The development of optimal classification criteria for specific mental disorders which share similar symptoms is an important issue for precise diagnosis. We investigated whether P300 features in both sensor-level and source-level could be effectively used to classify post-traumatic stress disorder (PTSD) and major depressive disorder (MDD).
EEG signals were recorded from fifty-one PTSD patients, 67 MDD patients, and 39 healthy controls (HCs) while performing an auditory oddball task. Amplitude and latency of P300 were evaluated, and the current source analysis of P300 components was conducted using sLORETA. Finally, we classified two groups using machine-learning methods with both sensor- and source-level features. Moreover, we checked the comorbidity effects using the same approaches (PTSD-mono diagnosis (PTSDm, n = 28) and PTSD-comorbid diagnosis (PTSDc, n = 23)).
PTSD showed significantly reduced P300 amplitudes and prolonged latency compared to HCs and MDD. Moreover, PTSD showed significantly reduced source activities, and the source activities were significantly correlated with symptoms of depression and anxiety. Also, the best classification accuracy at each pair was as follows: 80.00% (PTSD-HCs), 67.92% (MDD-HCs), 70.34% (PTSD-MDD), 82.09% (PTSDm-HCs), 71.58% (PTSDm-MDD), 82.56% (PTSDc-HCs), and 76.67% (PTSDc- MDD).
Post-traumatic stress disorder (PTSD) showed altered P300 characteristics in both sensor- and source-level compared to major depressive disorder (MDD) and healthy controls (HCs).
PTSD-comorbid diagnosis (PTSDc) group showed significantly altered P300 characteristics in both sensor- and source-level compared to MDD, whereas PTSD-mono diagnosis (PTSDm) showed a significant difference only in P300 latency (sensor-level).
The acceptable precision and recall values were obtained when discriminating PTSD from MDD and HCs using P300 features in both sensor- and source-level with machine-learning algorism, demonstrating the versatility and suitability of our approach.