Walinjkar, Amit (2019) Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision. PhD thesis, University of Essex.
Walinjkar, Amit (2019) Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision. PhD thesis, University of Essex.
Walinjkar, Amit (2019) Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision. PhD thesis, University of Essex.
Abstract
According to the British Heart Foundation, UK, there is a population of around 7 million living in the UK with heart and circulatory diseases; about 25% of all the deaths in the UK are caused by cardiovascular diseases and more than 30,000 people a year suffer cardiac arrest out-of-hospital. As people all over the world, continue to live busy and stressful lives, a vast majority of people start showing cardiac arrhythmia-related symptoms which, if not treated in time may lead to a serious heart condition or even sudden cardiac death. To identify the early-warning signs in cardiac arrhythmia, methods to identify the precursors to fatal arrhythmia were developed in this research study, using a wearable kit. To enable accurate classification between arrhythmic beats, novel feature extraction algorithms using spectral components were developed. Often a fatal cardiac arrhythmia, or a serious injury, may lead to trauma and in such situations, it becomes imperative that the critical care teams have adequate information about the patient’s health status at remote location following an ambulatory response. A real-time trauma scoring algorithm was developed, and correlation and regression analyses were performed to arrive at these scores using the physiological parameters and vital signs. It was found that with appropriate feature extraction algorithms, supervised learning classifiers could identify the precursors to arrhythmia in real time and on a resource-constrained device, regardless of time and location. The trauma scoring algorithm, implemented using ICU patients’ dataset, produced values that agreed with the patients’ status and events could be logged to electronic health records using standard clinical coding systems. It could, therefore, be concluded that regardless of situation and location of an individual, fatal arrhythmia and trauma events could be identified ahead of time before reaching a state of emergency.
| Item Type: | Thesis (PhD) |
|---|---|
| Additional Information: | Keeping your heart healthy, whatever your age, is the most important thing you can do to help prevent and manage heart disease. - The British Heart Foundation According to the British Heart Foundation, a UK based charity supporting researches aimed at cures and treatment of cardiovascular diseases, there is a population of around 7 million living in the UK with the burden of cardiovascular diseases. About 25% of all the deaths in the UK are caused by heart and circulatory diseases each year and about 175,000 people are admitted to the hospitals with a heart attack each year. The British Heart Foundation in their report also states that about 150,000 deaths due to heart and circulatory diseases over each year with an average of 420 people each day. From 2014 to 2016 it has been estimated that about 750 people per 100,000 population have premature death rate due to cardiovascular diseases. Atrial fibrillation is a heart condition that causes an irregular and often abnormally fast heart rate. In particular, about 30% of people with atrial fibrillation remain undiagnosed and therefore not being offered treatment, which increases their risk of stroke. It is a known fact that atrial fibrillation can lead to stroke and it is estimated that 5000 strokes could be prevented each year, according to National Institute of Health and Care Excellence (NICE) Surveillance Report 2017 (NICE 2017), if everyone diagnosed with atrial fibrillation could be provided with treatment in time. More than 30,000 people suffer cardiac arrest and a possible sudden cardiac death out of hospital. A significant number of patients admitted to the Accidents & Emergencies (A&E) are first time admits with fatal arrhythmia, as their arrhythmia had remained undiagnosed and had occurred while they were at work or during a commute. There is a strong evidence that premature arrhythmia can be observed in seemingly healthy individuals. The premature arrhythmia if not treated in time may manifest as fatal arrhythmia, especially the ventricular flutter or the ventricular fibrillation and may lead to cardiac arrest or sudden cardiac death if not treated in time. The research study presented in this thesis aimed at methods of identifying early warning signs of cardiac arrhythmia and continuous health monitoring of a patient and by performing trauma analysis of the patient to raise appropriate alerts and alarms to enable clinical decision making ahead of health related emergencies. |
| Uncontrolled Keywords: | Signal Processing for Early Warning Arrhythmia Detection and Survival Prediction for Clinical Decision |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RZ Other systems of medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering U Military Science > U Military Science (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases |
| Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| Depositing User: | Amit Walinjkar |
| Date Deposited: | 21 Nov 2019 12:52 |
| Last Modified: | 21 Nov 2019 12:52 |
| URI: | http://repository.essex.ac.uk/id/eprint/25989 |
Available files
Filename: ThesisCorrections9thnov.pdf