Conference Paper

Detrended fluctuation analysis of heart rate by means of symbolic series

Dept ESAII, Univ. Politec. de Catalunya, Barcelona, Spain
Conference: Computers in Cardiology, 2009
Source: OAI


Detrended fluctuation analysis (DFA) has been shown to be a useful tool for diagnosis of patients with cardiac diseases. The scaling exponents obtained with DFA are an indicator of power-law correlations in signal fluctuation, independently of signal amplitude and external trends. In this work, an approach based on DFA was proposed for analyzing heart rate variability (HRV) by means of RR series. The proposal consisted on transforming consecutive RR increments to symbols, according to an adapted symbolic-quantization. Three scaling exponents were calculated, ¿HF, ¿LF and ¿VLF, which correspond to the well known VLF, LF and HF frequency bands in the power spectral of the HRV. This DFA approach better characterized high and low risk of cardiac mortality in ischemic cardiomyiopathy patients than DFA applied to RR time series or RR increment series.

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    ABSTRACT: Sleep apnea syndrome (SAS) is monitored and examined clinically with polysomnography. However, it is expensive and complex to operate, which significantly affects the natural sleep of human. To evaluate the value of heart rate variability (HRV) in diagnosing SAS, we propose a new method for SAS classification based on fuzzy support vector machine (FSVM). Detrended Fluctuation Analysis (DFA) and Autoregressive (AR) model spectrum estimation are used to analyze R-R interval sequence of 38 healthy subjects and 28 SAS subjects during various sleep stages. Scaling exponents of age, gender and HRV at each sleep stage, as well as low/high frequency are selected as SAS characteristic parameters and FSVM is used to classify SAS. Results indicate that the proposed method can diagnose SAS effectively and the classification accuracy rate of SAS is 93.94%. Compared with current SAS diagnosis methods, this method is more simple and efficiently