Two novel methods for multiclass ECG arrhythmias classification based on PCA, fuzzy support vector machine and unbalanced clustering

Nait-Hamoud, Mohamed Cherif; Moussaoui, Abdelouaheb

doi:10.1109/icmwi.2010.5647931

Cited by 12 publications

(7 citation statements)

References 16 publications

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“…Nait-Hamound M. y Moussaoui A. [9] presentaron dos métodos para la clasificación de arritmias multiclase aplicando Análisis de Componentes Principales (PCA), las Máquinas de Soporte de Vectores Difusos (FSVM, por sus siglas en inglés), y la Closterización Desbalanceada (UC, por sus siglas en inglés).…”

Section: Estado Del Arteunclassified

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Algoritmos genéticos aplicados a la optimización de características en la clasificación de arritmias cardiacas utilizando los clasificadores KNN y naive Bayes

Padilla-Navarro¹,

Gonzalez-Reyna²,

Aguilera-González³

et al. 2017

RCS

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Resumen. Muchas muertes en el mundo suceden a consecuencia de enfermedades cardiovasculares. El método propuesto combina metaheuristícas-Algoritmos Genéticos (AG)-, y los clasificadores KNN y Naive Bayes. Las pruebas se realizaron a través de una base de datos del Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) [1]. Las metaheurísticas se implementan para mejorar el rendimiento de los clasificadores. Los resultados experimentales demuestran que se logra hasta un 94 % de precisión en la clasificación.

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Section: Estado Del Arteunclassified

“…Nait-Hamoud et al [11], muestran dos nuevos métodos para la clasificación de ECG para discriminar cinco tipos de ritmos cardíacos combinando Análisis de Componentes Principales (PCA) y Modified Fuzzy One-Against-One (MFOAO).…”

Section: Estado Del Arteunclassified

Algoritmos genéticos aplicados a la optimización de características en la clasificación de arritmias cardiacas utilizando los clasificadores KNN y naive Bayes

Padilla-Navarro¹,

Gonzalez-Reyna²,

Aguilera-González³

et al. 2017

RCS

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“…Berbagai teknik dikembangkan peneliti agar dapat mengenali kelainan jantung melalui sinyal EKG secara otomatis. Pengolahan sinyal EKG pada domain waktu dilakukan pada [3]- [5]. Metode yang digunakan diantaranya principal component analysis (PCA) dan Hjorth descriptor [6].…”

Section: Pendahuluanunclassified

Klasifikasi Citra Rekaman Sinyal Elektrokardiogram Menggunakan Metode Analisis Tekstur, K-Nn Dan Multilayer Perceptron

Sofie¹,

Rizal²

2016

Simet

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Sinyal elektrokardiogram (EKG) memiliki informasi yang menggambarkan kondisi kesehatan jantung. Beragai teknik analisis sinyal EKG dikembangkan untuk mengetahui kelainan di jantung secara ototmatis. Pada kenyataannya di Indonesia, kebanyakan perangkat EKG hanya menghasilkan rekaman berupa kertas EKG sehingga metode pengolahan sinyal tidak bisa diterapkan. Pada penelitian ini dilakukan pengenalan kelainan jantung melalui citra rekaman EKG menggunakan analisis tekstur. Garis sinyal EKG yang tergambar dalam citra rekaman EKG diharapkan bisa dibedakan antara kondisi yang satu dengan yang lain. Untuk ekstraksi ciri digunakan ciri statistik orde 1 dan grey level co-occurence matrix (GLCM) pada arah 0o, 45o, 90o, dan 135o. Untuk klasifikasi digunakan K-nearest neighbor (K-NN) dan multilayer perceptron (MLP). Akurasi yang dihasilkan mencapai 44.12% untuk lima kelas data dan 65.82% untuk dua kelas data. Penggunaan teknik pengolahan ctra terbukti mampu meningkatkan akurasi yang semula rendah.Kata kunci: analisis tekstur, K-NN, multilayerperceptron, citra rekaman EKG, pengolahan citra.

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“…It is crucial to know how to extract relevant discriminating and more characterizing features of the ECG signal. Principal components analysis (PCA), independent components analysis (ICA) and linear discriminating analysis (LDA) [8][9][10][11][12] are applied on biomedical signals to reduce features space dimension in aim to avoid redundancy and to decrease the computational cost. These data were then taken as the input of a classi¯er.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

Slimane

Talha

Zairi

et al. 2016

Biomed. Eng. Appl. Basis Commun.

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The main aim of this paper is a contribution to the design of an Intelligent Diagnosis System for Cardiac anomalies detection in acquired ECG (Electrocardiogram) signals. To attain this goal, the authors have developed two approaches to extract the most relevant and signi¯cant parameters that can best classify ECGs into two classes namely, Normal and Abnormal from real Electrocardiogram signals. The¯rst approach is a time method which consists in modeling ECGs using RBF (Radial Basis Function) neural network algorithm, whereas, the second approach is a frequency based method which relies on the DWT (Discrete Wavelet Transform) that projects the signal into a timescale (time-frequency) plane. In both approaches, the resulting data are submitted to the same SVM (Support Machine Vector) Classi¯er. The results obtained have shown a good adjustment of relevant parameters for each approach and have revealed the most e±cient combined processing-classi¯cation algorithm that has achieved classi¯cation rates up to 100 % allowing at a time a reduced number of parameters. This fact had considerably simpli¯ed the hardware for a real time implementation. Finally, the authors compared both approaches and have identi¯ed some implementation constraints, such as the sampling frequency for the frequency based approach and preprocessing and cutting for the time approach.

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Two novel methods for multiclass ECG arrhythmias classification based on PCA, fuzzy support vector machine and unbalanced clustering

Cited by 12 publications

References 16 publications

Algoritmos genéticos aplicados a la optimización de características en la clasificación de arritmias cardiacas utilizando los clasificadores KNN y naive Bayes

Algoritmos genéticos aplicados a la optimización de características en la clasificación de arritmias cardiacas utilizando los clasificadores KNN y naive Bayes

Klasifikasi Citra Rekaman Sinyal Elektrokardiogram Menggunakan Metode Analisis Tekstur, K-Nn Dan Multilayer Perceptron

Comparison of Time and Frequency Methods for Cardiac Anomalies Recognition

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