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Journal of Healthcare Engineering
Volume 2018 (2018), Article ID 3253519, 9 pages
https://doi.org/10.1155/2018/3253519
Research Article

Identification of Cerebral Artery Stenosis Using Bilateral Photoplethysmography

1Department of Neurology, Chosun University School of Medicine and Hospital, No. 375 Seosuk-dong, Dong-gu, Gwangju 501-759, Republic of Korea
2Department of Thoracic Surgery, Chosun University School of Medicine and Hospital, No. 375 Seosuk-dong, Dong-gu, Gwangju 501-759, Republic of Korea
3Department of Neurology, Chunbuk National University School of Medicine and Hospital, San 2-20, Geumam-dong, Deokjin-gu, Jeonbuk 561-180, Republic of Korea
4School of Information and Communication Engineering, Chosun University, No. 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea

Correspondence should be addressed to Youngsuk Shin; rk.ca.nusohc@nihssy

Received 29 July 2017; Accepted 24 January 2018; Published 19 March 2018

Academic Editor: Anilesh Dey

Copyright © 2018 Hyun Goo Kang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cerebral artery stenosis is currently diagnosed by transcranial Doppler (TCD), computed tomographic angiography (CTA), or magnetic resonance angiography (MRA). CTA exposes a patient to radiation, while CTA and MRA are invasive and side effects were related to contrast medium use. This study aims to provide a technique that can simply discriminate between people with normal blood vessels and those with cerebral artery stenosis using photoplethysmography (PPG), which is noninvasive and inexpensive. Moreover, the measurement takes only 120 seconds and is conducted on the fingers. The technique projects the light of a specific wavelength and analyzes the pulse waves which are generated when the blood passes through the blood vessels according to one’s heartbeat using the transmitted light. Normalization was performed after dividing the extracted pulse waveform into windows, and maximum positive and negative amplitudes (MPA, MNA) were extracted from the detected pulse waves as features. The extracted features were used to identify normal subjects and those with cerebral artery stenosis using a linear discriminant analysis. The study results showed that the recognition rate using MPA was 92.2%, MNA was 90.6%, and combined MPA + MNA was 90.6%. The technique proposed is expected to detect early stage asymptomatic cerebral artery stenosis and help prevent ischemic stroke.