Sergio Pinto Gomes Junior 
, João Baptista de Oliveira e Souza Filho , Felipe da Rocha Henriques & Michel Pompeu Tcheou
Abstract: This work discusses the design of an automatic detector of arrhythmia episodes in patients submitted to dialysis. The system aims to operate on portable devices in real-time, allowing a faster response of healthcare workers to possible intercurrence episodes. The detection is based on processing short windows of samples extracted from the electrocardiogram signal around the R-wave peak in raw format. A comprehensive study evaluating several classification techniques and class-imbalance strategies is conducted based on the MIT-BIH Arrhythmia Database. Besides, a new procedure for tuning the sample window length based on an experimental feature importance cumulative distribution is proposed. Results show that a Random Forest classifier, trained with minority class oversampling, is cost-effective regarding complexity and computational cost, achieving an accuracy of 98.7% for windows sizes as small as 105 samples.
Keywords: Dialysis, arrhythmia, beat classifier, clinical decision support.
DOI code: 10.21528/lnlm-vol20-no2-art3
PDF file: vol20-no2-art3.pdf
BibTex file: vol20-no2-art3.bib