The viability of a tool for fetal health monitoring

Health monitoring has become widespread these past few years. Such applications include from exercise, food intake and weight watching, to specific scenarios like monitoring people who suffer from chronic diseases. More and more we see the need to also monitor the health of new-born babies and even fetuses. Congenital Heart Defects (CHDs) are the main cause of deaths among babies and doctors do not know most of these defects. Hence, there is a need to study what causes these anomalies, and by monitoring the fetus daily there will be a better chance of identifying the defects in earlier stages. By analyzing the data collected, doctors can find patterns and come up with solutions, thus saving peoples’ lives. In many countries, the most common fetal monitor is the ultrasound and the use of it is regulated. In Sweden for normal pregnancies, there is only one ultrasound scan during the pregnancy period. There is no great evidence that ultrasound can harm the fetus, but many doctors suggest to use it as little as possible. Therefore, there is a demand for a new non-ultrasound device that can be as accurate, or even better, on detecting the FHR and not harming the baby. The problems that are discussed in this thesis include how can accurate fetus health be monitored non-invasively at home and how could a fetus health monitoring system for home use be designed. The first part of the research investigates different technologies that are currently being used on fetal monitoring, and techniques and parameters to monitor the fetus. The second part is a qualitative study held in Sweden between April and May 2016. The data for the qualitative study was collected through interviews with 21 people, 10 mothers/mothers-to-be and 11 obstetricians/gynecologists/midwives. The questions were related to the Swedish pregnancy protocol, the use of technology in medicine and in particular during the pregnancy process, and the use of an ECG based monitoring device. The results show that there is still room for improvements on the algorithms to extract the fetal ECG and the survey was very helpful in understanding the need for a fetal home monitor. Parents are open to new technologies especially if it doesn't affect the baby's growth. Doctors are open to use ECG as a great alternative to ultrasound; on the other hand, midwives are happy with the current system. The remote monitoring feature is very desirable to everyone, if such system will be used in the future.

IoT Based Pain Management using Physiological Parameters

The continuous technology evaluation is benefiting our lives to a great extent. The evolution of Internet of things and deployment of wireless sensor networks is making it possible to have more connectivity between people and devices used extensively in our daily lives. Almost every discipline of daily life including health sector, transportation, agriculture etc. is benefiting from these technologies. There is a great potential of research and refinement of health sector as the current system is very often dependent on manual evaluations conducted by the clinicians. There is no automatic system for patient health monitoring and assessment which results to incomplete and less reliable heath information. Internet of things has a great potential to benefit health care applications by automated and remote assessment, monitoring and identification of diseases. Acute pain is the main cause of people visiting to hospitals. An automatic pain detection system based on internet of things with wireless devices can make the assessment and redemption significantly more efficient. The contribution of this research work is proposing pain assessment method based on physiological parameters. The physiological parameters chosen for this study are heart rate, electrocardiography, breathing rate and galvanic skin response. As a first step, the relation between these physiological parameters and acute pain experienced by the test persons is evaluated. The electrocardiography data collected from the test persons is analyzed to extract interbeat intervals. This evaluation clearly demonstrates specific patterns and trends in these parameters as a consequence of pain. This parametric behavior is then used to assess and identify the pain intensity by implementing machine learning algorithms. Support vector machines are used for classifying these parameters influenced by different pain intensities and classification results are achieved. The classification results with good accuracy rates between two and three levels of pain intensities shows clear indication of pain and the feasibility of this pain assessment method. An improved approach on the basis of this research work can be implemented by using both physiological parameters and electromyography data of facial muscles for classification.