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

• Published in: Psychiatry research. Neuroimaging Vol. 332; p. 111641
• Main Authors: Aderinwale, Adedoyin, Tolossa, Gemechu Bekele, Kim, Ah Young, Jang, Eun Hye, Lee, Yong-il, Jeon, Hong Jin, Kim, Hyewon, Yu, Han Young, Jeong, Jaeseung
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2023
• Subjects: Depressive Disorder, Major - diagnosis; Diagnosis; Electroencephalography - methods; Frontal Lobe; Humans; Machine Learning; Non-linear analysis; Panic Disorder - diagnosis; Psychiatric disorders; Diagnosis; Psychiatric disorders; Non-linear analysis; Machine learning
• ISSN: 0925-4927; 1872-7506
• DOI: 10.1016/j.pscychresns.2023.111641

•Classification of panic disorder vs. major depressive disorder and controls using a 2-channel EEG system (FP1, FP2).•Panic disorder and major depressive disorder subjects show reduced cortical complexity.•Presents a rapid and practical paradigm for use in primary care settings. 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 2-channel 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.