Design of Efficient Algorithms for Cuff-less and Continuous Estimation of Blood Pressure in Smart Mobile Healthcare Systems

Kachuee, Mohammad | 2016

1859 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48587 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Shabany, Mahdi; Mohammadzadeh, Hoda
  7. Abstract:
  8. Continuous Blood Pressure monitoring can provide invaluable information about individuals’ health conditions. However, BP is conventionally measured using inconvenient cuff-based instruments, which prevents continuous BP monitoring. This work presents an efficient algorithm, based on the Pulse Arrival Time (PAT), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters or whole-based representation of vital signals. Finally, the regression algorithms are employed for the BP estimation. Also, an optional calibration procedure is suggested, which can improve the system’s accuracy even further. The proposed method is evaluated on about a thousand subjects using the Association for the Advancement of Medical Instrumentation (AAMI) and the British Hypertension Society (BHS) standards. The method complies with the AAMI standard in the estimation of DBP and MAP values. Regarding the BHS protocol, the results achieve grade A for the estimation of DBP and grade B for the estimation of MAP. We conclude that by using the PAT in combination with informative features from the vital signals, the BP can be accurately and reliably estimated in a non-invasive fashion. The results indicate that the proposed algorithm can potentially enable mobile health-care gadgets to monitor the BP continuously
  9. Keywords:
  10. Blood Pressure ; Electrocardiography ; Photoplethysmography ; Machine Learning ; Blood Pressure Estimation ; Smart Mobile Healthcare

 Digital Object List


...see more