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https://www.selleckchem.com/products/kira6.html Thus, our CNN-based approach achieved comparable results to other approaches that use a single PPG signal.Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physiological recordings, models based on temporal convolutional networks and recurrent neural networks have demonstrated encouraging results and an ability to capture complex patterns and dependencies in the data. However, representations that capture the entirety of the raw signal are suboptimal as not all portions of the signal are equally important. As such, attention mechanisms are proposed to divert focus to regions of interest, reducing computational cost and enhancing accuracy. Here, we evaluate attention-based frameworks for the classification of physiological signals in different clinical domains. We evaluated our methodology on three classification scenarios neurogenerative disorders, neurological status and seizure type. We demonstrate that attention networks can outperform traditional deep learning models for sequence modelling by identifying the most relevant attributes of an input signal for decision making. This work highlights the benefits of attention-based models for analysing raw data in the field of biomedical research.Dengue fever (DF) is a viral infection with possible fatal consequence. NS1 is a recent antigen based biomarker for dengue fever (DF), as an alternative to current serum and antibody based biomarkers. Convolutional Neural Network (CNN) has demonstrated impressive performance in machine learning problems. Our previous research has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) with great potential as an early, noninvasive detection method. SERS is an enhanced variant of Raman spectroscopy, with extremely high amplification that enables spectra of low concentration matter, such as NS1 in saliva

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