Classification of Arrhythmia from ECG Signals using MATLAB

An Electrocardiogram (ECG) is defined as a test that is performed on the heart to detect any abnormalities in the cardiac cycle. Automatic classification of ECG has evolved as an emerging tool in medical diagnosis for effective treatments. The work proposed in this paper has been implemented using MATLAB. In this paper, we have proposed an efficient method to classify the ECG into normal and abnormal as well as classify the various abnormalities. To brief it, after the collection and filtering the ECG signal, morphological and dynamic features from the signal were obtained which was followed by two step classification method based on the traits and characteristic evaluation. ECG signals in this work are collected from MIT-BIH, AHA, ESC, UCI databases. In addition to this, this paper also provides a comparative study of various methods proposed via different techniques. The proposed technique used helped us process, analyze and classify the ECG signals with an accuracy of 97% and with good convenience

Digitization and analysis of capnography using image processing technique

The study of carbon dioxide expiration is called capnometry. The graphical representation of capnometry is called capnography. There is a growing interest in the usage of capnography as the usage has expanded toward the study of metabolism, circulation, lung perfusion and diffusion, quality of spontaneous respiration, and patency of airways outside of its typical usage in the anesthetic and emergency medicine field. The parameters of the capnograph could be classified as carbon dioxide (CO2) concentration and time points and coordinates, slopes angle, volumetric studies, and functional transformation of wave data. Up to date, there is no gold standard device for the calculation of the capnographic parameters. Capnography digitization using the image processing technique could serve as an option. From the algorithm we developed, eight identical breath waves were tested by four investigators. The values of the parameters chosen showed no significant difference between investigators. Although there were no significant differences between any of the parameters tested, there were a few related parameters that were not calculable. Further testing after refinement of the algorithm could be done. As more capnographic parameters are being derived and rediscovered by clinicians and researchers alike for both lung and non-lung-related diseases, there is a dire need for data analysis and interpretation. Although the proposed algorithm still needs minor refinements and further large-scale testing, we proposed that the digitization of the capnograph via image processing technique could serve as an intellectual option as it is fast, convenient, easy to use, and efficient

Extração de sinal digital de ECG utilizando técnicas de processamento de imagens

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, Engenharia Eletrônica, 2021.Segundo a Organização Mundial da Saúde (OMS), as doenças cardiovasculares são as principais causas de morte no mundo. O diagnóstico de forma rápida e precisa dessas doenças é de grande importância no tratamento dos pacientes e a análise do exame de eletrocardiograma (ECG), desde sua invenção, é uma das ferramentas mais utilizadas para a realização desse diagnóstico. Além disso, para fins de consulta, esses registros precisam ser acessados de tempos em tempos por especialistas. Entretanto, a maioria dos exames de ECG existentes ainda está disponível somente no formato impresso, o que dificulta a preservação, a análise e o compartilhamento das informações clínicas dos pacientes. A criação de uma ferramenta capaz de obter o sinal do ECG a partir de uma imagem digital seria de muita utilidade para clínicas de saúde e hospitais. Dito isto, este trabalho propõe o desenvolvimento de uma ferramenta computacional capaz de extrair o sinal digital a partir de imagens digitais contendo as derivações do ECG, desenvolvida em Python, com o auxílio de bibliotecas abertas como OpenCV, SciPy e Pandas e técnicas de processamento digital de imagens. O objetivo geral deste trabalho é a obtenção de um sinal digital unidimensional, contendo tempo e amplitude, com base em imagens digitalizadas das derivações do ECG. Também propõe-se uma forma de identificar os complexos QRS, e consequentemente a frequência cardíaca do indivíduo, utilizando uma versão modificada do algoritmo de Pan-Tompkins. Os testes para validação foram realizados num total de 180 imagens obtidas na base online PTB Diagnostic ECG Database através da ferramenta online PhysioBank ATM. O algoritmo proposto obteve coeficiente de correlação linear médio de 0.88, um erro médio absoluto de 0.0446 mV e foi capaz de identificar a frequência cardíaca dos indivíduos com um erro percentual médio de 1.91% (0.68% desconsiderando 5 casos discrepantes) se comparados o sinal original com o sinal extraído. Tomando como base o erro percentual médio de 0.68%, o algoritmo obteve uma acurácia de 99.32% na detecção da frequência cardíaca dos indivíduos, sendo equiparável (ou até superior) a resultados reportados na literatura.According to the World Health Organization (WHO), cardiovascular diseases are the leading causes of death in the world. The fast and accurate diagnosis of these diseases is very important in the treatment of patients and the analysis of the electrocardiogram (ECG), since its invention, is one of the most used tools for this diagnosis. In addition, for consultation purposes, these records need to be accessed from time to time by specialists. However, most existing ECG scans are still only available in printed form, which makes it difficult to preserve, to analyze and to share patients’ clinical information. The creation of a tool capable of obtaining the ECG signal from a digital image would be very useful for health clinics and hospitals. That being said, this work proposes the development of a computational tool capable of extracting the digital signal from digital images containing the ECG leads, developed in Python with the help of open libraries such as OpenCV, SciPy and Pandas and digital image processing techniques. The main goal of this work is to obtain a one-dimensional digital signal, containing time and amplitude, based on digital images of ECG leads. It is also proposed a way to identify the QRS complexes, and consequently the heart rate of the individual, using a modified version of Pan-Tompkins algorithm. Validation tests were performed on a total of 180 images obtained from the online PTB Diagnostic ECG Database using the online tool PhysioBank ATM. The proposed algorithm obtained an average linear correlation coefficient of 0.88, an average absolute error of 0.0446 mV and was able to identify the heart rate of individuals with an average percentage error of 1.91% (0.68% disregarding 5 outliers) when comparing the original signal with the extracted signal. Based on the average percentage error of 0.68%, the algorithm obtained an accuracy of 99.32% in detecting the heart rate of the individuals, being comparable (or even better) than results found in the literature