Pulse oximeter for blood pressure estimation
Heart diseases are increasingly prominent in mortality rates. Blood pressure is an indicator for the coronary heart disease which is the prime type of heart disease. Blood pressure readings vary based on various conditions. There are multiple methods in finding the blood pressure levels. However, th...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/78326 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Heart diseases are increasingly prominent in mortality rates. Blood pressure is an indicator for the coronary heart disease which is the prime type of heart disease. Blood pressure readings vary based on various conditions. There are multiple methods in finding the blood pressure levels. However, these are often traditional method which requires medical professionals’ aid. One of which includes the pulse oximeter whereby it employs Photoplethysmogramic technique to extract multiple useful physiological parameters. Pulse oximeter operates in 2 modes – transmission and reflectance mode. Due to advancements in technology, mobile devices utilise the reflectance mode to yield Photoplethysmogramic signals (PPG) giving heart rate readings. PPG can also be implemented to estimate blood pressure readings by relating the peak values to that of pulse wave velocity, another indicator of blood pressure. However, there are no simple direct methods to extract obtained PPG signal waveforms from these mobile devices to be analysed to yield blood pressure readings. Therefore, this project synthesizes a unique image processing method to extract important features from PPG signal data originating from mobile devices. MATLAB was primarily used as the means of data processing platform. The general trend of various conditions affecting the blood pressure levels were also analysed. Lastly, this project involves itself in deriving a unique general formula relating image data information to that of blood pressure readings. The derived formula was promising as it successfully estimated blood pressure readings as validated by a blood pressure monitor with absolute difference percentage of less than 5%. |
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