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Blood Pressure Variation Trend Analysis Based on Model Study

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Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices (ICBHI 2019)

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Hypertension is an important risk factor for stroke and cardiovascular diseases. Ambulatory blood pressure (ABP) measurement is used to estimate the continuous blood pressure. However, the cuff method ABP must have a cuff setting around the upper arm and occluding the arm’s blood circulation during the recording period, which makes some of inconveniences, including feel uncomfortable and affects the quality of sleep. The cuff-less method of ABP measurement based on the Pulse Transit Time (PTT) with electrocardiogram (ECG) and photoplethysmogram (PPG) has solved the limitation and presented potential healthcare applications. This study applies five different blood pressure regression models with the major parameter (PTT) and minor parameters (heart rate, pulse wave interval and pulse width) for estimating continuous blood pressure by regression analysis. MIMIC II clinical database is used by the correlation and consistency analysis for different blood pressure models to compare the similarity of the real and estimated blood pressure variation. The best model among the applied blood pressure models is \( \text{PTT}_{{\text{ALL}}} - \text{BP} \) that can perform the average correlation in 0.87 and the average RRratio in 0.68. The blood pressure regression model of \( \text{PTT}_{{\text{ALL}}} - \text{BP} \) provides a successful analysis model for the estimation of long-term monitoring blood pressure trend. monitor for the real and estimated blood pressure have the same trend.

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Correspondence to Pei-Ying Chen .

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Chen, PY., Ting, HJ., Chen, MF., Lin, WC., Lin, KP. (2020). Blood Pressure Variation Trend Analysis Based on Model Study. In: Lin, KP., Magjarevic, R., de Carvalho, P. (eds) Future Trends in Biomedical and Health Informatics and Cybersecurity in Medical Devices. ICBHI 2019. IFMBE Proceedings, vol 74. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30635-9

  • Online ISBN: 978-3-030-30636-6

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