Towards Portable-Noninvasive Blood Pressure Monitoring System Utilizing the Photoplethysmogram Signal

Blood pressure (BP) responds instantly to the body's conditions, such as movements, diseases or infections, and sudden excitation. Therefore, BP monitoring is a standard clinical measurement and is considered one of the fundamental health signs that assist in predicting and diagnosing several cardiovascular diseases. The traditional BP techniques (i.e. the cuff-based methods) only provide intermittent measurements over a certain period. Additionally, they cause turbulence in the blood flow, impeding the continuous BP monitoring, especially in emergency cases. In this study, an instrumentation system is designed to estimate BP noninvasively by measuring the PPG signal utilizing the optical technique. The photoplethysmogram (PPG) signals were measured and processed for ≈ 450 cases with different clinical conditions and irrespective of their health condition. A total of 13 features of the PPG signal were used to estimate the systolic and diastolic blood pressure (SBP and DBP), utilizing several machine learning techniques. The experimental results showed that the designed system is able to effectively describe the complex-embedded relationship between the features of the PPG signal and BP (SBP and DBP) with high accuracy. The mean absolute error (MAE) ± standard deviation (SD) was 4.82 ± 3.49 mmHg for the SBP and 1.37 ± 1.65 mmHg for the DBP, with a mean error (ME) of ≈ 0 mmHg. The estimation results are consistent with the Association for the American National Standards of the Association for the Advancement of Medical Instrumentation (AAMI) and achieved Grade A in the British Hypertension Society (BHS) standards for the DBP and Grade B for the SBP. Such a study effectively contributes to the scientific efforts targeting the promotion of the practical application for providing a portable-noninvasive instrumentation system for BP monitoring purposes. Once the BP is determined with sufficient accuracy, it can be utilized further in the early prediction and classification of various arrhythmias such as hypertension, tachycardia, bradycardia, and atrial fibrillation (as the early detection can be a critical issue).