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Bioelectrical Signal Processing In Cardiac And Neurological Applications

Detection of abnormal heart conditions based on characteristics of ECG signals

A novel two-dimensional ECG feature extraction and classification algorithm based on convolution neural network for human authentication

Fake news, hostility, defamation are some of the biggest problems faced in social media. We present the findings of the shared tasks (https://constraint-shared-task-2021.github.io/) conducted at the CONSTRAINT Workshop at AAAI 2021. The shared tasks are ‘COVID19 Fake News Detection in English’ and ‘Hostile Post Detection in Hindi’. The tasks attracted 166 and 44 team submissions respectively. The most successful models were BERT or its variations.

Overview of CONSTRAINT 2021 Shared Tasks: Detecting English COVID-19 Fake News and Hindi Hostile Posts

In this paper, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning(DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia. This examination encourages us to recognize diverse kinds of arrhythmia utilizing Deep Learning algorithm. Here we use Convolutional Neural Network(CNN) a DL algorithm which is efficient in classifying signals. Utilizing CNN, features are learned Automatically from the time domain ECG signals which are acquired from MIT-BIH Database from Physiobank.com. The feature adapted specifically replaces manually extracted features and this analysis will help the Cardiologists in screening the patient with Cardiac illness effectively. The CNN is trained, tested using ECG Dataset obtained from MIT-BIH Database and from the signal 7 of arrhythmia were classified. The proposed system is compared for Various Activation function by varying the number of epochs. From the result obtained we came to know that ELU activation function gives better result with an accuracy of 93.6% and with a loss of 0.2.

P. Prabaharan

Papers

DHOT-Repository and Classification of Offensive Tweets in the Hindi Language

Total Variation Denoising Based Approach for R-peak Detection in ECG Signals

Text/Image Region Separation for Document Layout Detection of Old Document Images Using Non-linear Diffusion and Level Set☆