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Application of Resting Brain Frontal Lobe Complexity in Depression Screening

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Bioengineering and Biomedical Signal and Image Processing (BIOMESIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12940))

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Abstract

The increase in the number of depressed people worldwide has put forward higher requirements for higher accuracy and efficiency of depression screening. In this study, the wearable EEG devices were applied to improve screening efficiency, and the complexity attenuation rate (CAR) based on the large-scale and small-scale indexes of multivariate multiscale entropy (MMSE) were proposed, in order to improve the accuracy of screening. The EEG resting state recordings including 22 depressed patients and 20 healthy people was collected using a four-channel frontal lobe portable device. The results showed that, compared with healthy people, depressed patients had higher small-scale complexity and lower large-scale complexity, which means that depressed patients have a greater CAR. The study also verified depressed patients had lower alpha and higher beta power. Compared to other features, CAR had the highest correlation with depression scale scores. The leave-one-subject-out classification results showed that the accuracy of combined features (CAR, MMSE, multiscale entropy, and power spectral density (PSD)) reaches 88.63%, which was much higher than the traditional PSD accuracy of 79.60%. To further verify the reliability and robustness of the above results, the proposed method was verified in the public depression dataset, and the accuracy rate was increased to 87.05%. The conclusions proved that the depression screening method based on portable EEG devices proposed in this study is universal and has great practical application value.

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Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 61807007, in part by National Key Research and Development Program of China under Grant 2018YFC2001100, 2018YFB1305200.

Author information

Authors and Affiliations

  1. State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China

    Zhilin Gao, Wang Wan, Zhongze Gu & Xingran Cui

  2. Key Laboratory of Child Development and Learning Science, Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China

    Xingran Cui

  3. Institute of Biomedical Devices (Suzhou), Southeast University, Suzhou, 215000, China

    Zhongze Gu

Authors
  1. Zhilin Gao
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  2. Wang Wan
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  3. Zhongze Gu
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  4. Xingran Cui
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Corresponding author

Correspondence to Xingran Cui .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Granada, Granada, Spain

    Daniel Castillo-Secilla

  3. University of Granada, Granada, Spain

    Luis Javier Herrera

  4. Universidad de Granada, Granada, Granada, Spain

    Héctor Pomares

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Gao, Z., Wan, W., Gu, Z., Cui, X. (2021). Application of Resting Brain Frontal Lobe Complexity in Depression Screening. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-88163-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88162-7

  • Online ISBN: 978-3-030-88163-4

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