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Gao T, Liu S, Wang X, Liu J, Li Y, Tang X, Guo W, Han C, Fan Y. Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods. BIOMEDICAL OPTICS EXPRESS 2023; 14:4246-4260. [PMID: 37799681 PMCID: PMC10549729 DOI: 10.1364/boe.489441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/09/2023] [Accepted: 07/09/2023] [Indexed: 10/07/2023]
Abstract
Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.
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Affiliation(s)
- Tianxin Gao
- School of Medical Technology, Beijing Institute of Technology, 100081, Beijing, China
| | - Shuai Liu
- School of Medical Technology, Beijing Institute of Technology, 100081, Beijing, China
| | - Xia Wang
- Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, China
| | - Jingming Liu
- Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, China
| | - Yue Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, 100081, Beijing, China
| | - Wei Guo
- Beijing Tiantan Hospital, Capital Medical University, 100050, Beijing, China
| | - Cong Han
- Department of neurosurgery, the Fifth Medical Center of PLA General Hospital, 100071, Beijing, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, 100081, Beijing, China
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