Hardware Implementation of Wearable Cuff-less Blood Pressure Monitoring Module

Kiani, Mohammad Mahdi | 2016

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 48909 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Shabany, Mahdi; Mohammadzade, Hoda
  7. Abstract:
  8. Hypertension precvalence is 24 and 20.5 percent in men and women, respectively. 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), extracted from Electrocardiogram (ECG) and Photopletysmograph (PPG), for the continuous and cuff-less estimation of the Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), and Mean Arterial Pressure (MAP) values. Methods: The proposed framework estimates the BP values through processing vital signals and extracting two types of features, which are based on either physiological parameters of vital signals. Finally, the regression algorithms are employed for the BP estimation. Results: 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. For hardware implementation in this work, System on Chip (SOC) and microcontroller (µC) hardware was compared. The suggested SOC hardware designed for the purpose of Decision Tree Regression (DTR) in order to use in DTR, Adaboost or Random Forest Regressor (RFR). In result of comparison the µC method was chosen for achieving the appropriate blood pressure estimation hardware
  9. Keywords:
  10. Photoplethysmography ; Blood Pressure ; Machine Learning ; Electrocardiography ; Pulse Transit Time (PTT) ; Pulse Arrival Time (PAT)

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