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Houmani N, Vialatte F, Gallego-Jutglà E, Dreyfus G, Nguyen-Michel VH, Mariani J, Kinugawa K. Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework. PLoS One 2018; 13:e0193607. [PMID: 29558517 PMCID: PMC5860733 DOI: 10.1371/journal.pone.0193607] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 02/14/2018] [Indexed: 11/19/2022] Open
Abstract
This study addresses the problem of Alzheimer’s disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer’s disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.
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Affiliation(s)
- Nesma Houmani
- SAMOVAR, Télécom SudParis, CNRS, Université Paris-Saclay, 9 rue Charles Fourier EVRY, France
- * E-mail:
| | - François Vialatte
- UMR CNRS 8249 Brain Plasticity Laboratory, Paris, France
- ESPCI Paris, PSL Research University, Paris, France
| | - Esteve Gallego-Jutglà
- Data and Signal Processing Research Group, U Science Tech, University of Vic–Central University of Catalonia, Vic, Catalonia, Spain
| | | | - Vi-Huong Nguyen-Michel
- AP-HP, DHU FAST, GH Pitié-Salpêtrière-Charles Foix, Functional Exploration Unit of older patients, Ivry-sur-Seine, France
| | - Jean Mariani
- AP-HP, DHU FAST, GH Pitié-Salpêtrière-Charles Foix, Functional Exploration Unit of older patients, Ivry-sur-Seine, France
- Sorbonne Universités, UPMC Univ Paris 06, UMR 8256, Biological Adaptation and Ageing, Paris, France
- CNRS, UMR 8256, Biological Adaptation and Ageing, Paris, France
| | - Kiyoka Kinugawa
- AP-HP, DHU FAST, GH Pitié-Salpêtrière-Charles Foix, Functional Exploration Unit of older patients, Ivry-sur-Seine, France
- Sorbonne Universités, UPMC Univ Paris 06, UMR 8256, Biological Adaptation and Ageing, Paris, France
- CNRS, UMR 8256, Biological Adaptation and Ageing, Paris, France
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Wen D, Zhou Y, Li X. A critical review: coupling and synchronization analysis methods of EEG signal with mild cognitive impairment. Front Aging Neurosci 2015; 7:54. [PMID: 25941486 PMCID: PMC4403503 DOI: 10.3389/fnagi.2015.00054] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 03/30/2015] [Indexed: 11/13/2022] Open
Abstract
At present, the clinical diagnosis of mild cognitive impairment (MCI) patients becomes the important approach of evaluating early Alzheimer's disease. The methods of EEG signal coupling and synchronization act as a key role in evaluating and diagnosing MCI patients. Recently, these coupling and synchronization methods were used to analyze the EEG signals of MCI patients according to different angles, and many important discoveries have been achieved. However, considering that every method is single-faceted in solving problems, these methods have various deficiencies when analyzing EEG signals of MCI patients. This paper reviewed in detail the coupling and synchronization analysis methods, analyzed their advantages and disadvantages, and proposed a few research questions needed to solve in the future. Also, the principles and best performances of these methods were described. It is expected that the performance analysis of these methods can provide the theoretical basis for the method selection of analyzing EEG signals of MCI patients and the future research directions.
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Affiliation(s)
- Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- Institute of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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Capturing dynamic patterns of task-based functional connectivity with EEG. Neuroimage 2012; 66:311-7. [PMID: 23142654 DOI: 10.1016/j.neuroimage.2012.10.032] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 10/04/2012] [Accepted: 10/19/2012] [Indexed: 11/23/2022] Open
Abstract
A new approach to trace the dynamic patterns of task-based functional connectivity, by combining signal segmentation, dynamic time warping (DTW), and Quality Threshold (QT) clustering techniques, is presented. Electroencephalography (EEG) signals of 5 healthy subjects were recorded as they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into durations that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each temporal window, DTW is employed to measure the functional similarities among channels. Unlike commonly used temporal similarity measures, such as cross correlation, DTW compares time series by taking into consideration that their alignment properties may vary in time. QT clustering analysis is then used to automatically identify the functionally connected regions in each temporal window. For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity.
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Dauwels J, Weber T, Vialatte F, Musha T, Cichocki A. Quantifying statistical interdependence, part III: N > 2 point processes. Neural Comput 2011; 24:408-54. [PMID: 22091663 DOI: 10.1162/neco_a_00235] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Stochastic event synchrony (SES) is a recently proposed family of similarity measures. First, "events" are extracted from the given signals; next, one tries to align events across the different time series. The better the alignment, the more similar the N time series are considered to be. The similarity measures quantify the reliability of the events (the fraction of "nonaligned" events) and the timing precision. So far, SES has been developed for pairs of one-dimensional (Part I) and multidimensional (Part II) point processes. In this letter (Part III), SES is extended from pairs of signals to N > 2 signals. The alignment and SES parameters are again determined through statistical inference, more specifically, by alternating two steps: (1) estimating the SES parameters from a given alignment and (2), with the resulting estimates, refining the alignment. The SES parameters are computed by maximum a posteriori (MAP) estimation (step 1), in analogy to the pairwise case. The alignment (step 2) is solved by linear integer programming. In order to test the robustness and reliability of the proposed N-variate SES method, it is first applied to synthetic data. We show that N-variate SES results in more reliable estimates than bivariate SES. Next N-variate SES is applied to two problems in neuroscience: to quantify the firing reliability of Morris-Lecar neurons and to detect anomalies in EEG synchrony of patients with mild cognitive impairment. Those problems were also considered in Parts I and II, respectively. In both cases, the N-variate SES approach yields a more detailed analysis.
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Affiliation(s)
- Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore.
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Sakkalis V. Applied strategies towards EEG/MEG biomarker identification in clinical and cognitive research. Biomark Med 2011; 5:93-105. [PMID: 21319971 DOI: 10.2217/bmm.10.121] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
As the underlying causes of several neuronal disorders and neurodegenerative diseases still remain, to some extent, unknown and no accurate diagnostic tests are available, the identification of prognostic and predictive neurophysiological biomarkers has attracted tremendous interest. The continuous advancement of neuroscience methods applied in EEG and magnetoencephalography has been successful in capturing brain processes and identifying persistent cognitive deficits. In this article, the most promising approaches of this rapidly evolving field, along with some indicative clinical applications in major neuropathophysiological disorders, are reviewed. Such strategies for biomarker identification will lead the way to future clinical applications even if, currently, EEG biomarkers are in a premature state.
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Affiliation(s)
- Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research & Technology, Science & Technology Park of Crete, Vassilika Vouton, Heraklion, Greece.
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Vialatte FB, Dauwels J, Maurice M, Musha T, Cichocki A. Improving the specificity of EEG for diagnosing Alzheimer's disease. Int J Alzheimers Dis 2011; 2011:259069. [PMID: 21660242 PMCID: PMC3109519 DOI: 10.4061/2011/259069] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2011] [Revised: 03/18/2011] [Accepted: 03/28/2011] [Indexed: 12/04/2022] Open
Abstract
Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α1 (7.5–9.5 Hz),
α2 (9.5–12.5 Hz), and β
(12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ
range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.
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Dauwels J, Srinivasan K, Ramasubba Reddy M, Musha T, Vialatte FB, Latchoumane C, Jeong J, Cichocki A. Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin? Int J Alzheimers Dis 2011; 2011:539621. [PMID: 21584257 PMCID: PMC3090755 DOI: 10.4061/2011/539621] [Citation(s) in RCA: 96] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Revised: 02/10/2011] [Accepted: 02/15/2011] [Indexed: 11/20/2022] Open
Abstract
Medical studies have shown that EEG of Alzheimer's disease (AD) patients is "slower" (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.
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Affiliation(s)
- Justin Dauwels
- School of Electrical & Electronic Engineering (EEE), Nanyang Technological University (NTU), 50 Nanyang Avenue, Singapore 639798
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Dauwels J, Vialatte F, Latchoumane C, Jeong J, Cichocki A. EEG synchrony analysis for early diagnosis of Alzheimer's disease: a study with several synchrony measures and EEG data sets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2009:2224-7. [PMID: 19964954 DOI: 10.1109/iembs.2009.5334862] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
It has frequently been reported in the medical literature that the EEG of Alzheimer disease (AD) patients is less synchronous than in healthy subjects. In this paper, it is explored whether loss in EEG synchrony can be used to diagnose AD at an early stage. Multiple synchrony measures are applied to two different EEG data sets: (1) EEG of pre-dementia patients and control subjects; (2) EEG of mild AD patients and control subjects; the two data sets are from different patients, different hospitals, and obtained through different recording systems. It is observed that both Granger causality and stochastic event synchrony indicate statistically significant loss of EEG synchrony, for the two data sets; those two synchrony measures are then combined as features in linear and quadratic discriminant analysis (with crossvalidation), yielding classification rates of 83% and 88% for the pre-dementia data set and mild AD data set respectively. These results suggest that loss in EEG synchrony is indicative for early AD.
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Affiliation(s)
- Justin Dauwels
- Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Dauwels J, Vialatte F, Musha T, Cichocki A. A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG. Neuroimage 2010; 49:668-93. [PMID: 19573607 DOI: 10.1016/j.neuroimage.2009.06.056] [Citation(s) in RCA: 222] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2009] [Revised: 06/08/2009] [Accepted: 06/17/2009] [Indexed: 11/18/2022] Open
Affiliation(s)
- J Dauwels
- Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Vialatte FB, Dauwels J, Maurice M, Yamaguchi Y, Cichocki A. On the synchrony of steady state visual evoked potentials and oscillatory burst events. Cogn Neurodyn 2009; 3:251-61. [PMID: 19326256 PMCID: PMC2727163 DOI: 10.1007/s11571-009-9082-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2008] [Revised: 03/09/2009] [Accepted: 03/09/2009] [Indexed: 11/29/2022] Open
Abstract
In this paper, we investigate the large-scale synchrony of EEG oscillatory bursts, during stimulation by a flickering square of light. Whereas most studies focus on averaged raw EEG responses, this study considers oscillatory events within EEG of single trials, which leads to various new insights. We recorded EEG signals before, during and after stimulation by a flickering square of light in medium (16 Hz) and high frequency (32 Hz) ranges. Similar oscillatory bursts, to those observed in spontaneous EEG, can be found in single-trial synchrony of steady state visual evoked potentials (SSVEP). These bursts are extracted from the EEG of single trials using bump modeling. Stochastic event synchrony method is applied to those events, which quantifies synchronies of oscillatory bursts on a large-scale basis. Those oscillatory patterns have a significantly higher degree of co-occurrence during SSVEP, uncorrelated with ongoing signal synchrony. It means that EEG oscillatory patterns are presumably an outcome of brain activity, rather than a mere side effect of ongoing EEG. They undergo a consistent reorganization during visual stimulation, preferentially along the visual pathway, depending on magno or parvo stimulations. Flickering stimuli may induce some cognitive side-effects depending on the stimulation frequency.
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Affiliation(s)
- Francois B Vialatte
- Lab. for Advanced Brain Signal Processing (L.ABSP), RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-Shi, Saitama-Ken, 351-0198, Japan,
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Vialatte FB, Solé-Casals J, Dauwels J, Maurice M, Cichocki A. Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals. BMC Neurosci 2009; 10:46. [PMID: 19432999 PMCID: PMC2690596 DOI: 10.1186/1471-2202-10-46] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2008] [Accepted: 05/12/2009] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity - especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. RESULTS in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. CONCLUSION The tool is publicly available as a freeware at the address: http://www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html.
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