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Mingming Z, Wenhong C, Xiaoying M, Yang J, Liu HH, Lingli S, Hongwu M, Zhirong J. Abnormal prefrontal functional network in adult obstructive sleep apnea: A resting-state fNIRS study. J Sleep Res 2024; 33:e14033. [PMID: 37723923 DOI: 10.1111/jsr.14033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 09/20/2023]
Abstract
To assess prefrontal brain network abnormality in adults with obstructive sleep apnea (OSA), resting-state functional near infrared spectroscopy (rs-fNIRS) was used to evaluate 52 subjects, including 27 with OSA and 25 healthy controls (HC). The study found that patients with OSA had a decreased connection edge number, particularly in the connection between the right medial frontal cortex (MFG-R) and other right-hemisphere regions. Graph-based analysis also revealed that patients with OSA had a lower global efficiency, local efficiency, and clustering coefficient than the HC group. Additionally, the study found a significant positive correlation between the Montreal Cognitive Assessment (MoCA) score and both the connection edge number and the graph-based indicators in patients with OSA. These preliminary results suggest that prefrontal rs-fNIRS could be a useful tool for objectively and quantitatively assessing cognitive function impairment in patients with OSA.
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Affiliation(s)
- Zhao Mingming
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Chen Wenhong
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Mo Xiaoying
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Jianrong Yang
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Howe Hao Liu
- Physical Therapy Department, Allen College, Waterloo, Lowa, USA
| | - Shi Lingli
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Ma Hongwu
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
| | - Jiang Zhirong
- Department of Sleep Medicine, People's Hospital of Guangxi Zhuang Autonomous Region, Nan Ning, China
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Wenhong C, Xiaoying M, Lingli S, Binyun T, Yining W, Mingming Z, Yian L, Lixia Q, Wenyu H, Fengjin P. Assessing resting-state brain functional connectivity in adolescents and young adults with narcolepsy using functional near-infrared spectroscopy. Front Hum Neurosci 2024; 18:1373043. [PMID: 38606200 PMCID: PMC11007108 DOI: 10.3389/fnhum.2024.1373043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
This study aimed to elucidate the alterations in the prefrontal cortex's functional connectivity and network topology in narcolepsy patients using functional near-infrared spectroscopy (fNIRS). Twelve narcolepsy-diagnosed patients from Guangxi Zhuang Autonomous Region's People's Hospital Sleep Medicine Department and 11 matched healthy controls underwent resting fNIRS scans. Functional connectivity and graph theory analyses were employed to assess the prefrontal cortex network's properties and their correlation with clinical features. Results indicated increased functional connectivity in these adolescent and young adult patients with narcolepsy, with significant variations in metrics like average degree centrality and node efficiency, particularly in the left middle frontal gyrus. These alterations showed correlations with clinical symptoms, including depression and sleep efficiency. However, the significance of these findings was reduced post False Discovery Rate adjustment, suggesting a larger sample size is needed for validation. In conclusion, the study offers initial observations that alterations in the prefrontal cortex's functional connectivity may potentially act as a neurobiological indicator of narcolepsy, warranting further investigation with a larger cohort to substantiate these findings and understand the underlying mechanisms.
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Affiliation(s)
- Chen Wenhong
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Mo Xiaoying
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Shi Lingli
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Tang Binyun
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Wen Yining
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Zhao Mingming
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Lu Yian
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Qin Lixia
- Guangxi Clinical Reserch Center for Sleep Medicine, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Hu Wenyu
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
| | - Pan Fengjin
- Department of Sleep Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China
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Ho CSH, Wang J, Tay GWN, Ho R, Husain SF, Chiang SK, Lin H, Cheng X, Li Z, Chen N. Interpretable deep learning model for major depressive disorder assessment based on functional near-infrared spectroscopy. Asian J Psychiatr 2024; 92:103901. [PMID: 38183738 DOI: 10.1016/j.ajp.2023.103901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 12/20/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) affects a substantial number of individuals worldwide. New approaches are required to improve the diagnosis of MDD, which relies heavily on subjective reports of depression-related symptoms. AIM Establish an objective measurement and evaluation of MDD. METHODS Functional near-infrared spectroscopy (fNIRS) was used to investigate the brain activity of MDD patients and healthy controls (HCs). Leveraging a sizeable fNIRS dataset of 263 HCs and 251 patients with MDD, including mild to moderate MDD (mMDD; n = 139) and severe MDD (sMDD; n = 77), we developed an interpretable deep learning model for screening MDD and staging its severity. RESULTS The proposed deep learning model achieved an accuracy of 80.9% in diagnostic classification and 78.6% in severity staging for MDD. We discerned five channels with the most significant contribution to MDD identification through Shapley additive explanations (SHAP), located in the right medial prefrontal cortex, right dorsolateral prefrontal cortex, right superior temporal gyrus, and left posterior superior frontal cortex. The findings corresponded closely to the features of haemoglobin responses between HCs and individuals with MDD, as we obtained a good discriminative ability for MDD using cortical channels that are related to the disorder, namely the frontal and temporal cortical channels with areas under the curve of 0.78 and 0.81, respectively. CONCLUSION Our study demonstrated the potential of integrating the fNIRS system with artificial intelligence algorithms to classify and stage MDD in clinical settings using a large dataset. This approach can potentially enhance MDD assessment and provide insights for clinical diagnosis and intervention.
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Affiliation(s)
- Cyrus Su Hui Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, National University Hospital, Singapore.
| | - Jinyuan Wang
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Gabrielle Wann Nii Tay
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Roger Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, National University Hospital, Singapore; Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Syeda F Husain
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Soon Kiat Chiang
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Psychological Medicine, National University Hospital, Singapore; Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Hai Lin
- Department of Neurosurgery, Shenzhen Second People's Hospital, the First Affiliated Hospital of Shenzhen University, Shenzhen, China
| | - Xiao Cheng
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore
| | - Zhifei Li
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore; Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore; National University of Singapore (Suzhou) Research Institute, Suzhou, China
| | - Nanguang Chen
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore; National University of Singapore (Suzhou) Research Institute, Suzhou, China
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Yoo JH, Jeong H, An JH, Chung TM. Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:715. [PMID: 38276406 PMCID: PMC10818263 DOI: 10.3390/s24020715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/17/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.
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Affiliation(s)
- Joo Hun Yoo
- Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Republic of Korea;
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
| | - Harim Jeong
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
- Department of Interaction Science, Sungkyunkwan University, Seoul 03063, Republic of Korea
| | - Ji Hyun An
- Department of Psychiatry, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Tai-Myoung Chung
- Hippo T&C Inc., Suwon 16419, Republic of Korea;
- Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Eastmond C, Subedi A, De S, Intes X. Deep learning in fNIRS: a review. NEUROPHOTONICS 2022; 9:041411. [PMID: 35874933 PMCID: PMC9301871 DOI: 10.1117/1.nph.9.4.041411] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 06/22/2022] [Indexed: 05/28/2023]
Abstract
Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional near-infrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields. Aim: We aim to review the emerging DL applications in fNIRS studies. Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including brain-computer interface, neuro-impairment diagnosis, and neuroscience discovery. Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions: The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.
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Affiliation(s)
- Condell Eastmond
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Aseem Subedi
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Suvranu De
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
| | - Xavier Intes
- Center for Modeling, Simulation and Imaging for Medicine, Rensselaer Polytechnic, Department of Biomedical Engineering, Troy, New York, United States
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Mao M, Chen G, Feng K, Xu D, Hua X, Shan C, Liu P. Characteristics of prefrontal activity during emotional and cognitive processing in patients with bipolar disorder: A multi-channel functional near-infrared spectroscopy study. Front Neurosci 2022; 16:946543. [PMID: 35958997 PMCID: PMC9360599 DOI: 10.3389/fnins.2022.946543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/28/2022] [Indexed: 01/10/2023] Open
Abstract
Bipolar disorder (BD) is a recurrent chronic mental disorder with a broad profile of functional deficits including disturbed emotional processing and cognitive impairments. The goal of the current study was to further explore the underlying neural mechanism of dysfunction in patients with BD from a comprehensive perspective of both cognition and emotion. Forty-six clinical patients with BD and forty-five healthy controls performed emotion induction task and verbal fluency task (VFT), with frontal activity measured by functional near-infrared spectroscopy (fNIRS). Our results show distinct hemodynamic activity in the prefrontal region during emotional and cognitive processing between patients with BD and healthy controls. Patients with BD exhibit valence-dependent prefrontal cortex (PFC) hemodynamic response to emotional stimuli, with bilateral frontal hypoactivity indicating decreased positive reactivity and left frontal hyperactivity indicating increased negative reactivity. On the other hand, patients with BD showed impaired performance with bilateral frontal hypoactivity during VFT. Taken together, frontal dysfunction of cognition and emotionality in patients with BD probed by fNIRS would be a potential biomarker in clinical assessment.
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Affiliation(s)
- Mengchai Mao
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guifang Chen
- YuQuan Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Affiliated Hospital of Zunyi Medical University, Guizhou, China
- *Correspondence: Guifang Chen,
| | - Kun Feng
- YuQuan Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Dongsheng Xu
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xuyun Hua
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chunlei Shan
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Center of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Chunlei Shan,
| | - Pozi Liu
- YuQuan Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
- Pozi Liu,
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Brain Melody Interaction: Understanding Effects of Music on Cerebral Hemodynamic Responses. MULTIMODAL TECHNOLOGIES AND INTERACTION 2022. [DOI: 10.3390/mti6050035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Music elicits strong emotional reactions in people, regardless of their gender, age or cultural background. Understanding the effects of music on brain activity can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research. We explore the effects of three different music genres on people’s cerebral hemodynamic responses. Functional near-infrared spectroscopy (fNIRS) signals were collected from 27 participants while they listened to 12 different pieces of music. The signals were pre-processed to reflect oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) concentrations in the brain. K-nearest neighbor (KNN), random forest (RF) and a one-dimensional (1D) convolutional neural network (CNN) were used to classify the signals using music genre and subjective responses provided by the participants as labels. Results from this study show that the highest accuracy in distinguishing three music genres was achieved by deep learning models (73.4% accuracy in music genre classification and 80.5% accuracy when predicting participants’ subjective rating of emotional content of music). This study validates a strong motivation for using fNIRS signals to detect people’s emotional state while listening to music. It could also be beneficial in giving personalised music recommendations based on people’s brain activity to improve their emotional well-being.
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Hamid H, Naseer N, Nazeer H, Khan MJ, Khan RA, Shahbaz Khan U. Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22051932. [PMID: 35271077 PMCID: PMC8914987 DOI: 10.3390/s22051932] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 02/21/2022] [Accepted: 02/25/2022] [Indexed: 05/11/2023]
Abstract
This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain's left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation.
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Affiliation(s)
- Huma Hamid
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (H.H.); (H.N.)
| | - Noman Naseer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (H.H.); (H.N.)
- Correspondence:
| | - Hammad Nazeer
- Department of Mechatronics and Biomedical Engineering, Air University, Islamabad 44000, Pakistan; (H.H.); (H.N.)
| | - Muhammad Jawad Khan
- School of Mechanical and Manufacturing Engineering, National University of Science and Technology, Islamabad 44000, Pakistan;
| | - Rayyan Azam Khan
- Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada;
| | - Umar Shahbaz Khan
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan;
- National Centre of Robotics and Automation (NCRA), Rawalpindi 46000, Pakistan
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Jiao C, Xu Z, Bian Q, Forsberg E, Tan Q, Peng X, He S. Machine learning classification of origins and varieties of Tetrastigma hemsleyanum using a dual-mode microscopic hyperspectral imager. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021; 261:120054. [PMID: 34119773 DOI: 10.1016/j.saa.2021.120054] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/26/2021] [Accepted: 06/02/2021] [Indexed: 06/12/2023]
Abstract
A dual-mode microscopic hyperspectral imager (DMHI) combined with a machine learning algorithm for the purpose of classifying origins and varieties of Tetrastigma hemsleyanum (T. hemsleyanum) was developed. By switching the illumination source, the DMHI can operate in reflection imaging and fluorescence detection modes. The DMHI system has excellent performance with spatial and spectral resolutions of 27.8 μm and 3 nm, respectively. To verify the capability of the DMHI system, a series of classification experiments of T. hemsleyanum were conducted. Captured hyperspectral datasets were analyzed using principal component analysis (PCA) for dimensional reduction, and a support vector machine (SVM) model was used for classification. In reflection microscopic hyperspectral imaging (RMHI) mode, the classification accuracies of T. hemsleyanum origins and varieties were 96.3% and 97.3%, respectively, while in fluorescence microscopic hyperspectral imaging (FMHI) mode, the classification accuracies were 97.3% and 100%, respectively. Combining datasets in dual mode, excellent predictions of origin and variety were realized by the trained model, both with a 97.5% accuracy on a newly measured test set. The results show that the DMHI system is capable of T. hemsleyanum origin and variety classification, and has the potential for non-invasive detection and rapid quality assessment of various kinds of medicinal herbs.
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Affiliation(s)
- Changwei Jiao
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China
| | - Zhanpeng Xu
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.
| | - Qiuwan Bian
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China
| | - Erik Forsberg
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China
| | - Qin Tan
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China
| | - Xin Peng
- Ningbo Research Institute, Zhejiang University, Ningbo 315100, China.
| | - Sailing He
- Centre for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments, Zhejiang Provincial Key Laboratory for Sensing Technologies, Zhejiang University, Hangzhou 310058, China.
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Luo J, Zhang H, Forsberg E, Hou S, Li S, Xu Z, Chen X, Sun X, He S. Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae. OPTICS EXPRESS 2021; 29:37281-37301. [PMID: 34808804 DOI: 10.1364/oe.438253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 10/11/2021] [Indexed: 06/13/2023]
Abstract
We propose a confocal hyperspectral microscopic imager (CHMI) that can measure both transmission and fluorescent spectra of individual microalgae, as well as obtain classical transmission images and corresponding fluorescent hyperspectral images with a high signal-to-noise ratio. Thus, the system can realize precise identification, classification, and location of microalgae in a free or symbiosis state. The CHMI works in a staring state, with two imaging modes, a confocal fluorescence hyperspectral imaging (CFHI) mode and a transmission hyperspectral imaging (THI) mode. The imaging modes share the main light path, and thus obtained fluorescence and transmission hyperspectral images have point-to-point correspondence. In the CFHI mode, a confocal technology to eliminate image blurring caused by interference of axial points is included. The CHMI has excellent performance with spectral and spatial resolutions of 3 nm and 2 µm, respectively (using a 10× microscope objective magnification). To demonstrate the capacity and versatility of the CHMI, we report on demonstration experiments on four species of microalgae in free form as well as three species of jellyfish with symbiotic microalgae. In the microalgae species classification experiments, transmission and fluorescence spectra collected by the CHMI were preprocessed using principal component analysis (PCA), and a support vector machine (SVM) model or deep learning was then used for classification. The accuracy of the SVM model and deep learning method to distinguish one species of individual microalgae from another was found to be 96.25% and 98.34%, respectively. Also, the ability of the CHMI to analyze the concentration, species, and distribution differences of symbiotic microalgae in symbionts is furthermore demonstrated.
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Chao J, Zheng S, Wu H, Wang D, Zhang X, Peng H, Hu B. fNIRS Evidence for Distinguishing Patients With Major Depression and Healthy Controls. IEEE Trans Neural Syst Rehabil Eng 2021; 29:2211-2221. [PMID: 34554917 DOI: 10.1109/tnsre.2021.3115266] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuous-wave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC) and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (CBV, COE, |L | and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.
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An Advanced Spectral–Spatial Classification Framework for Hyperspectral Imagery Based on DeepLab v3+. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125703] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.
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Ma T, Wang S, Xia Y, Zhu X, Evans J, Sun Y, He S. CNN-based classification of fNIRS signals in motor imagery BCI system. J Neural Eng 2021; 18. [PMID: 33761480 DOI: 10.1088/1741-2552/abf187] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 03/24/2021] [Indexed: 11/11/2022]
Abstract
Objective. Development of a brain-computer interface (BCI) requires classification of brain neural activities to different states. Functional near-infrared spectroscopy (fNIRS) can measure the brain activities and has great potential for BCI. In recent years, a large number of classification algorithms have been proposed, in which deep learning methods, especially convolutional neural network (CNN) methods are successful. fNIRS signal has typical time series properties, we combined fNIRS data and kinds of CNN-based time series classification (TSC) methods to classify BCI task.Approach. In this study, participants were recruited for a left and right hand motor imagery experiment and the cerebral neural activities were recorded by fNIRS equipment (FOIRE-3000). TSC methods are used to distinguish the brain activities when imagining the left or right hand. We have tested the overall person, single person and overall person with single-channel classification results, and these methods achieved excellent classification results. We also compared the CNN-based TSC methods with traditional classification methods such as support vector machine.Main results. Experiments showed that the CNN-based methods have significant advantages in classification accuracy: the CNN-based methods have achieved remarkable results in the classification of left-handed and right-handed imagination tasks, reaching 98.6% accuracy on overall person, 100% accuracy on single person, and in the single-channel classification an accuracy of 80.1% has been achieved with the best-performing channel.Significance. These results suggest that using the CNN-based TSC methods can significantly improve the BCI performance and also lay the foundation for the miniaturization and portability of training rehabilitation equipment.
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Affiliation(s)
- Tengfei Ma
- Centre for Optical and Electromagnetic Research, State Key Laboratory of Modern Optical Instrumentations, Zhejiang University, Hangzhou, People's Republic of China.,Ningbo Research Institute, Zhejiang University, Ningbo 315100, People's Republic of China
| | - Shasha Wang
- Center for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, People's Republic of China
| | - Yuting Xia
- Centre for Optical and Electromagnetic Research, State Key Laboratory of Modern Optical Instrumentations, Zhejiang University, Hangzhou, People's Republic of China.,Ningbo Research Institute, Zhejiang University, Ningbo 315100, People's Republic of China
| | - Xinhua Zhu
- Ningbo Aolai Technology Ltd, Ningbo, People's Republic of China
| | - Julian Evans
- Centre for Optical and Electromagnetic Research, State Key Laboratory of Modern Optical Instrumentations, Zhejiang University, Hangzhou, People's Republic of China
| | - Yaoran Sun
- Centre for Optical and Electromagnetic Research, State Key Laboratory of Modern Optical Instrumentations, Zhejiang University, Hangzhou, People's Republic of China.,Ningbo Research Institute, Zhejiang University, Ningbo 315100, People's Republic of China
| | - Sailing He
- Centre for Optical and Electromagnetic Research, State Key Laboratory of Modern Optical Instrumentations, Zhejiang University, Hangzhou, People's Republic of China.,Ningbo Research Institute, Zhejiang University, Ningbo 315100, People's Republic of China.,Center for Optical and Electromagnetic Research, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, People's Republic of China
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