: Marques, Joao Alexandre Lobo, Han, Tao, Wu, Wanqing, Madeiro, Joao Paulo do Vale, Neto, Aloisio Vieira Lira, Gravina, Raffaele, Fortino, Giancarlo, de Albuquerque, Victor Hugo C.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence (AI); Artificial neural networks; Biomedical monitoring; Blood pressure; Classification; Classifiers; Cloud computing; convolutional neural networks (CNNs); Diagnostic systems; Feature extraction; Fetal heart rate; fetal monitoring; health analytics; Heart rate; Internet of Things; maternal monitoring; Medical diagnostic imaging; Monitoring; Oxygen content; Subsystems; Telemedicine
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3037759

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.