Electrophysiological changes in poststroke subjects with depressed mood: A quantitative EEG study

Depression evaluation based on prefrontal EEG signals in resting state using fuzzy measure entropy

OBJECTIVE

Depression is a mental disorder that causes emotional changes and even suicide. However, there is still a lack of objective physiological data to support current clinical depression diagnosis. Accurate computer-aided diagnosis systems are becoming more and more crucial and urgent for future depression diagnosis. The purpose of this study is to analyze the electroencephalogram (EEG) regularity of depression using fuzzy measure entropy (FMEn), and thus to explore its role in the computer-aided diagnosis of depression.

APPROACH

Three-channel EEG signals among 35 subjects (divided into two groups according to the severity of the disease) were recorded in this study. First, the frontal delta, theta, alpha and beta frequency bands were extracted after preprocessing, and the sample entropy (SEn) and the FMEn were calculated. Then, the difference between the two groups and the correlation between the entropy values and the Hamilton Depression Rating Scale scores were analyzed using statistical analysis. Finally, the results of FMEn were compared with those of SEn.

MAIN RESULTS

A better statistically significant difference between the two groups using FMEn was revealed, with p < 0.01 in the theta and alpha bands. In terms of SEn, only SEn_Fp2 in the delta band, SEn_Fp2 in the theta band and SEn_Fp1 in the alpha band performed better, showing significant differences with p = 0.0006, p = 0.002 and p = 0.0114.

SIGNIFICANCE

These findings suggest that frontal EEG signal complexity analysis with depression using FMEn might be more sensitive than that using SEn. FMEn could be considered as a promising biomarker for future clinical depression detection.

Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.