Discover @brunobro Open Source projects

Bruno Rodrigues de Oliveira (@brunobro)

brunobro

Stars
21
Global Rank 621,339 (Top 22 %)
Followers 7
Registered almost 9 years ago
Most used languages

Jupyter Notebook
76.9 %

MATLAB
15.4 %

Python
7.7 %
Location 🇧🇷 Brazil
Country Total Rank 19,077
Country Ranking

MATLAB
335

Jupyter Notebook
439

deteccao-remocao-de-outliers

Detecção/Remoção de Outliers com Python: Casos Univariado e Multivariado

Jupyter Notebook

bordasimagensdwt

Obtenção de Bordas em Imagens utilizando Transformada Wavelet

Jupyter Notebook

escalograma-series-temporais

Escalograma para análise de Séries Temporais

Jupyter Notebook

decomposicao-em-valores-singulares-em-multirresolucao

Decomposição em Valores Singulares em Multirresolução (MRSVD - Multiresolution Singular Value Decomposition)

Jupyter Notebook

ahptd

ANALYTIC HIERARCHY PROCESS (AHP) FOR TABULAR DATA

deteccao-de-picos-e-vales-em-series-temporais

Jupyter Notebook

comitemaquinas

Comitê de Máquinas de Aprendizado (Ensemble Learning) - Voto Majoritário

Jupyter Notebook

dataset-forty-soybean-cultivars-from-subsequent-harvests

We present a dataset obtained from forty soybean cultivars planted in subsequent seasons. The experiment used randomized blocks, arranged in a split-plot scheme, with four replications.

Jupyter Notebook

como-exportar-um-dataframe-do-pandas-para-uma-tabela-do-ms-word

Como exportar um DataFrame do Pandas para uma Tabela do MS Word

Jupyter Notebook

tendenciaserietemporalutiliozandodwt

Obtenção de tendência de séria temporal empregando a Transformada Wavelet

Jupyter Notebook

alocacao-inventimentos-com-ahp

Alocação de recursos em investimentos utilizando um modelo da Análise Hierárquica de Processos (AHP)

Jupyter Notebook

power-line-interference-removal-in-ECG

Introduction The analysis of electrocardiogram (ECG) signals allows the experts to diagnosis several cardiac disorders. However, the accuracy of such diagnostic depends on the signals quality. In this paper it is proposed a simple method for power-line interference (PLI) removal based on the wavelet decomposition, without the use of thresholding techniques.

early-detection-of-ventricular-bigeminy-trigeminy-rhythms

Premature Ventricular Contraction (PVC) is an arrhythmia that can be associated with several cardiac disorders that affect from 40% to 75% of the general population. PVC occurrence is measure from Electrocardiogram (ECG). If in an ECG occur one (or two) PVC between two Normal heartbeats, then there is a Ventricular Bigeminy (or Trigeminy). The prevalence of Ventricular Bigeminy/Trigeminy rhythms was associated with angina, hypertension, congestive heart failure and myocardial infarction. For this, early detection of these rhythms is very important. In this work it is proposed a new approach for early diagnosis of these rhythms, which is based on Random Forest algorithm and information about previous heartbeat and heart rhythm. Thus, the proposed approach uses only the information before occurrence of Ventricular Bigeminy/Trigeminy. This simple approach was capable of predict the Bigeminy/Trigeminy occurrence with accuracy, sensitivity and specificity of 98.94%, 96.28% and 99.83, respectively. Furthermore, the results show that the Ventricular Bigeminy/Trigeminy is preceded for Normal, A-V junctional and Paced heart rhythms in most of the examples. Besides that, it is presented a simple algorithm for decision about the occurrence of Ventricular Bigeminy/Trigeminy rhythms.