Windowing Compensation in Fourier Based Surrogate Analysis and Application to EEG Signal Classification

被引:8
作者
Caza-Szoka, Manouane [1 ]
Massicotte, Daniel [1 ]
机构
[1] Univ Quebec Trois Rivieres, Lab Signaux & Syst Integre, Dept Elect & Comp Engn, Chaire Rech Sur Signaux & Intelligence Syst Haute, Trois Rivieres, PQ G9A 5H7, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Electroencephalography; Time-domain analysis; Electromyography; Time series analysis; Frequency-domain analysis; Bayes methods; Sensors; Attention-deficit disorder primarily inattentive (ADD); attention-deficit disorder primarily hyperactive-impulsive (ADHD); electroencephalogram (EEG); fractal dimension; nonlinear analysis; nonlinear dynamics; surrogate data; windowing techniques; TIME-SERIES; NONLINEARITY; ALGORITHMS; DIMENSION; ENTROPY; MEG;
D O I
10.1109/TIM.2022.3149325
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article shows how adding a second step of windowing after each phase randomization can reduce the false rejection rate in the Fourier-based surrogate analysis (SA). Windowing techniques reduce the discontinuities at the boundaries of the periodically extended data sequence in the Fourier Series. However, they add time-domain nonstationarity that affects the SA. This effect is particularly problematic for short low-pass signals. Applying the same window to the surrogate data allows having the same nonstationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte Carlo simulations. Previous methods were not able to yield good performances for left- and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests and bilateral tests. In order to show that the proposed windowing method can be useful in the real context, in this extended paper, it was applied for an electroencephalogram (EEG) diagnostic problem. A dataset comprising the EEG measurements of 15 subjects distributed in three groups, attention-deficit disorder primarily hyperactive-impulsive (ADHD), attention-deficit disorder primarily inattentive (ADD), and anxiety with attentional fragility (ANX), was used. Both statistical and machine learning (naive Bayesian) approaches were considered. The mean short-windowed SA (MSWSA) was used as a signal feature, and its performances were studied with respect to the windowing systems. The main findings were that: 1) the MSWSA feature has less variability for ADD than for ADHD or ANX; 2) the proposed windowing method reduces bias and nonnormality of the SA feature; 3) with the proposed method and a naive Bayesian classifier, a 93% success rate of discriminating ADD from ADHD and ANX was achieved with leave-one-out cross-validation; and 4) the new feature could not have yielded interesting results without the proposed windowing system.
引用
收藏
页数:11
相关论文
共 61 条
[1]   FOCUS: Detecting ADHD Patients by an EEG-Based Serious Game [J].
Alchalabi, Alaa Eddin ;
Shirmohammadi, Shervin ;
Eddin, Amer Nour ;
Elsharnouby, Mohamed .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2018, 67 (07) :1512-1520
[2]   Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients [J].
Andrzejak, Ralph G. ;
Schindler, Kaspar ;
Rummel, Christian .
PHYSICAL REVIEW E, 2012, 86 (04)
[3]   Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state [J].
Andrzejak, RG ;
Lehnertz, K ;
Mormann, F ;
Rieke, C ;
David, P ;
Elger, CE .
PHYSICAL REVIEW E, 2001, 64 (06) :8-061907
[4]  
[Anonymous], 2013, DIAGN STAT MAN MENT, V21
[5]  
[Anonymous], 2020, CAN ADHD PRACT GUID, V4th
[6]  
[Anonymous], 1999, Discrete-Time Signal Processing
[7]   Detecting connectivity in EEG: A comparative study of data-driven effective connectivity measures [J].
Bakhshayes, Hanieh ;
Fitzgibbon, Sean P. ;
Janani, Azin S. ;
Grummett, Tyler S. ;
Popea, Kenneth J. .
COMPUTERS IN BIOLOGY AND MEDICINE, 2019, 111
[8]  
Balli T., 2009, ENCY INFORM SCI TECH, VSecond Ed., P2834
[9]   Nonlinear time-series analysis revisited [J].
Bradley, Elizabeth ;
Kantz, Holger .
CHAOS, 2015, 25 (09)
[10]  
Caza-Szoka M., 2021, P IEEE INT INSTR MEA, P1