A Novel Neural Network based Classification for ECG Signals

Cardiac Arrhythmia represents heart abnormalities. This problem is faced by people, irrespective of age. Even the physicians feel difficulty in diagnosing the abnormal behavior of heart accurately. Accurate detection of cardiac abnormalities helps to provide right treatment. Classification plays an important role in predicting abnormal behaviors of heart and it helps the physician to treat the patients who are having cardiac arrhythmia. Extracted features from ECG (Electrocardiogram) signals are used for classification. It is possible to extract multiple features from ECG signal regardless of the features used for classification. Classification performed using all the extracted features leads to misclassification of abnormalities. So feature selection is an important concept in classifying the normal and abnormal behavior of heart. MIT BIH Arrhythmia dataset is used in our proposed work where the classification is done in MATLAB using Fitting Neural Network. DOI: 10.17762/ijritcc2321-8169.150314

EXPERT SYSTEM MODELING FOR THE MULTIDIMENSIONAL EVALUATION OF AGED PEOPLE

Objective: to describe the modeling of an Expert System for the Multidimensional Evaluation of aged people.Method: the study was carried out from April 2021 to September 2022 by researchers from universities in the inland of Minas Gerais - Brazil. The following stages were conducted: literature review; survey of the System requirements; modeling; and implementation.Results: the System makes it possible to assess the physical, psychosocial and functional aspects; it identifies the geriatric-gerontological needs and classifies them according to severity levels, in addition to offering suggestions for therapeutic interventions. The diverse information generated can be shared through instant messengers via apps, providing the basis for the development of a monitoring panel for aged people assisted in the municipality.Conclusion: the System presents itself as a technological solution given the importance of the multidimensional evaluation of aged people within the scope of care for this population segment and the lack of technological solutions to carry out the assessment

Continuously Tested and Used QRS Detection Algorithm: Free Access to the MATLAB Code

Each ECG analysis begins with the detection of the QRS complex, which is the most distinguishable wave for initial investigation. Long ago we published an algorithm for ventricular beats (VB) detection in single ECG lead. The classification of normal QRS complexes is based on the slope, the amplitude and the width of the ECG waves. Other criteria recognize ventricular ectopic beats (EB) by presence of biphasic beats and separate premature EB from the already detected QRS complexes. The aim of this paper is to place the MATLAB program of our algorithm at disposal to the readers (http://www.biomed.bas.bg/bioautomation/2019/vol_23.1/files/23.1_06.zip) looking forward to more successful ECG investigations

MODELAGEM DE SISTEMA ESPECIALISTA PARA AVALIAÇÃO MULTIDIMENSIONAL DE PESSOAS IDOSAS

Objetivo: descrever a modelagem de um Sistema Especialista para Avaliação Multidimensional de pessoas idosas.Método: realizado no período de abril de 2021 a setembro de 2022, sendo conduzido por pesquisadores de universidades do interior de Minas Gerais - Brasil. Percorreu-se pelas etapas: revisão de literatura, levantamento dos requisitos para o Sistema, modelagem e implementação.Resultados: o Sistema possibilita avaliação dos aspectos físico, psicossocial e funcional, identifica as necessidades geriátrico-gerontológicas e as classifica de acordo com níveis de gravidade, além de oferecer sugestões de intervenções terapêuticas. As informações geradas podem ser compartilhadas por meio de mensageiros instantâneos através de aplicativos, dando base para o desenvolvimento de um painel de monitoramento das pessoas idosas assistidas no município.Conclusão: o Sistema se apresenta como uma solução tecnológica dada a importância da avaliação multidimensional da pessoa idosa no âmbito do cuidado a essa população e a carência de soluções tecnológicas para realizar a avaliação

DISEÑO DE UN SISTEMA EXPERTO PARA LA EVALUACIÓN MULTIDIMENSIONAL DEL ADULTO MAYOR

Objetivo: describir el diseño de un Sistema Experto para la Evaluación Multidimensional del adulto mayor.Método: estudio realizado, de abril de 2021 a septiembre de 2022, por investigadores de universidades del interior de Minas Gerais, Brasil. Pasó por los pasos: revisión de la literatura, relevamiento de los requisitos del Sistema, diseño e implementación.Resultados: el Sistema permite evaluar aspectos físicos, psicosociales y funcionales, identifica necesidades geriátrico-gerontológicas y las clasifica según el nivel de gravedad, además ofrece sugerencias de intervenciones terapéuticas. La información generada podrá ser compartida a través de mensajería instantánea mediante aplicaciones, y sentará las bases para el desarrollo de un panel de seguimiento de los adultos mayores atendidos en el municipio.Conclusión: el Sistema es una solución tecnológica dada la importancia que tiene la evaluación multidimensional del adulto mayor en el ámbito de la atención de esta población y la falta de soluciones tecnológicas para realizar la evaluación

Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification

Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of classifier depends significantly on the quality of these features. This study develops an effective method to extract low-dimensional ECG beat feature vectors. It employs wavelet multi-resolution analysis to extract time-frequency domain features and then applies principle component analysis to reduce the dimension of the feature vector. In classification, 12-element feature vectors characterizing six types of beats are used as inputs for one-versus-one support vector machine, which is conducted in form of 10-fold cross validation with beat-based and record-based training schemes. Tested upon a total of 107049 beats from MIT-BIH arrhythmia database, our method has achieved average sensitivity, specificity and accuracy of 99.09%, 99.82% and 99.70%, respectively, using the beat-based training scheme, and 44.40%, 88.88% and 81.47%, respectively, using the record-based training scheme

Automatic diagnosis of heart diseases branch block

Los electrocardiogramas (ECG) registran la actividad eléctrica del corazón a través de doce señales principales denominadas derivaciones. Estas derivaciones son analizadas por expertos médicos observando aquellos segmentos de la señal eléctrica que determinan cada una de las patologías que pueden afectar al corazón. Este hecho en general, es un condicionante muy importante para el diseño de sistemas expertos de diagnóstico médico, ya que es preciso conocer, delimitar y extraer de la señal eléctrica aquellos segmentos que determinan la patología. Dar solución a estos problemas, sería fundamental para facilitar el diseño de sistemas expertos para el diagnóstico de enfermedades cardiacas. El objetivo de este trabajo es demostrar que es posible identificar patologías cardiacas analizando la señal completa de las diferentes derivaciones de los ECGs, y determinar puntos concretos que determinan la patología en lugar de segmentos de la señal. Para ello se ha utilizado una BBDD de electrocardiogramas y se ha determinado mediante un algoritmo, los puntos de la señal que determinan la patología. Se ha aplicado a la patología de bloqueos de rama y los puntos obtenidos con el algoritmo se han utilizado para el diseño de un clasificador automático basado en redes neuronales artificiales, obteniendo un coeficiente de sensibilidad del 100% y de especificad del 99.24%, demostrando su validez para el diseño de sistemas expertos de clasificación