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This paper presents an ECG-based human identification that aims to increase the prediction accuracy with a minimized training dataset using a Block Dense CNN (BDCNN) architecture. This study specifically examines the effect of increasing the number of individuals, while decreasing the amount of training data on the performance of the network. Furthermore, the performance of the proposed network with multisession ECG recordings is also investigated. Comprehensive experimentation on nine ECG public databases that include normal and various abnormal ECG beats has been carried out. Performance of the network is evaluated using metrics such as prediction accuracy, false rejection ratio (FRR), false acceptance ratio (FAR), computational complexity, and training time, and the results are then compared to the results of a residual network. Experimental results show that, not only in a single session but also in multisession ECG recordings, the proposed network proficiently overbears the load of increased persons with decreased training dataset with about a 5.06% increase in identification rate as compared to its close counterpart. Therefore, the proposed BDCNN can be a potential framework for ECG-based human identification systems. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMRI) segmentation transforms cardiac MR images into semantic regions to define the left ventricle cavity, right ventricle cavity, and myocardium. CMRI segmentation provides ventricles' volume, mass, and ejection fraction, playing a significant role in cardiac disease diagnosis. This paper aims to propose novel deep supervision in U-Net-based architecture to enhance the segmentation performance. It presents a deeply supervised W-Net, which creates another path in parallel with the decoder path in U-Net-based architecture. The output of every upsampling layer in the decoder path is combined with pixel-wise addition for feature reuse, and loss is computed at each feature dimension on the deep supervision layer, which enables gradients to be implanted at a greater depth into the network and enhances the training of all layers in the network. Proposed W-Net applied on single scanner-based ACDC dataset and Multi-Centre, Multi-Vendor & Multi-Disease dataset, making it more robust in model generalization. W-Net significantly outperforms numerous state-of-the-art methods on the two publicly available CMRI datasets, according to experiments conducted. W-Net obtained better segmentation results ranked in the top three for many metrics. It is evident that the proposed W-Net has considerable potential in CMRI segmentation, cardiac assessment, and disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper deals with new approaches to analyse electrocardiogram (ECG) signals for extracting useful diagnostic features. Initially, elimination of different types of noise is carried out using maximal overlap discrete wavelet transform (MODWT) and universal thresholding. Next, R-peak fiducial points are detected from these noise free ECG signals using discrete wavelet transform along with thresholding. Then, extraction of other features, viz., Q waves, S waves, P waves, T waves, P wave onset and offset points, T wave onset and offset points, QRS onset and offset points are identified using some rule based algorithms. Eventually, other important features are computed using the above extracted features. The software developed for this purpose has been validated by extensive testing of ECG signals acquired from the MIT-BIH database. The resulting signals and tabular results illustrate the performance of the proposed method. The sensitivity, predictivity and error of beat detection are 99.98%, 99.97% and 0.05%, respectively. The performance of the proposed beat detection method is compared to other existing techniques, which shows that the proposed method is superior to other methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper emphasized on Microwave-assisted Green Synthesis of ZnO nanoparticles. • This method has perfect control over particle size and morphology. • Aegle marmelos act as stabilizer and reducing agent. • The average particle size was found to be in the range of 18–28 nm. • The synthesized ZnO NPs are excellent in degrading dye to 80% only in one hour. Microwave-assisted green method was successfully used to synthesize ZnO nanoparticles in Nanoscale range by varying the Aegle marmelos leaf extract concentration. The existence of functional groups on the nanoparticle surface was confirmed by the FTIR spectra, which displayed distinctive vibrational modes. The nanoparticles size, shape, and surface features were discovered through morphological and structural research utilizing HRTEM, and SEM. The average particle size was found to be in the range of 18–28 nm. Under UV light irradiation, the ZnO NPs exhibited promising photocatalytic activity in the breakdown of methylene blue dye. The synthesized ZnO NPs are excellent in degrading dye to 80 % only in one hour. Furthermore, both Gram-positive and Gram-negative bacterial strains were effectively inhibited by the synthesized ZnO NPs' high antibacterial activity. Thus, characterization and promising photocatalytic and antibacterial properties point to the potential of these ZnO NPs for use in a range of environmental and biological applications. [ABSTRACT FROM AUTHOR]