RESUMO
Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.
RESUMO
The purpose of this research study was to establish ziprasidone HCl NR 40 mg and trihexyphenidyl HCl SR 4mg in the form of bi-layer sustained release floating tablets. The tablets were prepared using sodium HPMC K4M / HPMC K15M as bio-adhesive polymers and sodium bicarbonate acting as a floating layer. Tablets were evaluated based on different parameters such as thickness, hardness, friability, weight variation, in vitro dissolution studies, content of active ingredient and IR studies. The physico-chemical properties of the finished product complied with the specifications. In vitro release from the formulation was studied as per the USP XXIII dissolution procedure. The formulations gave a normal release effect followed by sustained release for 12 h which indicates bimodal release of ziprasidone HCl from the matrix tablets. The data obtained was fitted to Peppas models. Analysis of n values of the Korsmeyer equation indicated that the drug release involved non-diffusional mechanisms. By the present study, it can be concluded that bi-layer tablets of ziprasidone HCl and trihexyphenidyl HCl will be a useful strategy for extending the metabolism and improving the bioavailability of Ziprasidone HCl and Trihexyphenidyl HCl.
O propósito deste trabalho foi preparar ziprasidona. HCl NR 40 mg e triexifenidila.HCl SR 4 mg na forma de comprimidos efervescentes bicamada de liberação controlada. Os comprimidos foram preparados utilizando HPMC K4M / HPMC K15M sódico como polímero bioadesivo e bicarbonato como camada efervescente. Os comprimidos foram avaliados quanto a diferentes parâmetros, como espessura, dureza, friabilidade, variação de peso, dissolução in vitro, conteúdo do ingrediente ativo e estudos de IV. As propriedades físico-químicas dos produtos finais cumprem as especificações. A liberação in vitro da formulação foi estudada de acordo com o procedimento de dissolução da USP XXIII. As formulações resultaram em liberação normal, seguida por liberação controlada por 12 h, o que indica a liberação bimodal de cloridrato de ziprasidona dos comprimidos matriz. Os dados obtidos se adequaram aos modelos de Peppas. A análise de valores de n da equação de Korsmeyer indicou que a liberação do fármaco envolveu mecanismos não difusionais. Por este estudo, pode-se concluir que os comprimidos bicamada de ziprasidona.HCl e de triexifenidila.HCl serão um bom caminho para estender o metabolismo e para melhorar a biodisponibilidade de ziprasidona.HCl e de triexifenidila.HCl.
