Abstract
The standard incubator used to monitor the development of preterm infants, with much attention for random optimization can interrupt the three main parameters (oxygen, environmental temperature, and humidity) responsible for preterm growth. The artificial neural network (ANN) has been recently proposed as a novel technique to control those parameters to provide a better and stabilized environment in an incubator. Unfortunately, this novel technique cannot continuously provide and indicate the update challenge of preterm growth. The objective of this paper is to apply a Markov multi-state growth process incorporates with multilayer feed-forward artificial neural network as an improved methodology to continuously control and provide an update of preterm growth in an incubator. The exchangeable Markov growth process, transition graph, and artificial neural network discussed on and applied in the designed incubator as methodology in paper and then make a joint density function of Markov multi-states growth process through multi-steps designed Algorithm to get the theoretical results. The updated measurements (weight, height, and head-perimeter) associated with controlled parameters used as input to the threshold logic unit (TLU) of ANN and then distinguish whether the growth process is abnormal or normal at each state. The summarized algorithm and multilayer feed-forward ANN utilized the panel data collected at Murunda hospital in Rwanda as input to show the application of improved methodology proposed in this paper, specifically, multi-state growth process of preterm infants across gender. As results, the continuous exchangeability of the growth process at each state has updated and may show abnormal or normal of growth process, and then sensors may notify these change through the joint density function of Markov multi-states growth process. Thus, improved methodology can increase the security and minimize time consumption in continuous monitoring growth process in an advanced way in time this idea has been implemented.
Highlights
Preterm birth is any birth before 37 completed weeks or fewer than 259 days of gestation
Artificial neural network (ANN) used to control environmental temperature, oxygen, and humidity in an incubator to provide a better environment to grow for preterm infants
The exchangeable multi-state Markov process incorporates with artificial neural network proposed as an improved methodology to continuously control and provide an update growth process of preterm infants in an incubator
Summary
Preterm birth is any birth before 37 completed weeks or fewer than 259 days of gestation. With those of less than 1000 grams need to be transferred into the incubator to optimize oxygen and energy consumption, and provide a well environmental condition regulated between 22 to 26°C without hostile effects to ensure their growth as a normal live condition [1,2,3]. The functionalities of an incubator have been improved to facilitate the rapid and safety preterm growth, and for minimizing the challenge of exchangeability of environmental temperature, which was suggested to be an issue to concern for its sensitivity with preterm infants [4]. The artificial neural network (ANN) implemented as back-propagation method was used to control the internal environment of the premature infant incubator, where Sensors were used to indicate temperature, humidity, and oxygen concentration of the incubator internal environment, and their output was entered to the ANN to identify the corresponding case and decide the proper reaction upon previous training [9]
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