In detecting brain activity and behavior, the Electroencephalogram (EEG) plays a key role. The EEG signals recorded are almost always corrupted by artifacts and hence affect the EEG signal analysis. Therefore, it is highly essential to devise techniques to extract noise-free EEG data from the recorded EEG signals. The performance efficiency of two variation denoising methods: Simultaneous Low-Pass Filtering/Total Variation Denoising(LPF/TVD) and Transient Artifact Reduction Algorithm (TARA) in artifact removal from Schizophrenia (SZ) patient's and healthy adolescent's EEG signals are being evaluated. The artifact removal technique of TARA is a modified genre of Simultaneous Low-Pass Filtering and Total Variation Denoising (LPF/TVD). It has been observed that TARA performs well in denoising the signals from a group of 45 SZ and 39 healthy control EEG signals. The efficiency of the methods is evaluated using the index of Signal to noise ratio (SNR). A high SNR value for TARA shows the efficiency of this method in removing the artifacts from SZ EEG signals.

Alzheimer's disease (AD) is a prevalent neurodegenerative form of dementia that progressively affects individuals. Alterations in the fornix region are significant biomarkers of AD. In this work, an approach is proposed to distinguish mild cognitive impairment (MCI) from normal cognition (NC) and AD subjects using structural magnetic resonance (sMR) images and rotational invariant moments (RIMs). Initially, the sMR images are preprocessed using a standard pipeline and the fornix structure is segmented using the reaction-diffusion level set (RDLS) algorithm. Then, a total of 11 RIMs are computed from the fornix region. Further, the significant features are identified using statistical tests: Shapiro-Wilk, Wilcoxon's rank-sum and ANOVA. The results indicate that RDLS effectively segments the fornix structure. The RIM features demonstrate high statistical significance (p < 0.01) in differentiating MCI from NC and AD, highlighting their ability to capture shape variations in the fornix. The proposed approach effectively differentiates MCI from NC and AD subjects, suggesting its clinical relevance in Alzheimer's condition analysis.

Alzheimer's disease (AD) is a degenerative disorder of the brain that affects elderly individuals, leading to cognitive decline and memory loss. Mild cognitive impairment (MCI) is a transition stage between normal cognition (NC) and AD. Early detection of MCI is crucial since it allows for timely intervention to delay AD progression. The onset of AD is associated with tissue alterations in the fornix, a white matter region of the brain responsible for cognition, learning, memory consolidation, and attention. In this study, fornix morphometrics in MCI and AD are characterized using structural magnetic resonance (sMR) brain images and pseudo-Zernike moment (PZM) features. For this study, a publicly available database is used. Initially, a standard pipeline is used to pre-process the sMR brain images, followed by segmentation of the fornix structure using the level set without re-initialization (LSWR) algorithm. Subsequently, 64 PZMs are computed from the fornix region. Statistical tests such as the Kolmogorov-Smirnov test, Student's t-test, Wilcoxon-Mann-Whitney test, and one-way ANOVA are employed to identify significant features and machine learning algorithms also performed for binary classification. The outcomes revealed that the LSWR algorithm segmented the fornix structure at an accuracy of 99%. The PZM features exhibited statistical significance (p < 0.05) in distinguishing MCI and AD, emphasizing their effectiveness in capturing fornix shape variations. The proposed approach employed in this study emphasizes the clinical relevance in differentiating MCI from NC and AD subjects.