An Empiric Analysis of Wavelet-Based Feature Extraction on Deep Learning and Machine Learning Algorithms for Arrhythmia Classification

The aberration in human electrocardiogram (ECG) affects cardiovascular events that may lead to arrhythmias. Many automation systems for ECG classification exist, but the ambiguity to wisely employ the in-built feature extraction or expert based manual feature extraction before classification still needs recognition. The proposed work compares and presents the enactment of using machine learning and deep learning classification on time series sequences. The two classifiers, namely the Support Vector Machine (SVM) and the Bi-directional Long Short-Term Memory (BiLSTM) network, are separately trained by direct ECG samples and extracted feature vectors using multiresolution analysis of Maximal Overlap Discrete Wavelet Transform (MODWT). Single beat segmentation with R-peaks and QRS detection is also involved with 6 morphological and 12 statistical feature extraction. The two benchmark datasets, multi-class, and binary class, are acquired from the PhysioNet database. For the binary dataset, BiLSTM with direct samples and with feature extraction gives 58.1% and 80.7% testing accuracy, respectively, whereas SVM outperforms with 99.88% accuracy. For the multi-class dataset, BiLSTM classification accuracy with the direct sample and the extracted feature is 49.6% and 95.4%, whereas SVM shows 99.44%. The efficient statistical workout depicts that the extracted feature-based selection of data can deliver distinguished outcomes compared with raw ECG data or in-built automatic feature extraction. The machine learning classifiers like SVM with knowledge-based feature extraction can equally or better perform than Bi-LSTM network for certain datasets

Integrating Pre-Earthquake Signatures From Different Precursor Tools

Potential earthquake precursors include, among others, electromagnetic fields, gas emissions, Land Surface Temperature (LST), Sea Surface Temperature (SST), and Surface Air Temperature (SAT) anomalies. These observables have been individually studied, before earthquakes, by many researchers. The ionospheric studies concerning earthquakes (EQs) using magnetic data from Low Earth Orbit (LEO) satellites are increasingly being used to detect ionospheric anomalies before large EQs. Also, LST, SST, and SAT values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) are considered as physical precursors before EQs. In this work, we jointly analyze magnetic, MODIS, and MERRA-2 data in space and time around the epicenters before the selected EQs in Mexico, Japan, Chile, and Indonesia. Our analyses present interesting findings where anomalies in temperature and magnetic field, preceding the considered EQs, are confirmed through different methods. Particularly, we utilize the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT) for analyzing magnetic data over the designated EQs regions. We use the magnetic data acquired by Swarm satellites in the top side ionosphere along with MODIS and MERRA-2. Five case studies are described to prove the effectiveness of our analyses. Precursory anomalies were observed using these methods in different anomalous days from the considered four regions of interest around the epicenter. It is concluded that these methods could be effective and reliable in detecting anomalies preceding the upcoming EQs

Fast Characterization of Power Quality Events Based on Discrete Signal Processing and Data Mining

The extensive use of solid-state power electronics technology in industrial, commercial and residential equipment causes degradation of quality of electric power with the deterioration of the supply voltage. The disturbances result in degradation of the efficiency, decaying the life span of the equipment, increase in the losses, electromagnetic interference, the malfunctions of equipment and other harmful fallout. Generally, the power quality is the measurement of an ideal power supply. More over the power quality is the continuity and characteristics of the supply voltage in terms of frequency, magnitude and symmetry. The mitigation of power quality (PQ) disturbances requires detection of the source and causes of disturbances. The MODWT is a suitable method for forecasting of further occurrence of disturbance. However proper and quick detection and localization of the disturbances plays a crucial role in the power quality environment. Hence, in this thesis, a fast detection technique has been proposed along with the MODWT in order to provide time-scale representation of the signals by removing the drawback of the traditional methods like DWT and ST. Comparative analysis shows that SGWT is a best technique for localization and detection of distortions than the conventional methods. During the course of the research, it is found that suitable algorithms are required for the characterization of the disturbances for smooth mitigation of the distortions. So, data mining based classifier has been proposed for discrimination of both single and multiple disturbances. Further, the suitable features are needed for efficient characterization of the disturbances. Hence, the suitable features are extracted in order to ii reduce the number of raw data. The data normalization also plays a crucial role for efficient classification. These classification techniques are fast and able to analyze large number of disturbances. In this thesis, large numbers of signals are synthesized both in noisy and noise free environment. In the real time environment, these techniques have been performed satisfactorily. This leads to increase in the overall efficiency of the combination of the detection and classification method. In recent times, with the advancement of renewable source requires better quality of power. The important issue of the today’s distributed generation based interconnected power system is the islanding detection. Non detection zone is a good and reliable measurement of the islanding. However, failure to detect islanding situation sometimes leads to number of serious problem both for the utility and the customers. Hence, this thesis also provides a comparative analysis of the benefits and the drawbacks of aforementioned detection methods which are applied in power quality environment. The voltage signal at the PCC of the renewable distributed generation embedded with IEEE−14 bus system is captured and given as input to the analysis methods in order to extract features from the output of the analysis. The proposed SGWT properly discriminates power quality disturbances from the islanding events by introducing threshold selection. The data mining classifiers are implemented for classification of power quality as well as islanding events captured from IEEE bus system. Similar to the previous cases, the signals of same length are given to all the detection methods in ordered to compare the time of operation of each these methods. Moreover, the proposed techniques have been applied in noise free and noisy environment, bus system embedded with renewable source, real time environment etc. The overall findings of the thesis could be useful for the industrial and domestic applications. Since the detection methods are simple and faster, they could be useful for power industry and other applications such as medical science etc. Similarly, the classification can be used for application such as stock exchange, medical science etc