{"title":"Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis","authors":"A. Dliou, S. Elouaham, R. Latif, M. Laaboubi","doi":"10.5772/intechopen.86007","DOIUrl":"https://doi.org/10.5772/intechopen.86007","url":null,"abstract":"The electrocardiogram (ECG) signal is a fundamental tool for patient treatment, especially in the cardiology domain, due to the high mortality rate of heart diseases. The main objective of this paper is to present the most optimal techniques that can link the processing and analysis of ECG signals. This work is divided into two steps. In the first one, we propose a comparison between some denoising techniques that can reduce noise affecting the ECG signals; these techniques are the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the second one, we make a comparison of three time-frequency techniques: the Choi-Williams (CW), the periodogram (PE), and the smoothed pseudo Wigner-Ville (SPWV). Firstly, the obtained results illustrate the effectiveness of the CEEMDAN in reducing noise that interferes with ECG signals compared to other denoising methods. Secondly, they show that the periodogram time-frequency technique gives a good detection and localization of the main components in the time-frequency plan of ECG signals. This work proves the utility of the combination of the periodogram and CEEMDAN techniques in analyzing the ECG signals. to analyze these biomedical signals. The obtained results show that, in the first part, the CEEMDAN presents a high effectiveness in the noise elimination and, in the second one, the periodogram provides the best solution for analyzing ECG signals. We conclude that a combination of the CEEMDAN denoising method and the PE time-frequency technique can be a good issue in analyzing the ECG signals.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132835435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Ventricular Tachycardia and Heart Failure","authors":"H. Altay","doi":"10.5772/INTECHOPEN.85256","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85256","url":null,"abstract":"Ventricular tachycardia (VT) is a common arrhythmia seen in patients with heart failure (HF) and is now seen more frequently as these patients survive longer with modern therapies. In patients with HF, half of the deaths are sudden due to life-threatening ventricular arrhythmias, including VT. Although disease modifying drugs, such as beta blockers, mineralocorticoid drugs, and angiotensin receptor neprilysin inhibitors, prevent the occurrence of VT to some extent, the mainstay of therapy is the antiarrhythmic drug therapy, implantable cardioverter-defibrillator (ICD) implantation, and traditional radiofrequency catheter ablation. Autonomic nerve system modulation and stereotactic body radiation therapy have emerged as novel techniques for the management of refractory VT cases. Patients with refractory VT and repetitive ICD shocks should be further evaluated regarding the candidacy for left ventricular assist device and transplantation.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126823067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Diagnosing Abnormal Electrocardiogram (ECG) via Deep Learning","authors":"Xin Gao","doi":"10.5772/INTECHOPEN.85509","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.85509","url":null,"abstract":"In this chapter, we investigate the most recent automatic detecting algorithms on abnormal electrocardiogram (ECG) in a variety of cardiac arrhythmias. We pres-ent typical examples of a medical case study and technical applications related to diagnosing ECG, which include (i) a recently patented data classifier on the basis of deep learning model, (ii) a deep neural network scheme to diagnose variable types of arrhythmia through wearable ECG monitoring devices, and (iii) implementation of the health cloud platform, which consists of automatic detection, data mining, and classifying via the Android terminal module. Our work establishes a cross-area study, which relates artificial intelligence (AI), deep learning, cloud computing on huge amount of data to minishape ECG monitoring devices, and portable interaction platforms. Experimental results display the technical advantages such as saving cost, better reliability, and higher accuracy of deep learning-based models in contrast to conventional schemes on cardiac diagnosis.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122629856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Visualization of ECG Data on Variant Maps","authors":"Zhihui Hou, Jeffrey Zheng","doi":"10.5772/INTECHOPEN.83552","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83552","url":null,"abstract":"This chapter presents variant maps for showing potential features in ECG data sets. The variant map is a visualization method different from a traditional ECG. In this chapter, the ECG data sets obtained by clinical ECG monitoring are used as the data source, and the corresponding variant maps are obtained by the variant statistics method. This chapter mainly introduces the variant statistics method about converting ECG data into variant maps. From sample results, various visual properties can be observed, and further explorations are required.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131967052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Characteristics of Atrial Premature Beat ECG Signals on Variant Maps","authors":"Lihua Leng, Jeffery Zheng, Jing Zhang","doi":"10.5772/INTECHOPEN.83551","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83551","url":null,"abstract":"Premature heartbeat is also known as extrasystole. It means the foundation of sinus or ectopic heart rhythm, a certain pacemaker in the heart excitable earlier than the basic rhythm, cause the heart to be local or all happening prematurely remove pole. Premature atrial beats may lead to cardiomyopathy. The experimental data in this chapter are provided by Xishan People ’ s Hospital of Wuxi city, including normal ECG signals and abnormal ECG signals (atrial premature beat). The two types of ECG data sequences are processed experimentally through the variant measurement model, and the differences in the variant maps are compared.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116198031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Genetic Polymorphisms that Playing Role in Development of Hypertrophic Cardiomyopathy","authors":"Nevra Alkanli, A. Ay","doi":"10.5772/INTECHOPEN.83473","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83473","url":null,"abstract":"Hypertrophic cardiomyopathy (HCM) is a complex heart disease with various physiopathological, morphological, functional, and clinical features. In this disease, HCM is known to be an autosomal genetic disease in more than half of the cases. Mutations in sarcomeric genes are thought to play an important role in the pathogenesis of the disease. Modifying genes and environmental factors also together affect the phenotypic expression and severity of HCM. The phenotypic expression of HCM is determined by causal sarcomeric gene mutations and the regulatory genetic basis of genes. HCM, a multi-factorial disease, involves the effects of many environmental gene modifiers and the sarcomeric/cytoskeletal genes. The single nucleotide polymorphisms occurring in the human genome differ in terms of susceptibility to disease in various populations. Therefore, the determination of genetic polymorphisms involved in the development of HCM disease is very important for the diagnosis of the disease.","PeriodicalId":290165,"journal":{"name":"Practical Applications of Electrocardiogram","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122365610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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