BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.
Shigella flexneri is a leading etiologic agent of diarrhea in low socioeconomic countries. Notably, various serotypes in S. flexneri are reported from different regions of the world. The precise approximations of illness and death owing to shigellosis are missing in low socioeconomic countries, although it is widespread in different regions. The inadequate statistics available reveal S. flexneri to be a significant food and waterborne pathogen. All over the world, different antibiotic-resistant strains of S. flexneri serotypes have been emerged especially multidrug-resistant strains. Recently, increased resistance was observed in cephalosporins (3rd generation), azithromycin, and fluoroquinolones. There is a need for a continuous surveillance study on antibiotic resistance that will be helpful in the update of the antibiogram. The shigellosis burden can be reduced by adopting preventive measures like delivery of safe drinking water, suitable sanitation, and development of an effective and inexpensive multivalent vaccine. This review attempts to provide the recent findings of S. flexneri related to epidemiology and the emergence of multidrug resistance.
- MeSH
- antibakteriální látky farmakologie MeSH
- bacilární dyzentérie farmakoterapie mikrobiologie MeSH
- bakteriální léková rezistence MeSH
- lidé MeSH
- objevující se infekční nemoci farmakoterapie mikrobiologie MeSH
- Shigella flexneri účinky léků genetika izolace a purifikace fyziologie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
