Multi-scale features for heartbeat classification using directed acyclic graph CNN

ABSTRACT

A new architecture of deep neural networks, directed acyclic graph convolutional neural networks (DAG-CNNs), is used to classify heartbeats from electrocardiogram (ECG) signals into different subject-based classes. DAG-CNNs not only fuse the feature extraction and classification stages of the ECG classification into a single automated learning procedure, but also utilized multi-scale features and perform score-level fusion of multiple classifiers automatically. Therefore, DAG-CNN negates the necessity to extract hand-crafted features. In most of the current approaches, only the high level features which extracted by the last layer of CNN are used. Instead of performing feature level fusion manually and feeding the results into a classifier, the proposed multi-scale system can automatically learn different level of features, combine them and predict the output label. The results over the MIT-BIH arrhythmia benchmarks database demonstrate that the proposed system achieves a superior classification performance compared to most of the state-of-the-art methods.     

Extraction and determination of mercury from spring water,beverage and rice samples using combined microwave-assisted cloud point and dispersive-solid phase extraction in micellar media

The determination of inorganic mercury species in water, beverages and foods has been a special research priority in recent years because mercury, as a highly carcinogenic species, even in trace concentration, has high adverse effects on the body of living organisms. In this research, combining two extraction techniques, including microwave-assisted cloud point extraction and dispersive-solid phase extraction (MA-CP-dSPE), for the separation and pre-concentration of mercury in environmental samples is presented. This method is based on the dispersion of nanoparticles in micellar media in the presence of a novel mono-thiosemicarbazone Schiff base (2 N′-[4-diphenylamino]benzylidene) hydrazinecarbothiohydrazide) ligand. Using the experimental conditions defined in the optimization, the method allowed mercury determination from 0.020 to 350 mg/L with a detection limit of 0.005 mg/L. The accuracy of the procedure was tested by analysing certified reference material. The method was successfully applied to copper determination in spring water, beverage and rice samples.     

Green Synthesis of Chitosan-Coated Silver Nanoparticle,Characterization, Antimicrobial Activities,and Cytotoxicity Analysis in Cancerous and Normal Cell Lines

Journal of Inorganic and Organometallic Polymers and Materials - The production of silver nanoparticles (AgNPs) using chemical synthesis routes require hazardous and toxic solvents. Nowadays, there...