Sistema automÃtico para anÃlise de variabilidade da freqÃencia cardÃaca

• Autor(a) principal: JoÃo Paulo do Vale Madeiro
• Data de Publicação: 2008
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFC
• Texto Completo: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=2050
• topic: Eletrocardiograma; transformada Wavelet; transformada de Hilbert; variabilidade da frequencia cardÃaca; TELEINFORMATICA

This dissertation describes a system for analysis of heart rate variability through metrics on time and frequency domain and by non-linear methodology, which is initiated by the process of segmentation of the QRS complex of the electrocardiogram signal. The motivation for this work is the analysis of the influence from the algorithms of beat segmentation and selection of valid cardiac cycles for the variability analysis, which were developed in the research process, over the computation of the metrics. After determining the intervals between QRS complexes (RR), the cardiac cycles with ectopic beats, resultant of arrhythmic events or detection fails (false-positive or false negative) are excluded. Then, the available metrics of heart rate variability found on literature are computed over the time series of intervals between normal beats (NN): on time domain (statistical and geometrical methods), on frequency domain (VLF - Very Low Frequency, LF - Low Frequency and HF â High Frequency) and by non-linear methodology (Poincarà plot). The QRS detection and segmentation results are validated through simulation tests over exams from Arrhythmia Database and QT database of the MIT-BIH database. The manual annotations of the QRS fiducial points and QRS onset and offset are compared with the automatic detections. The results related to heart rate variability metrics are validated through the manual selection of beats, and consequently of the intervals between them, pertaining to exams selected from Arrhythmia Database and the computation of the referred metrics over them, comparing with those ones automatically generated by the proposed method. The system, which provides averages of positive predictivity as 99.35% and sensitivity as 99.02%, and averages of deviations between automatic and manual analysis of heart rate variability metrics varying from 0.05% to 5.24%, can be carried into several platforms, making possible its production in commercial scale.