Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Patient satisfaction in tertiary private hospital in Dhaka: a case study on Square Hospital Ltd.

Vol. No.1, Issue No.2, April, 201

Students’ performance in higher education

Vol. 2, Issue No.2, July, 201

Attitudes of Parents and Children towards Multilingualism in Pakistan

This study investigated the attitudes of parents and children towards multilingualism in Faisalabad. In Faisalabad, many languages are spoken but in this study three main widely spoken languages, Urdu (national language), Punjabi (mother tongue) and English (international language) were selected. The attitudes of parents and children greatly vary towards these languages. This research took into account the variable of social status, education, age, gender and family context to probe into the reasons behind different language attitudes. This study aimed to find out how these variables play their role in shaping language attitudes. The data was collected through interview and questionnaire .The questionnaire was used to know parent’s attitudes while group interview to know children attitudes about multilingualism. Questionnaire data was analyzed by using SPSS software while interview data was analyzed qualitatively. Results depicted that children are more are conscious about English language and they have same assumptions as their parents have. On the other hand parents want their children to speak only Urdu and English

The Nexus of Foreign Direct Investment, Economic Growth and Environment in Pakistan

This paper estimate the impact of sectoral FDI on economic growth and carbon dioxide emissions in Pakistan. To this end, it uses time series secondary data from 1972 to 2011 and applies Auto Regressive Distributed Lag (ARDL) models. The results showed that FDI inflows in manufacturing, transport, storage and communication sectors and energy consumption have positive effect on the GDP growth of Pakistan. Besides, FDI inflow in manufacturing, transport, storage and communication sector and population density are responsible for the CO2 emissions in Pakistan. The results also validate Environmental Kuznet Curves in both long and short run. JEL Classification: E2, O4, Q5 Keywords: Sectoral FDI, CO2 emissions, Environmental Kuznet Curves, Gross Domestic Product Growt