Processing and analyzing physiological signals to detect a health condition

Described herein is a method of developing a fuzzy logic system to detect a non-normal health condition. In particular, signal processing and transformation of electrocardiogram (EKG) signals for sleep disorder breathing are provided. The method includes: recording EKG measurements during sleep; cli...

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Main Authors Al-Abed, Mohammad A, Behbehani, Khosrow, Manry, Michael T
Format Patent
LanguageEnglish
Published 21.06.2011
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Abstract Described herein is a method of developing a fuzzy logic system to detect a non-normal health condition. In particular, signal processing and transformation of electrocardiogram (EKG) signals for sleep disorder breathing are provided. The method includes: recording EKG measurements during sleep; clipping the measurements into clips of a consistent length; calculating heart rate and obtaining an evenly sampled discrete time series data clip; performing Short-Time Discrete Fourier Transform on each data clip generating STDFT respective matrices; encoding each STDFT matrix into a grey-level image; calculating Grey-Level Co-occurrence Matrices; extracting textural features; performing statistical analysis on the features to formulate rules; and employing the rules in a Fuzzy Logic system. The system and method described herein yields an accuracy of 75.88%, or better, in detection of sleep apnea.
AbstractList Described herein is a method of developing a fuzzy logic system to detect a non-normal health condition. In particular, signal processing and transformation of electrocardiogram (EKG) signals for sleep disorder breathing are provided. The method includes: recording EKG measurements during sleep; clipping the measurements into clips of a consistent length; calculating heart rate and obtaining an evenly sampled discrete time series data clip; performing Short-Time Discrete Fourier Transform on each data clip generating STDFT respective matrices; encoding each STDFT matrix into a grey-level image; calculating Grey-Level Co-occurrence Matrices; extracting textural features; performing statistical analysis on the features to formulate rules; and employing the rules in a Fuzzy Logic system. The system and method described herein yields an accuracy of 75.88%, or better, in detection of sleep apnea.
Author Al-Abed, Mohammad A
Manry, Michael T
Behbehani, Khosrow
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