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PIER PIER B PIER C PIER M PIER Letters
2020-12-31
Distinguishing Bipolar Depression from Major Depressive Disorder Using fNIRS and Deep Neural Network
By
Tengfei Ma,

    Dr. Tengfei Ma

    Center for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments

    Zhejiang University

    China

    Homepage

Hailong Lyu,

    Dr. Hailong Lyu

    The Key Laboratory of Mental Disorder Management in Zhejiang Province

    China

    Homepage

Jingjing Liu,

    Dr. Jingjing Liu

    Zhejiang University School of Medicine

    China

    Homepage

Yuting Xia,

    Dr. Yuting Xia

    Center for Optical and Electromagnetic Research, National Engineering Research Center for Optical Instruments

    Zhejiang University

    China

    Homepage

Chao Qian,

    Dr. Chao Qian

    Zhejiang University School of Medicine

    China

    Homepage

Julian Evans,

    Dr. Julian Evans

    Center for Optical and Electromagnetic Research

    Zhejiang University

    China

    Homepage

Weijuan Xu,

    Dr. Weijuan Xu

    Department of Psychiatry, The First Aliated Hospital

    Zhejiang University School of Medicine

    China

    Homepage

Jianbo Hu,

    Dr. Jianbo Hu

    Department of Psychiatry, The First Aliated Hospital

    Zhejiang University School of Medicine

    China

    Homepage

Shaohua Hu,

    Dr. Shaohua Hu

    Department of Psychiatry, The First Affiliated Hospital

    Zhejiang University School of Medicine

    China

    Homepage

Sailing He

    Prof. Sailing He

    Department of Electromagnetic Theory

    Royal Institute of Technology

    Sweden

    Homepage

Progress In Electromagnetics Research, Vol. 169, 73-86, 2020
doi:10.2528/PIER20102202
Abstract
A variety of psychological scales are utilized at present as the most important basis for clinical diagnosis of mood disorders. An experienced psychiatrist assesses and diagnoses mood disorders based on clinical symptoms and relevant assessment scores. This symptom based clinical criterion is limited by the psychiatrist's experience. In practice, it is difficult to distinguish the patients with bipolar disorder with depression episode (bipolar depression, BD) from those with major depressive disorder (MDD). The functional near-infrared spectroscopy (fNIRS) technology is commonly used to perceive the emotions of a human. It measures the hemodynamic parameters of the brain, which correlate with cerebral activation. Here, we propose a machine learning classification method based on deep neural network for the brain activations of mood disorders. Large time scale connectivity is determined using an attention long short term memory neural network and short-time feature information are considered using the InceptionTime neural network in this method. Our combined method is referred to as AttentionLSTM-InceptionTime (ALSTMIT). We collected fNIRS data of 36 MDD patients and 48 BD patients who were in the depressed state. All the patients were monitored by fNIRS during conducting the verbal fluency task (VFT). We trained the model with the ALSTMIT network. The algorithm can distinguish the two types of patients effectively: the average accuracy of classification on the test set can reach 96.2% stably. The classification can provide an objective diagnosis tool for clinicians, and this algorithm may be critical for the early detection and precise treatment for the patients with mood disorders.
Citation
Tengfei Ma, Hailong Lyu, Jingjing Liu, Yuting Xia, Chao Qian, Julian Evans, Weijuan Xu, Jianbo Hu, Shaohua Hu, and Sailing He, "Distinguishing Bipolar Depression from Major Depressive Disorder Using fNIRS and Deep Neural Network," Progress In Electromagnetics Research, Vol. 169, 73-86, 2020.
doi:10.2528/PIER20102202
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