Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods.

Gao, Tianxin; Zou, Chuyue; Li, Jinyu; Han, Cong; Zhang, Houdi; Li, Yue; Tang, Xiaoying; Fan, Yingwei

Gao, Tianxin; Zou, Chuyue; Li, Jinyu; Han, Cong; Zhang, Houdi; Li, Yue; Tang, Xiaoying; Fan, Yingwei.

Affiliation

Gao T; School of Life Science, Beijing Institute of Technology, Beijing, China.
Zou C; School of Life Science, Beijing Institute of Technology, Beijing, China.
Li J; School of Life Science, Beijing Institute of Technology, Beijing, China.
Han C; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China.
Zhang H; Medical School of Chinese PLA, Beijing, China.
Li Y; Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China.
Tang X; School of Medicine, Tsinghua University, Beijing, China.
Fan Y; School of Life Science, Beijing Institute of Technology, Beijing, China.

J Biophotonics ; 15(7): e202100388, 2022 07.

Article in En | MEDLINE | ID: mdl-35102703

Subject(s)

Moyamoya Disease; Cerebrovascular Circulation/physiology; Humans; Machine Learning; Oxygen; Oxygen Saturation

Key words

XGBoost; cerebral oxygen saturation; machine learning; moyamoya disease; near infrared spectroscopy; random forest; support vector machine; wavelet phase coherence

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Moyamoya Disease Type of study: Diagnostic_studies / Screening_studies Limits: Humans Language: En Journal: J Biophotonics Journal subject: BIOFISICA Year: 2022 Document type: Article Affiliation country: China Country of publication: Germany

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