Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features.

Accurate and early diagnosis of mild cognitive impairment (MCI) is necessary to prevent the progress of Alzheimer's and other kinds of dementia. Unfortunately, the symptoms of MCI are complicated and may often be misinterpreted as those associated with the normal ageing process. To address this issue, many studies have proposed application of machine learning techniques for early MCI diagnosis based on electroencephalography (EEG).

A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features.

Major depressive disorder (MDD) as a psychiatric illness negatively affects the behavior and daily life of the patients.Therefore, the early MDD diagnosis can help to cure the patients more efficiently and prevent adverse effects, although its unclear manifestations make the early diagnosis challenging. Nowadays, many studies have proposed automatic early MDD diagnosis methods based on electroencephalogram (EEG) signals.

A major depressive disorder classification framework based on EEG signals using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis.

Background: Major depressive disorder (MDD) is a prevalent mental illness that is diagnosed through questionnaire-based approaches; however, these methods may not lead to an accurate diagnosis. In this regard, many studies have focused on using electroencephalogram (EEG) signals and machine learning techniques to diagnose MDD.

New Method: This paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features.

Automatic segmentation of Sperm's parts in microscopic images of human semen smears using concatenated learning approaches.

Accurate segmentation of the sperms in microscopic semen smear images is a prerequisite step in automatic sperm morphology analysis. It is a challenging task due to the non-uniform distribution of light in semen smear images, low contrast between sperm's tail and its surrounding region, the existence of various artifacts, high concentration of sperms and wide spectrum of the shapes of the sperm's parts. This paper proposes an automatic framework based on concatenated learning approaches to segment the external and internal parts of the sperms.