Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural network.

Access to accurate and precise monitoring systems for cardiac arrhythmia could contribute significantly to preventing damage and subsequent heart disorders. The present research concentrates on using photoplethysmography (PPG) and arterial blood pressure (ABP) with deep convolutional neural networks (CNN) for the classification and detection of fetal cardiac arrhythmia or premature ventricular contractions (PMVCs). The framework for the study entails (Icentia 11k) a public dataset of ECG signals consisting of different cardiac abnormalities. Following this, the weights obtained from the Icentia 11k dataset are transferred to the proposed CNN. Finally, fine-tuning was carried out to improve the accuracy of classification. Results obtained showcase the capacity of the proposed method to detect and classify PMVCs into three types: Normal, P1, and P2 with an accuracy of 99.9%, 99.8%, and 99.5%.

Experimental and Theoretical Studies of Green Synthesized Cu2O Nanoparticles Using Datura Metel L.

In biomedical applications, Cu2O nanoparticles are of great interest. The bioengineered route is eco-friendly for the synthesis of nanoparticles. Therefore, in the present study, there is an attempt to synthesis Cu2O nanoparticles using Datura metel L. The synthesized nanoparticles were characterized by UV-Vis, XRD, and FT-IR. UV-Vis results suggest the presence of hyoscyamine, atropine in Datura metel L, and also, nanoparticles formation has been confirmed by the presence of absorption peak at 790 nm. The average crystallite size (19.56 nm) was obtained by XRD. FT-IR was also used to confirm the different functional groups. Fourier Power Spectrum was also employed to examine the synthesized nanomaterials spectrum data to emphasize the peak of the prominent frequencies. Density functional theory (DFT) was also utilized to assess the energy of the substance over time, which appears to indicate a stable molecule. Furthermore, calculated energies, thermodynamic properties (such as enthalpies, entropies, and Gibbs-free energies), modeled structures of complexes, crystals, and clusters, and predicted yields, rates, and regio- and stereospecificity of reactions were all in good agreement with experimental results. Overall, the results show that the successful production of Cu2O nanoparticles with Datura metel L. corresponds to theoretical research.

Smart automated heart health monitoring using photoplethysmography signal classification.

This paper proposes a smart, automated heart health-monitoring (SAHM) device using a single photoplethysmography (PPG) sensor that can monitor cardiac health. The SAHM uses an Orthogonal Matching Pursuit (OMP)-based classifier along with low-rank motion artifact removal as a pre-processing stage. Major contributions of the proposed SAHM device over existing state-of-the-art technologies include these factors: (i) the detection algorithm works with robust features extracted from a single PPG sensor; (ii) the motion compensation algorithm for the PPG signal can make the device wearable; and (iii) the real-time analysis of PPG input and sharing through the Internet. The proposed low-cost, compact and user-friendly PPG device can also be prototyped easily. The SAHM system was tested on three different datasets, and detailed performance analysis was carried out to show and prove the efficiency of the proposed algorithm.