A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

Vargas-Lopez, Olivia; Amezquita-Sanchez, Juan P.; De-Santiago-Perez, J. Jesus; Rivera-Guillen, Jesus Rooney; Valtierra-Rodríguez, Martín; Toledano‐Ayala, Manuel; Perez-Ramirez, Carlos A.

doi:10.3390/s20010009

Cited by 19 publications

(12 citation statements)

References 61 publications

(86 reference statements)

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“…Signal denoising is a crucial preprocessing phase to obtain the unique region of interest for each data class, i.e., ASD, VSD, and normal. Empirical mode decomposition (EMD) [ 43 , 44 , 45 ] is a widely employed method in the domain of medical signal processing for denoising [ 46 , 47 ] and feature extraction [ 48 , 49 ]. EMD reduces the given data into a collection of subcomponents called intrinsic mode functions (IMFs).…”

Section: Methodsmentioning

confidence: 99%

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Aziz

Khan

Alhaisoni

et al. 2020

Sensors

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Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

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Section: Methodsmentioning

confidence: 99%

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Aziz

Khan

Alhaisoni

et al. 2020

Sensors

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“…The basic idea of the EEMD is to use sifting to decompose different scale signals in the original signal step by step, and generate a set of IMFs with different characteristic scales and a residual term [35].…”

Section: Acquisition Of Positive Feeder Galloping Monitoring Signals and Eemd Principlementioning

confidence: 99%

Design and Data Processing of Galloping Online Monitoring System for Positive Feeder of High‐Speed Railway Catenary in Gale Area

Zhao

Zhang

et al. 2021

IEEJ Transactions Elec Engng

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To monitor the galloping state of the catenary positive feeder of the Lanzhou–Urumqi high‐speed railway in the gale area, on the basis of previous research results, an online multi‐point distributed galloping monitoring system based on wireless sensor network (WSN) was designed, and the author improves the design of each part of the catenary positive feeder galloping monitoring system to enhance its availability and reliability. By the system to collect accelerations of each galloping monitoring point, and the displacement is obtained by integrating the acceleration twice, but the integration result was seriously distorted because of the trends. A localization algorithm is proposed to correct the galloping signal waveform. Firstly, in accordance with a detailed analysis of ensemble empirical mode decomposition (EEMD) anti‐modal aliasing principle and the steps of the waveform correction method, the original signal was decomposed to obtain ten intrinsic modal functions (IMFs). Secondly, with the IMFs' characteristics as basis, a new method of trend extraction was introduced to extract and eliminate the trends in the original signal. Finally, through the analysis of the measured signal of wire galloping, the feasibility and effectiveness of the proposed method were verified by comparing its results with those of the moving average method, and the galloping trajectory of the wire is obtained by curve fitting. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…The IoT in medical care present another relevant publications in this community [44]. A line of research is the monitoring of physiological signals [45], among which the processing of electrocardiograms (ECG) stands out, developing finite response models (FIR) to correct discretely acquired ECGs and methods that allow for recording by Smartphone [46], for the discrimination of possible atrial fibrillations in patients. The subject's behavior, position and movement represent a relevant source of information, which allows for the development of joint models for medical study, or the sports activity of an athlete by monitoring their physiological parameters [47].…”

Section: Community Analysismentioning

confidence: 99%

Electronics and Its Worldwide Research

et al. 2020

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The contributions of researchers at a global level in the journal Electronics in the period 2012–2020 are analyzed. The objective of this work is to establish a global vision of the issues published in the Electronic magazine and their importance, advances and developments that have been particularly relevant for subsequent research. The magazine has 15 thematic sections and a general one, with the programming of 385 special issues for 2020–2021. Using the Scopus database and bibliometric techniques, 2310 documents are obtained and distributed in 14 thematic communities. The communities that contribute to the greatest number of works are Power Electronics (20.13%), Embedded Computer Systems (13.59%) and Internet of Things and Machine Learning Systems (8.11%). A study of the publications by authors, affiliations, countries as well as the H index was undertaken. The 7561 authors analyzed are distributed in 87 countries, with China being the country of the majority (2407 authors), followed by South Korea (763 authors). The H-index of most authors (75.89%) ranges from 0 to 9, where the authors with the highest H-Index are from the United States, Denmark, Italy and India. The main publication format is the article (92.16%) and the review (5.84%). The magazine publishes topics in continuous development that will be further investigated and published in the near future in fields as varied as the transport sector, energy systems, the development of new broadband semiconductors, new modulation and control techniques, and more.

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A New Methodology Based on EMD and Nonlinear Measurements for Sudden Cardiac Death Detection

Cited by 19 publications

References 61 publications

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Design and Data Processing of Galloping Online Monitoring System for Positive Feeder of High‐Speed Railway Catenary in Gale Area

Electronics and Its Worldwide Research

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