Heart sounds recognition using multifractal detrended fluctuation analysis and support vector machine

2017 ◽  
Author(s):  
Mohamed Moustafa Azmy ◽  
Reem Mohamady
Keyword(s):  
Support Vector Machine ◽  
Detrended Fluctuation Analysis ◽  
Heart Sounds ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

scholarly journals Multifractal Analysis for Soft Fault Feature Extraction of Nonlinear Analog Circuits

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xinmiao Lu ◽  
Hong Zhao ◽  
Haijun Lin ◽  
Qiong Wu
Keyword(s):  
Support Vector Machine ◽  
Analog Circuits ◽  
Detrended Fluctuation Analysis ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Soft Fault ◽  
Nonlinear Analog Circuits ◽  
Detrended Fluctuation ◽  
Nonlinear Analog

Aiming at the nonstationarity and nonlinearity of soft fault signals of nonlinear analog circuits, the use of multifractal detrended fluctuation analysis can effectively reveal the dynamic behavior hidden in multiscale nonstationary signals. This paper adopts a new method that uses multifractal detrended fluctuation analysis to calculate the multifractal singularity spectrum of soft fault signals of nonlinear analog circuits. Moreover, this method endows the parameters of the spectrum with definite physical meanings including width, maximum singular index, minimum singular index, and corresponding singularity index of the extreme point. Therefore, this method can be applied to characterize the internal dynamic mechanism of the soft fault signals of nonlinear analog circuits, making it suitable for the feature extraction of fault circuits. All multifractal feature parameters can be organized into a feature set, which will be then input to a support vector machine, and fault detection for the nonlinear analog circuit can be conducted via the support vector machine.

Classification of Epileptic EEG Based on Detrended Fluctuation Analysis and Support Vector Machine

2011 ◽  
Vol 27 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Dongmei CAI ◽  
Weidong ZHOU ◽  
Shufang LI ◽  
Jiwen WANG ◽  
Guijuan JIA ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Detrended Fluctuation Analysis ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Detrended Fluctuation

scholarly journals Multifractal Detrended Fluctuation Analysis of Insole Pressure Sensor Data to Diagnose Vestibular System Disorders

2021 ◽  
Author(s):  
Batuhan Günaydın ◽  
Serhat İkizoğlu
Keyword(s):  
Vestibular System ◽  
Pressure Sensor ◽  
Detrended Fluctuation Analysis ◽  
Human Life ◽  
Sensor Data ◽  
Support Vector ◽  
Substantial Part ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Abstract The vestibular system (VS) is a sensory system that has a vital function in human life by serving to maintain balance. In this study, multifractal detrended fluctuation analysis (MFDFA) is applied to insole pressure sensor data collected from subjects in order to extract features to identify diseases related to VS dysfunction. We use the multifractal spectrum width as the feature to distinguish between healthy and diseased people. It is observed that multifractal behavior is more dominant and thus the spectrum is wider for healthy subjects, where we explain the reason as the long-range correlations of the small and large fluctuations of the time series for this group. We directly process the instantaneous pressure values to extract features in contrast to studies in the literature where gait analysis is based on investigation of gait dynamics (stride time, stance time, etc.) requiring long gait cycles. Thus, as the main innovation of this work, we detrend the data to give meaningful information even for a relatively short-duration gait cycle. Extracted feature set was input to fundamental classification algorithms where the Support-Vector-Machine (SVM) performed best with an accuracy of 98.2% for the binary classification as healthy or suffering. This study is a substantial part of a big project where we finally aim to identify the specific VS disease that causes balance disorder and also determine the stage of the disease, if any. Within this scope, the achieved performance gives high motivation to work more deeply on the issue.

Multifractal detrended fluctuation analysis of temperature in Spain (1960–2019)

2021 ◽  
pp. 126118
Author(s):  
Javier Gómez-Gómez ◽  
Rafael Carmona-Cabezas ◽  
Ana B. Ariza-Villaverde ◽  
Eduardo Gutiérrez de Ravé ◽  
Francisco José Jiménez-Hornero
Keyword(s):  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Detrended Fluctuation

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

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