SRM University

About: SRM University is a education organization based out in Chennai, India. It is known for research contribution in the topics: Computer science & Population. The organization has 10787 authors who have published 11704 publications receiving 103767 citations. The organization is also known as: Sri Ramaswamy Memorial University.

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.

Abstract: Given the patient to doctor ratio of 50,000:1 in low income and middle-income countries, there is a need for automated heart sound classification system that can screen the Phonocardiogram (PCG) records in real-time. This paper proposes deep neural network architectures such as a one-dimensional convolutional neural network (1D-CNN) and Feed-forward Neural Network (F-NN) for the classification of unsegmented phonocardiogram (PCG) signal. The research paper aims to automate the feature engineering and feature selection process used in the analysis of the PCG signal. The original PCG signal is down-sampled at 500 Hz. Then they are divided into smaller time segments of 6 s epochs. Savitzky–Golay filter is used to suppress the high-frequency noises in the signal by data point smoothening. The processed data was then provided as an input to the proposed deep neural network (DNN) architectures. 1081 PCG records were used for training and validating the proposed DNN models. The Feed-forward Neural Network model with five hidden layers provided a better overall accuracy of 0.8565 with a sensitivity of 0.8673, and specificity of 0.8475. The balanced accuracy of the model was found to be 0.8574. The performance of the model was also studied using the Receiver Operating Characteristic (ROC) plot, which produced an Area Under the Curve (AUC) value of 0.857. The classification accuracy of the proposed models was compared to the related works on PCG signal analysis for cardiovascular disease detection. The DNN models studied in this study provided comparable performance in heart sound classification without the requirement of feature engineering and segmentation of heart sound signals.

Characterization of synthesized silver nanoparticles and assessment of its genotoxicity potentials using the alkaline comet assay.

Abstract: Nano-silver (Nano-Ag) particles were synthesized and then characterized using transmission electron microscopy (TEM) and X-ray diffractometry. TEM showed that Nano-Ag were spherical in shape and their size ranged from 40 to 60nm. X-ray diffractometry indicated that the sample was crystalline and had a face centered cubic structure of pure silver. Genotoxicity of this Nano-Ag was evaluated in human peripheral blood cells using the alkaline comet assay. Results indicated that Nano-Ag (50 and 100μg/mL) caused DNA damage following a 3h treatment. Subsequently, a short treatment of 5min also showed DNA damage. In conclusion, we have shown that the synthesized Nano-Ag induced DNA damage in human peripheral blood cells as detected by the alkaline comet assay. Results further indicated that treatment of cells with Nano-Ag in the presence of hydrogen peroxide did not induce any DNA damage.