IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jan 2007
Comparative StudyComplexity and nonlinearity in short-term heart period variability: comparison of methods based on local nonlinear prediction.
This paper evaluates the paradigm that proposes to quantify short-term complexity and detect nonlinear dynamics by exploiting local nonlinear prediction. Local nonlinear prediction methods are classified according to how they judge similarity among patterns of L samples (i.e., according to different definitions of the cells utilized to discretize the phase space) and examined in connection with different types of surrogate data: 1) phase-randomized or Fourier transform based, FT; 2) amplitude-adjusted FT, AAFT; 3) iteratively-refined AAFT, IAAFT, preserving distribution IAAFT-1; 4) IAAFT preserving power spectrum, IAAFT-2. ⋯ As to complexity analysis we found that: 1) although complexity indexes derived from different methods were different in terms of absolute values, changes due to experimental conditions were consistently detected; 2) complexity was significantly decreased by all the experimental conditions provoking a sympathetic activation and by controlled respiration at slow breathing rates. As to detection of nonlinearities we found that: 1) IAAFT-1 and IAAFT-2 surrogates performed similarly in all protocols; 2) FT and IAAFT surrogates detected about the same percentage of nonlinear dynamics in all protocols; 3) AAFT surrogates were inappropriate with all the methods and should be dismissed in future applications; 4) methods based on overlapping cells with variable size were characterized by a larger rate of false detections of nonlinear dynamics; 5) short-term heart period variability at rest was mostly linear; 6) controlled respiration at slow breathing rates increased nonlinear components, while the separate activation of the two branches of the autonomic nervous system (i.e., sympathetic or parasympathetic) was ineffective at this regard.
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IEEE Trans Biomed Eng · Jan 2007
Utility of approximate entropy from overnight pulse oximetry data in the diagnosis of the obstructive sleep apnea syndrome.
Approximate entropy (ApEn) is a family of statistics introduced as a quantification of regularity in time series without any a priori knowledge about the system generating them. The aim of this preliminary study was to assess whether a time series analysis of arterial oxygen saturation (SaO2) signals from overnight pulse oximetry by means of ApEn could yield essential information on the diagnosis of obstructive sleep apnea (OSA) syndrome. We analyzed SaO2 signals from 187 subjects: 111 with a positive diagnosis of OSA and 76 with a negative diagnosis of OSA. ⋯ Results showed that recurrence of apnea events in patients with OSA determined a significant increase in ApEn values. This method was assessed prospectively using the test dataset, where we obtained 82.09% sensitivity and 86.96% specificity. We conclude that ApEn analysis of SaO2 from pulse oximetric recording could be useful in the study of OSA.
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IEEE Trans Biomed Eng · Jan 2007
Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.
A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. ⋯ Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.