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). In this study, a machine learning framework for MCI diagnosis is proposed in this study, which extracts spectral, functional connectivity, and nonlinear features from EEG signals. The sequential backward feature selection (SBFS) algorithm is used to select the best subset of features. Several classification models and different combinations of feature sets are measured to identify the best ones for the proposed framework. A dataset of 16 and 18 EEG data of normal and MCI subjects is used to validate the proposed system. Metrics including accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) are evaluated using 10-fold crossvalidation. An average AC of 99.4%, SE of 98.8%, SP of 100%, F1 of 99.4%, and FDR of 0% have been provided by the best performance of the proposed framework using the linear support vector machine (LSVM) classifier and the combination of all feature sets. The acquired results confirm that the proposed framework provides an accurate and robust performance for recognizing MCI cases and outperforms previous approaches. Based on the obtained results, it is possible to be developed in order to use as a computer-aided diagnosis (CAD) tool for clinical purposes.

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. This study also presents an automated EEG-based MDD diagnosis framework based on Dictionary learning (DL) approaches and functional connectivity features. Firstly, a feature space of MDD and healthy control (HC) participants were constructed via functional connectivity features.Next, DL-based classification approaches such as Label Consistent K-SVD (LC-KSVD) and Correlation-based Label Consistent K-SVD (CLC-KSVD) methods, were utilized to perform the classification task. A public dataset was used, consisting of EEG signals from 34 MDD patients and 30 HC subjects, to evaluate the proposed method. To validate the proposed method, 10-fold cross-validation technique with 100 iterations was employed, providing accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR) performance metrics. The results show that LC-KSVD2 and CLC-KSVD2 performed efficiently in classifying MDD and HC cases. The best classification performance was obtained by the LCKSVD2 method, with average AC of 99.0%, SE of 98.9%, SP of 99.2%, F1 of 99.0%, and FDR of 0.8%. According to the results, the proposed method provides an accurate performance and, therefore, it can be developed into a computer-aided diagnosis (CAD) tool for automatic MDD diagnosis.

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. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework. RESULTS: The proposed method is validated with a public EEG dataset, including the EEG data of 34 MDD patients and 30 healthy subjects. The evaluation of the proposed framework is conducted using 10-fold cross-validation, providing the metrics such as accuracy (AC), sensitivity (SE), specificity (SP), F1-score (F1), and false discovery rate (FDR). The best performance of the proposed method has provided an average AC of 99%, SE of 98.4%, SP of 99.6%, F1 of 98.9%, and FDR of 0.4% using the support vector machine with RBF kernel (RBFSVM) classifier. COMPARISON WITH EXISTING METHODS: The obtained results demonstrate that the proposed method outperforms other approaches for MDD classification based on EEG signals. CONCLUSIONS: According to the obtained results, a highly accurate MDD diagnosis would be provided using the proposed method, while it can be utilized to develop a computer-aided diagnosis (CAD) tool for clinical purposes.

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. The external parts of the sperms are segmented using two convolutional neural network (CNN) models which produce the probability maps of the head and the axial filament regions. To obtain acrosome and nucleus segments, the K-means clustering approach is applied to the head segments. A Support Vector Machine (SVM) classifier is used to classify each pixel of the axial filament segments to extract tail and mid-piece regions from obtained segments. The proposed method is validated on the images of the Gold-standard dataset. It achieves 0.90, 0.77, 0.77, 0.78, 0.75 and 0.64 of the average of dice similarity coefficient for the head, axial filament, acrosome, nucleus, tail, and mid-piece segments, respectively. Experimental results demonstrate that the proposed method outperforms state-of-the-art algorithms for the head and its internal parts segmentation. It also segments the axial filament region and its internal parts with desirable accuracy. Different from previous works, the proposed method is able to segment all parts of the sperms which enables automatic quantitative analysis of the sperm morphology.