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

Comparison of Different Types of Water Melon for Their Important Nutrients

In the present study three different types (Green, light green and light green banded) of water melon was collected from local market of Peshawar and were analyzed for certain physical parameters (TSS, RI, pH, EC, and acidity) proximate composition and sugars content. The data indicated that TSS (6.90), acidity (10.08) and pH (5.79) were higher in light green banded while EC (472.33) was higher in Green type. RI (1.34) was found same in all types. Proximate showed higher values of moisture (91.93%) and crude fat (43.00 x 10 -3 %) in light green banded while ash (9.60 x 10-3 %) and crude protein (1.75 %) were higher in green. The sugar content of green was highest among all others.    It could be concluded from the data that various types of water melon along with apparent difference are also different according to their composition. So for any nutritional formulations the nutritional composition of each type should considered

Tensile strength of woven yarn kenaf fiber reinforced polyester composites

This paper presents the tensile strength of woven kenaf fiber reinforced polyester composites. The as-received yarn kenaf fiber is weaved and then aligned into specific fiber orientations before it is hardened with polyester resin. The composite plates are shaped according to the standard geometry and uni-axially loaded in order to investigate the tensile responses. Two important parameters are studied such as fiber orientations and number of layers. According to the results, it is shown that fiber orientations greatly affected the ultimate tensile strength but it is not for modulus of elasticity for both types of layers. It is estimated that the reductions of both ultimate tensile strength and Young’s modulus are in the range of 27.7-30.9% and 2.4-3.7% respectively, if the inclined fibers are used with respect to the principal axis

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.MethodsEEG single trials are decomposed with discrete wavelet transform (DWT) up to the level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.ResultsThe proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60, sensitivities 93.55, specificities 94.85, precisions 92.50, and area under the curve (AUC) 0.93 using SVM and k-NN machine learning classifiers.ConclusionThe proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds

Corporate Social Responsibility and Firms’ Financial Performance: A Conceptual Framework

This paper conceptually analyses the impact of corporate social responsibility(CSR) on firms’ financial performance. CSR is considered as an important business strategy that achieves a steady growth in firms’ profitability through improving their image. It includes all those strategies which account for are an ethical conduct and society friendly approach beneficial for the development of society. In addition to profit-maximization, the firm is also supposed to undertake activities which uplift the life of employees and the general public. In this regard, the conceptual framework of the current study shows that firms spend on awarding scholarships to needy students, health activities such as health awareness program, free medical camps, environmental protection awareness programs and sports activities. The conceptual framework alsohighlights that firms with large capital spend more on CSR activities owing to anincreased pressure of the government, public, media and other stakeholders. Based on the previous extant literature, it is also assumed that firms which earn higher profits spend more on CSR in the coming year/s that have positive impacts on their profitability. However, by following theoretical postulations, it is assumed the relationship between CSR and firms’ profitability may be endogenous. Accordingly, this study proposes dynamic GMM estimation that is a preferred technique, particularly in the presence of endogeneity

Serious Games in Science Education. A Systematic Literature Review

publishedVersio

Materials Characterization of Dissolved Chalcogenide Spin Coated Thin Films

Additive manufacturing technology introduced revolutionary new development in the field of aerospace manufacturing, medical equipment, and other industrial products. Our focus is to fabricate a space grade radiation-sensing device for space exploration application using additive manufacturing technology. Fabrication of ink is the first step of this process. We have developed both nano-particle and dissolution based chalcogenide glass ink. In this paper we report the formation of Se containing dissolution based ink dissolved in amines. Before starting printing, we studied initially spin-coated films. These films have been analyzed applying XRD, SEM, and EDS

Brain Behavior in Learning and Memory Recall Process: A High-Resolution EEG Analysis

Learning is a cognitive process, which leads to create new memory. Today, multimedia contents are common-ly used in classroom for learning. This study investigated brain physiological behavior during learning and memory process using multimedia contents and Electroencephalogram (EEG) method. Fifteen healthy subjects voluntarily participated and performed three experimental tasks: i) Intelligence task, ii) learning task, and iii) recall task. EEG was recorded duration learning and memory recall task using 128 channels Hydro Cel Geodesic Net system (EGI Inc., USA) with recommended specifications. EEG source localization showed that deep brain medial temporal region was highly activated during learning task. EEG theta band in frontal and parietal regions and gamma band at left posterior temporal and frontal regions differentiated successful memory recall. This study provides additional understanding of successful memory recall that complements earlier brain mapping studies