IIIT Hyderabad Publications |
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Non-Invasive Blood Pressure Estimation Using Improved Features Extracted From ECG and PPG Signals.Author: muskan.singla Date: 2020-11-19 Report no: IIIT/TH/2020/99 Advisor:Azeemuddin Syed AbstractCuff-less blood pressure (BP) is essential for continuous health monitoring to prevent diseases such as hypertension. Due to the discomfort caused by inflation and deflation of the cuff, it is not possible to monitor continuously. Although Pulse Arrival Time (PAT) derived from Electrocardiogram (ECG) and Photoplethysmogram (PPG) for cuff-less Blood Pressure (BP) measurement has been a contemporary and widely accepted technique. However, the features extracted for it are conventionally from an isolated pulse of ECG and PPG signals. As a result, the estimated BP is intermittent. Multi-parameter models are developed using regression analysis, to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). Hence, the correlation of multiple extracted features with the blood pressure is proved. To achieve this, simultaneous electrocardiogram (ECG) and photoplethysmographic (PPG) along with respective BP data were collected for 171 patients as per Association for the Advancement of Medical Instrumentation (AAMI) standards from Care Hospital, Hyderabad. During data collection, the simultaneous ECG and PPG data was collected from (Food and Drug Administration) FDA approved Vios Medical System (VMS) and BP data was collected using mercury based sphygmomanometer, manually after the required training. The developed algorithm uses wavelet transformation on ECG and PPG signals for detection of the occurrence of essential wave points precisely, even in the presence of artifacts. The feature vector of dimension 32, has been created using the fiducial points detected from ECG and PPG signals. The SBP and DBP estimation model has been obtained using regression analysis. The proposed estimation model provides the error within the range proposed within AAMI and British Hypertension Society(BHS) standards for BP estimation. In addition to this, Central BP (CBP), which is more closely associated with the cardiac activities, has also been estimated. Data for 33 patients was collected from Cath lab and CBP estimation model has been trained using PLSR regression analysis. As a resultant, fair amount of estimation accuracy has been achieved. Further, hardware design has been proposed which will simultaneously measure ECG and PPG signal and transmit the data to processing unit via bluetooth. The data received at processing unit will be processed and peripheral BP and CBP are estimated using the proposed algorithms. This continuous real-time BP monitoring technique can be useful in the treatment of hypertensive and potential-hypertensive subjects Full thesis: pdf Centre for VLSI and Embeded Systems Technology |
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