Skip to main content

Advertisement

SpringerLink
Log in

Classification of schizophrenia using general linear model and support vector machine via fNIRS

  • Scientific Paper
  • Published:
Physical and Engineering Sciences in Medicine Aims and scope Submit manuscript

Abstract

Schizophrenia is a type of serious mental illness. In clinical practice, it is still a challenging problem to identify schizophrenia-related brain patterns due to the lack of objective physiological data support and a unified data analysis method, physicians can only use the subjective experience to distinguish schizophrenia patients and healthy people, which may easily lead to misdiagnosis. In this study, we designed an optimized data-preprocessing method accompanied with techniques of general linear model feature extraction, independent sample t-test feature selection and support vector machine to identify a set of robust fNIRS pattern features as a biomarker to discriminate schizophrenia patients and healthy people. Experimental results demonstrated that the proposed combination way of data preprocessing, feature extraction, feature selection and support vector machine classification can effectively identify schizophrenia patients and the healthy people with a leave-one-out-cross-validation classification accuracy of 89.5%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Lynall ME, Bassett DS, Kerwin R et al (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci Off J Soc Neurosci 30:9477–9487

    Article  CAS  Google Scholar 

  2. Palaniyappan L, Mallikarjun P, Joseph V et al (2011) Folding of the prefrontal cortex in schizophrenia: regional differences in gyrification. Biol Psychiatry 69:974–979

    Article  Google Scholar 

  3. Alexanderbloch AF, Vértes PE, Stidd R et al (2013) The anatomical distance of functional connections predicts brain network topology in healthy and schizophrenia. Cereb Cortex 23(1):127–138

    Article  Google Scholar 

  4. Steen RG, Mull C, Mcclure R et al (2006) Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies. Br J Psychiatry 188(6):510–518

    Article  Google Scholar 

  5. Sui J, Castro E, He H et al (2014) Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection. In: 2014 36th Annual international conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp 3889–3892

  6. Arbabshirani MR, Kiehl K, Pearlson G et al (2013) Classification of schizophrenia patients based on resting-state functional network connectivity. Front Neurosci 7:133

    Article  Google Scholar 

  7. Castro E, Gómez-Verdejo V, Martínez-Ramón M et al (2014) A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia. NeuroImage 87:1–17

    Article  Google Scholar 

  8. Kamran MA, Mannan MMN, Jeong MY (2016) Cortical signal analysis and advances in functional near-infrared spectroscopy signal: a review. Front Hum Neurosci 10:261–272

    Article  Google Scholar 

  9. Zephaniah PV, Kim JG (2014) Recent functional near infrared spectroscopy based brain computer interface systems: developments, applications and challenges. Biomed Eng Lett 4(3):223–230

    Article  Google Scholar 

  10. Obrig H, Villringer A (2003) Beyond the visible—imaging the human brain with light. J Cereb Blood Flow Metab Off J Int Soc Cereb Blood Flow Metab 23:1–18

    Article  Google Scholar 

  11. Logothetis NK, Wandell BA (2004) Interpreting the BOLD signal. Annu Rev Physiol 66:735–769

    Article  CAS  Google Scholar 

  12. Ehlis AC, Schneider S, Dresler T et al (2014) Application of functional near-infrared spectroscopy in psychiatry. Neuroimage 85:478–488

    Article  Google Scholar 

  13. Takizawa R, Kasai K, Kawakubo Y et al (2008) Reduced frontopolar activation during verbal fluency task in schizophrenia: a multi-channel near-infrared spectroscopy study. Schizophr Res 99(1–3):250–262

    Article  Google Scholar 

  14. Koike S, Nishimura Y, Takizawa R et al (2013) Near-infrared spectroscopy in schizophrenia: a possible biomarker for predicting clinical outcome and treatment response. Front Psychiatry 4:145

    Article  Google Scholar 

  15. Dadgostar M, Setarehdan SK, Shahzadi S et al (2018) Classification of schizophrenia using SVM via fNIRS. Biomed Eng Appl Basis Commun 30(02):1850008

    Article  CAS  Google Scholar 

  16. Li Z, Wang Y, Quan W et al (2015) Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy. J Neurosci Methods 241:101–110

    Article  CAS  Google Scholar 

  17. Azechi M, Iwase M, Ikezawa K et al (2010) Discriminant analysis in schizophrenia and healthy subjects using prefrontal activation during frontal lobe tasks: a near-infrared spectroscopy. Schizophr Res 117(1):52–60

    Article  Google Scholar 

  18. Song H, Bogdan IIM, Wang S et al (2016) Automatic schizophrenia discrimination on fnirs by using pca and svm. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM), IEEE, pp 389–394

  19. Swinehart DF (1962) The Beer–Lambert law. J Chem Educ 39(7):333

    Article  CAS  Google Scholar 

  20. Song H, Chen L, Gao RQ et al (2017) Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM. BMC Med Inform Decis Mak 17(3):166

    Article  Google Scholar 

  21. Kiebel SJ, Mueller K (2015) The general linear model. Brain Mapp 25:465–469

    Article  Google Scholar 

  22. Shen XP, Ding JS, Li JS (2007) 2-Independent-samples t-test undertaking by the means of mean and standard deviation in spss. Mod Prev Med 34(21):4066–4069

    Google Scholar 

  23. Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9:293–300

    Article  Google Scholar 

  24. Xuegong Z (2000) Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica 26(1):32–42

    Google Scholar 

  25. Fung GM, Mangasarian OL (2005) Multicategory proximal support vector machine classifiers. Mach Learn 59(1–2):77–97

    Article  Google Scholar 

  26. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12(6):783–789

    Article  CAS  Google Scholar 

  27. Garrett D, Peterson DA, Anderson CW, Thaut MH (2003) Comparison of linear, nonlinear, and feature selection methods for eeg signal classification. IEEE Trans Neural Syst Rehabil Eng 11(2):141–144

    Article  Google Scholar 

  28. Wang S, Gwizdka J, Chovalitwongse WA (2016) Using wireless eeg signals to assess memory workload in the n-back task. IEEE Trans Hum Mach Syst 46(3):424–435

    Article  Google Scholar 

  29. Kaper M, Meinicke P, Grossekathoefer U, Lingner T, Ritter H (2004) Bci competition 2003-data set iib: support vector machines for the p300 speller paradigm. IEEE Trans Biomed Eng 51(6):1073–1076

    Article  Google Scholar 

  30. Blankertz B, Curio G, Muller K (2002) Classifying single trial eeg: towards brain computer interfacing. Adv Neural Inf Process Syst 14(2):157–164

    Google Scholar 

  31. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:1–27. https://www.csie.ntu.edu.tw/~cjlin/libsvm/

  32. Cawley GC, Talbot NLC (2003) Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognit 36(11):2585–2592

    Article  Google Scholar 

  33. Cawley GC, Talbot NLC (2004) Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Netw 17(10):1467–1475

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (2017YFC0110700) and National Natural Science Foundation of China (61771056).

Author information

Authors and Affiliations

  1. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China

    Lei Chen, Qiang Li, Hong Song & Ruiqi Gao

  2. School of Optics and Electronics, Beijing Institute of Technology, Beijing, China

    Jian Yang

  3. Peking University Sixth Hospital, Peking University Institute of Mental Health, Key Laboratory of Mental Health, Ministry of Health (Peking University), Beijing, China

    Wentian Dong & Weimin Dang

Authors
  1. Lei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruiqi Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wentian Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Weimin Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Song.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the ethics review committee of Peking University Sixth Hospital.

Informed consent

This study was consented by all the patients.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Li, Q., Song, H. et al. Classification of schizophrenia using general linear model and support vector machine via fNIRS. Phys Eng Sci Med 43, 1151–1160 (2020). https://doi.org/10.1007/s13246-020-00920-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13246-020-00920-0

Keywords

Search

Navigation