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In recent years, there has been research on non-invasive blood pressure (NIBP) measurement using physiological signals. However, the main challenges are the selection of physiological signals, low complexity algorithm development and its hardware realization. This paper focuses on FPGA-based realization of a NIBP measurement model using Photoplethysmogram (PPG) signal. The PPG signal is pre-processed, beats are segmented, autoencoder features are extracted, and among 12 different regression models the most competitive model is selected to predict the systolic (SBP) and diastolic blood pressure (DBP) from PPG beat. The model was evaluated with PhysioNet MIMIC-III waveform database and achieved mean absolute error (MAE) and standard deviation (SD) of 3.69 and 5.06 mmHg respectively for SBP, and 1.96 and 3.68 mmHg for DBP respectively. The model has been implemented into Zynq-7000 SoC ZC702 standalone hardware with the lower detection latency ( \(\sim \) 6.24 ms for 1 PPG beat) reported so far in the literature and low memory requirement ( \(\sim \) 78 KB).

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FPGA-Based Implementation of Cuffless Blood Pressure Measurement Using Photoplethysmogram Signal

  • 会议论文

  • 发表日期：

    2025年

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  • D. Yudha Raja Singam,
  • Sumitra Mukhopadhyay,
  • Rajarshi Gupta

摘要

In recent years, there has been research on non-invasive blood pressure (NIBP) measurement using physiological signals. However, the main challenges are the selection of physiological signals, low complexity algorithm development and its hardware realization. This paper focuses on FPGA-based realization of a NIBP measurement model using Photoplethysmogram (PPG) signal. The PPG signal is pre-processed, beats are segmented, autoencoder features are extracted, and among 12 different regression models the most competitive model is selected to predict the systolic (SBP) and diastolic blood pressure (DBP) from PPG beat. The model was evaluated with PhysioNet MIMIC-III waveform database and achieved mean absolute error (MAE) and standard deviation (SD) of 3.69 and 5.06 mmHg respectively for SBP, and 1.96 and 3.68 mmHg for DBP respectively. The model has been implemented into Zynq-7000 SoC ZC702 standalone hardware with the lower detection latency ( \(\sim \) 6.24 ms for 1 PPG beat) reported so far in the literature and low memory requirement ( \(\sim \) 78 KB).