Design and implementation of a low-cost Pre-Eclampsia detection system using image processing.

Abstract

According to research carried out by the world health organization dated 16th February 2018, every day, approximately 830 women die from preventable causes related to pregnancy and childbirth. Maternal mortality is higher in women living in rural areas and among poorer communities. Having skilled care before, during and after childbirth can save the lives of women and newborn babies. So regular prenatal care is needed so as to earlier detect and diagnose health conditions for pregnant women to save women and newborns from death. These health conditions in pregnant women include gestational diabetes, still births, preeclampsia and many more. Our project is aimed at developing a design to detect preeclampsia in rural areas. Preeclampsia is a condition that occurs only during pregnancy. It is characterized by symptoms that include: high blood pressure (>160mmHg of the systolic value) and protein in the urine (around 0.3g/L of protein in urine), occurring after week 20 (5months) of the pregnancy. Preeclampsia affects at least 5-8% of pregnancies. [14] The device designed is going to help the skilled health lab attendant and doctors to carry out prenatal care at a faster rate since the device will automatically give a diagnosis on whether the pregnant mother has preeclampsia by taking a picture of a dipstick urinalysis test strip and using the value of the blood pressure for after week 20 of her pregnancy. This will enable a doctor work on more patients since most rural health centers are crowded with patients that need prenatal care in a way saving many lives thus reducing the maternal deaths rates in rural areas. [4] Our device uses a raspberry pi to carry out its analysis which is a cheaper version of a computer. Raspberry pi works very well for rural health centers due to the fact that they require very little voltage (as low as 5V) to operate and their cost is relatively low cost fitting budgets for rural health centers. It would also enable the few doctors available and lab technicians to work faster since the rasp berry pi gives diagnosis faster than a human making the doctor to work on the many patients that come for prenatal care in away earlier detection of preeclampsia.