Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/20763
Title: Dynamical analysis of physiologic signals
Authors: Noraznita Ramli.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2009
Abstract: The report provides a review study in the patterns and dynamics of physiological time series such as the human heart rate. The human heart rate consists of “real world” physiological signals derived from human subjects. They are available online and called Fantasia Database. [3, 4] Fantasia Database has ECG signals obtained from both equal number of men and women comprising of twenty young adults between the ages of 21 to 34 years old and twenty elderly between the ages of 68 to 85 years old. The subjects, rigorously-screened healthy, underwent a 120 minutes of continuous supine resting while continuous electrocardiographic (ECG) and respiration signals were collected. Analyses of physiologic signals tend to focus on average quantities, with comparisons of means and variances which are also known as time domain statistics. Thus, focusing on the analysis of actual time series derived from human subjects was explored using two methods. They are Approximate Entropy (ApEn) and Detrended Fluctuation Analysis (DFA). ApEn is a measurement designed to quantify the degree of regularity versus unpredictability in a given dataset. [5] DFA is a fractal-related method that provides for estimation of scaling exponents. [6, 7] The purpose of applying these methods is to see whether such techniques based on dynamical analysis add information to conventional statistics.
URI: http://hdl.handle.net/10356/20763
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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