This chapter discusses the problem of continuously monitoring intraocular pressure (IOP) from an engineering perspective. It is aimed to all public in general although we think that medical staff and engineers may benefit the most from it. Although equations are included for engineers to get a glimpse of how the system works, this chapter does not go into great detail in mathematics and physics to make it understandable to medical staff. It provides though references for engineers who wish to get a better understanding of key subjects tackled in this chapter. The chapter is organized as follows: Section 1 introduces intraocular pressure (IOP) and need for its continuous monitoring. Section 2 describes the most recent efforts to develop a continuous IOP monitoring system. Section 3 shows what medical and engineering considerations must be taken into account to effectively measure IOP. Section 4 deals with health issues due to tissue warming and how to prevent them. Section 5 explains how an implant can be fabricated using either passive electronic components or active ones. Finally, Section 6 explains how the pressure sensor and the electronic circuits can be integrated.
Part of the book: Glaucoma
Monitoring of fetal cardiac activity is a well-known approach to the assessment of fetal health. The fetal heart rate can be measured using conventional cardiotocography (CTG). However, this method does not provide the beat-to-beat variability of the fetal heart rate because of the averaging nature of the autocorrelation function that is used to estimate the heart rate from a set of heart beats enclosed in the autocorrelation function window. Therefore, CTG presents important limitations for fetal arrhythmia diagnosis. CTG has a high rate of false positives and poor inter- and intra-observer reliability, such that fetal status and the perinatal outcome cannot be predicted reliably. Non-invasive fetal electrocardiography (NI-FECG) is a promising low-cost and non-invasive continuous fetal monitoring alternative. However, there is little that has been published to date on the clinical usability of NI-FECG. The chapter will include data on the accurate diagnosing of fetal distress based on heart rate variability (HRV). A fuzzy logic inference system was designed based on a set of fetal descriptors selected from the HRV responses, as evident descriptors of fetal well-being, to increase the sensitivity and specificity of detection. This approach is found to be rather prospective for the subsequent clinical implementation.
Part of the book: Non-Invasive Diagnostic Methods