Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods

被引:4
作者
Aderinwale, Adedoyin [1 ,2 ]
Tolossa, Gemechu Bekele [1 ,3 ]
Kim, Ah Young [2 ]
Jang, Eun Hye [2 ]
Lee, Yong-il [1 ]
Jeon, Hong Jin [4 ]
Kim, Hyewon [4 ]
Yu, Han Young [2 ,6 ]
Jeong, Jaeseung [5 ]
机构
[1] Korea Adv Inst Sci & Technol KAIST, Dept Bio & Brain Engn, Daejeon 34141, South Korea
[2] Elect & Telecommun Res Inst ETRI, Daejeon 34129, South Korea
[3] Washington Univ, Sch Med, Dept Neurosci, St Louis, MO 63110 USA
[4] Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Depress Ctr,Dept Psychiat, Seoul, South Korea
[5] Korea Adv Inst Sci & Technol KAIST, Dept Brain & Cognit Sci, 291 Daehak Ro, Daejeon 34141, South Korea
[6] Elect & Telecommun Res Inst ETRI, 218 Gajeong Ro, Daejeon 34129, South Korea
基金
新加坡国家研究基金会;
关键词
Psychiatric disorders; Non -linear analysis; Machine learning; Diagnosis; LEMPEL-ZIV COMPLEXITY; COGNITIVE-BEHAVIORAL THERAPY; ALZHEIMERS-DISEASE PATIENTS; WAVELET-CHAOS METHODOLOGY; GENERALIZED ANXIETY; NEUROANATOMICAL HYPOTHESIS; BIOLOGICAL SUBSTRATE; BACKGROUND ACTIVITY; LYAPUNOV EXPONENTS; EMOTION REGULATION;
D O I
10.1016/j.pscychresns.2023.111641
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.
引用
收藏
页数:10
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