Diagnosis of neurodegenerative diseases with a refined Lempel–Ziv complexity

Zhao, Huan; Xie, Junxiao; Chen, YangQuan; Cao, Junyi; Liao, Wei-Hsin; Cao, Hongmei

doi:10.1007/s11571-023-09973-9

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…The calculation of the LZ complexity steps involves graining the time series X (x 1 , x 2 , ..., x n ) into subsequences using a threshold value, which is always the average quantity. The signal is divided into two parts based on the threshold, assigning 1 to data larger than the threshold and 0 to data smaller than the threshold (Zhao et al, 2023).…”

Section: Lempel-ziv Complexitymentioning

confidence: 99%

“…The Hurst exponent describes the proportional relationship between (R/S) h and h as (R/S) h = c × h HE , where c is a constant and HE is the Hurst exponent. Thus, the Hurst exponent can be estimated by plotting the logarithm of (R/S) h against the logarithm of h. The slope of the fitted line in the plot corresponds to the Hurst exponent (Zhao et al, 2023). The value of the exponent ranges between 0 and 1.…”

Section: Hurst Exponentmentioning

confidence: 99%

“…In formula 12, the equation k(t) represents the measure of divergent distance, where K represents the initial distance and λ 1 denotes the largest Lyapunov exponent (Zhao et al, 2023).…”

Section: Lyapunov Exponentmentioning

confidence: 99%

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Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Hernandez,

Kargarnovin,

Hejazi

et al. 2023

Front. Comput. Neurosci.

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BackgroundConsidering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS.MethodsUsing the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients.ResultsA bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions.ConclusionAlthough nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.

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Section: Lempel-ziv Complexitymentioning

confidence: 99%

Section: Hurst Exponentmentioning

confidence: 99%

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Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Hernandez,

Kargarnovin,

Hejazi

et al. 2023

Front. Comput. Neurosci.

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“…Plenty of research has been done on Parkinson's Disease (PD) by examining EEG signals, using linear analysis methods such as Wavelet Transforms [15,16], Fourier Transforms [17,18], and Autoregressive methods [19,20] to extract features. Among non-linear methods, numerous studies based on Entropy [21,22], Lyapunov Exponent [23], Hurst Exponent [24,25], and Fractal Dimensions (FD) have been proposed to understand crucial aspects of Parkinson's Disease (PD) by deriving insights from the EEG signal. However fractal dimensions have several key advantages over previously mentioned measures as mentioned below: -…”

Section: Introductionmentioning

confidence: 99%

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Lal,

Chikkankod,

Longo

2023

Preprint

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Parkinson's Disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multi-channel, and non-linear signal with noise that problematizes identifying PD symptoms. A few studies have employed Fractal Dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that K-Nearest Neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of 99.65% for PD patients ON medication and 99.45% for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB's feature importance to generate brain topographic plots. This integration of Explainable AI (XAI) enhanced the transparency and comprehensibility of our model's classifications. Additionally, a comparison between the performance of FD and a few Entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing Fractal Dimension as an effective way of extracting salient features from EEG signals.

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Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography

Lal,

Chikkankod,

Longo

2024

Neural Comput & Applic

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Parkinson’s disease (PD) is an incurable neurological disorder that degenerates the cerebrospinal nervous system and hinders motor functions. Electroencephalography (EEG) signal analysis can provide reliable information regarding PD conditions. However, EEG is a complex, multichannel, and nonlinear signal with noise that problematizes identifying PD symptoms. A few studies have employed fractal dimension (FD) to extract distinguishing PD features from EEG signals. However, no exploratory study exists, as per our knowledge, on the efficiency of the different FD measures. We aim to conduct a comparative analysis of the various FDs that, as feature extraction measures, can discriminate PD patients who are ON and OFF medication from healthy controls using ML architecture. This study has implemented and analyzed several techniques for segmentation, feature extraction, and ML models. The results show that k-nearest neighbors (KNN) classifier with Higuchi FD and 90% overlap for segmented window delivers the highest accuracies, yielding a mean accuracy of $$99.65\pm 0.15\%$$ 99.65 ± 0.15 % for PD patients ON medication and $$99.45\pm 0.18\%$$ 99.45 ± 0.18 % for PD patients OFF medication, respectively. The model accurately identifies the signs of the disease in resting-state EEG with almost equivalent accuracy in both OFF and ON medication patients. To enhance the interpretability in our study, we leveraged XGB’s feature importance to generate brain topographic plots. This integration of explainable AI (XAI) enhanced the transparency and comprehensibility of our model’s classifications. Additionally, a comparison between the performance of FD and a few entropy measures has also been drawn to validate the significance of FD as a superior feature extraction measure. This study contributes to the body of knowledge with an architectural pipeline for detecting PD in resting-state EEG while emphasizing fractal dimension as an effective way of extracting salient features from EEG signals.

show abstract

Diagnosis of neurodegenerative diseases with a refined Lempel–Ziv complexity

Cited by 4 publications

References 52 publications

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Fractal Dimensions and Machine Learning for Detection of Parkinson’s Disease in Resting-State EEG

Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography

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