Cuff-less Blood Pressure Measurement Using Supplementary ECG and PPG Features Extracted Through Wavelet Transformation

Cuff-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 transit time (PTT) based approach is commonly used, other parameters also vary with BP. 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. 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. Pulse wave analysis (PWA) is performed to create a feature vector. From the experimental results, it is found that, the SBP model gives mean error of 0.4916 mmHg, with standard deviation of 6.3986 mmHg, whereas for DBP model, mean error is 0.2527 mmHg and standard deviation is 3.2835 mmHg which is acceptable as per the British Hypertension Society (BHS) and Association for the Advancement of Medical Instruments (AAMI) standards. After the removal of BP oddities, mean absolute error improves from 5.625 to 3.854 mmHg for SBP and from 2.564 to 2.144 mmHg for DBP.