Eduardo Luz - Academia.edu (original) (raw)

Papers by Eduardo Luz

Research paper thumbnail of Crônica e brasilidade: a catação do mínimo e do escondido

Machado de Assis em Linha, 2012

Research paper thumbnail of Crônica e brasilidade: a catação do mínimo e do escondido

Machado de Assis em Linha, 2012

Research paper thumbnail of Estudo eletromiográfico das forças atuantes no quadril em apoio monopodal: uma nova visão sobre o modelo de Koch* An electromyographic study of forces acting at the hip on monopodal weightbearing: a new insight on Koch's model

Research paper thumbnail of Estudo eletromiográfico das forças atuantes no quadril em apoio monopodal: uma nova visão sobre o modelo de Koch* An electromyographic study of forces acting at the hip on monopodal weightbearing: a new insight on Koch's model

Research paper thumbnail of Evaluating the use of ECG signal in low frequencies as a biometry

Expert Systems with Applications, 2014

Traditional strategies, such as fingerprinting and face recognition, are becoming more and more f... more Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (>100 Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30 Hz and 60 Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30 Hz and 60 Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360 Hz (the maximum frequency existing in our database). We also evaluate the impact of:

Research paper thumbnail of ECG arrhythmia classification based on optimum-path forest

Expert Systems with Applications, 2013

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) sign... more An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the noninvasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the Optimum-Path Forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, predictive positive value, sensitivity, and F−score) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., Support Vector Machine (SVM), Bayesian and Multilayer Artificial Neural Network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. Figure 1: A normal heartbeat ECG signal.

Research paper thumbnail of Sistema de castas

Que es * De blanco e indía= mestizo * De castizo con blanca= español * De indio con negra= zambo ... more Que es * De blanco e indía= mestizo * De castizo con blanca= español * De indio con negra= zambo * De negro con zamba= zambo prieto * De español con negra= mulato * De mulata con blanco= morisco * De español con morisca= albino * De español con mestizo= castizo * De albino con blanca= salta atrás ( Uno de los miembros tenia que tener por como abuelo o bisabuelo negro) * De indio con mestizo= coyote ( También se denomina cholo) * De Mulato con india= chino * De español con coyote= harnizo * De coyote con indio= chamizo * De chino con india= cambujo * De salta atras con mulato= lobo * De lobo con china= gilvaro * De gilvaro con mulata= albarazado * De albarazado con negra= cambujo * De cambujo con india= sambiago * De sambiago con loba= campamulato * De campamulato con cambuja= tente en el aire * De tente en el aire con mulata= no te entiendo * De no te entiendo con india= torna atras * De español e india nace mestizo * De mestizo y español, castizo * De castiza y español, español * De española y negro, mulato * De español y mulato, morisco * De español y morisca, albino * De español y albino, torna atras * De indio y torna atrás, lobo * De lobo e india, zambazo * De zambazo e india, cambujo * De cambujo y mulata, albarazado * De albarazado y mulata, barcino * De barcino y mulata, coyote * De coyote india, chamizo * De chamizo y mestiza, coyote mestizo * De coyote y mestizo, alli te estás.

Research paper thumbnail of Crônica e brasilidade: a catação do mínimo e do escondido

Machado de Assis em Linha, 2012

Research paper thumbnail of Crônica e brasilidade: a catação do mínimo e do escondido

Machado de Assis em Linha, 2012

Research paper thumbnail of Estudo eletromiográfico das forças atuantes no quadril em apoio monopodal: uma nova visão sobre o modelo de Koch* An electromyographic study of forces acting at the hip on monopodal weightbearing: a new insight on Koch's model

Research paper thumbnail of Estudo eletromiográfico das forças atuantes no quadril em apoio monopodal: uma nova visão sobre o modelo de Koch* An electromyographic study of forces acting at the hip on monopodal weightbearing: a new insight on Koch's model

Research paper thumbnail of Evaluating the use of ECG signal in low frequencies as a biometry

Expert Systems with Applications, 2014

Traditional strategies, such as fingerprinting and face recognition, are becoming more and more f... more Traditional strategies, such as fingerprinting and face recognition, are becoming more and more fraud susceptible. As a consequence, new and more fraud proof biometrics modalities have been considered, one of them being the heartbeat pattern acquired by an electrocardiogram (ECG). While methods for subject identification based on ECG signal work with signals sampled in high frequencies (>100 Hz), the main goal of this work is to evaluate the use of ECG signal in low frequencies for such aim. In this work, the ECG signal is sampled in low frequencies (30 Hz and 60 Hz) and represented by four feature extraction methods available in the literature, which are then feed to a Support Vector Machines (SVM) classifier to perform the identification. In addition, a classification approach based on majority voting using multiple samples per subject is employed and compared to the traditional classification based on the presentation of single samples per subject each time. Considering a database composed of 193 subjects, results show identification accuracies higher than 95% and near to optimality (i.e., 100%) when the ECG signal is sampled in 30 Hz and 60 Hz, respectively, being the last one very close to the ones obtained when the signal is sampled in 360 Hz (the maximum frequency existing in our database). We also evaluate the impact of:

Research paper thumbnail of ECG arrhythmia classification based on optimum-path forest

Expert Systems with Applications, 2013

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) sign... more An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the noninvasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the Optimum-Path Forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, predictive positive value, sensitivity, and F−score) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., Support Vector Machine (SVM), Bayesian and Multilayer Artificial Neural Network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. Figure 1: A normal heartbeat ECG signal.

Research paper thumbnail of Sistema de castas

Que es * De blanco e indía= mestizo * De castizo con blanca= español * De indio con negra= zambo ... more Que es * De blanco e indía= mestizo * De castizo con blanca= español * De indio con negra= zambo * De negro con zamba= zambo prieto * De español con negra= mulato * De mulata con blanco= morisco * De español con morisca= albino * De español con mestizo= castizo * De albino con blanca= salta atrás ( Uno de los miembros tenia que tener por como abuelo o bisabuelo negro) * De indio con mestizo= coyote ( También se denomina cholo) * De Mulato con india= chino * De español con coyote= harnizo * De coyote con indio= chamizo * De chino con india= cambujo * De salta atras con mulato= lobo * De lobo con china= gilvaro * De gilvaro con mulata= albarazado * De albarazado con negra= cambujo * De cambujo con india= sambiago * De sambiago con loba= campamulato * De campamulato con cambuja= tente en el aire * De tente en el aire con mulata= no te entiendo * De no te entiendo con india= torna atras * De español e india nace mestizo * De mestizo y español, castizo * De castiza y español, español * De española y negro, mulato * De español y mulato, morisco * De español y morisca, albino * De español y albino, torna atras * De indio y torna atrás, lobo * De lobo e india, zambazo * De zambazo e india, cambujo * De cambujo y mulata, albarazado * De albarazado y mulata, barcino * De barcino y mulata, coyote * De coyote india, chamizo * De chamizo y mestiza, coyote mestizo * De coyote y mestizo, alli te estás.