Dr. David Siu-Yeung Cho Profile

Dr. David Siu-Yeung Cho

at Univ of Nottingham Ningbo

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 23 October 2018 Paper

Cuff-less blood pressure measurement using fingertip photoplethysmogram signals and physiological characteristics

S. Yang, Y. Zhang, Siu-Yeung Cho, Stephen Morgan, Ricardo Correia, L. Wen

Proceedings Volume 10820, 1082036 (2018) https://doi.org/10.1117/12.2502315

KEYWORDS: Artificial neural networks, Photoplethysmography, Data modeling, Databases, Feature extraction, Blood pressure, Machine learning, Electrocardiography

Read Abstract +

Blood pressure (BP) measurement data is an important indication of health and quality of life in clinical medicine and daily life. However, conventional measurement does not provide continuous monitoring data and sometimes is considered as inconvenient. With the ever increased urge for cuff-less continuous BP measurement, novel methods based on pulse transit time (PTT) obtained from the photoplethysmogram (PPG) and electrocardiogram (ECG) signals have gained its popularity. However, the collection of ECG signals involves the application of electrodes and inconvenience due to lengthy continuous measurement. In contrast, the collection of PPG signals is comparatively simpler and easier, therefore, novel methods that extract features from PPG signal are receiving more attentions. However, previous studies only focus on the features extracted from the PPG signal and did not include the physiological characteristics, which can serve as important predictors for BP. To improve the accuracy in the estimation of BP based on the PPG signal, this study not only extracts features from the PPG signal, but also includes the physiological characteristics of total 191 sets of subjects, such as height, weight, and age. After pre-processing the raw PPG signal, different machine learning methods are used to estimate the diastolic blood pressure (DBP) and the systolic blood pressure (SBP). The mean absolute error of DBP and SBP are 4.13 mmHg and 9.18 mmHg respectively. The results complied with the British Hypertension Society (BHS) standards and the implementation of physiological characteristics improved the accuracy of BP estimation.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years