Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition

Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; EspañaFil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; EspañaFil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; Españ

Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics

Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.Facultad de IngenieríaCentro de Investigaciones Óptica

Entropy Analysis of Univariate Biomedical Signals:Review and Comparison of Methods

International audienc

CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals

Background: We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. Methods: Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. Results: The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ (‘tau’) where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded at https://bitbucket.org/deepak_panday/ceps/src/pipeline_v2/. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. Conclusions: We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing

Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations

[EN] Despite its widely demonstrated usefulness, there is still room for improvement in the basic Permutation Entropy (PE) algorithm, as several subsequent studies have proposed in the recent years. For example, some improved PE variants try to address possible PE weaknesses, such as its only focus on ordinal information, and not on amplitude, or the possible detrimental impact of equal values in subsequences due to motif ambiguity. Other evolved PE methods try to reduce the influence of input parameters. A good representative of this last point is the Bubble Entropy (BE) method. BE is based on sorting relations instead of ordinal patterns, and its promising capabilities have not been extensively assessed yet. The objective of the present study was to comparatively assess the classification performance of this new method, and study and exploit the possible synergies between PE and BE. The claimed superior performance of BE over PE was first evaluated by conducting a series of time series classification tests over a varied and diverse experimental set. The results of this assessment apparently suggested that there is a complementary relationship between PE and BE, instead of a superior/inferior relationship. A second set of experiments using PE and BE simultaneously as the input features of a clustering algorithm, demonstrated that with a proper algorithm configuration, classification accuracy and robustness can benefit from both measures.Cuesta Frau, D.; Vargas-Rojo, B. (2020). Permutation Entropy and Bubble Entropy: Possible interactions and synergies between order and sorting relations. Mathematical Biosciences and Engineering. 17(2):1637-1658. https://doi.org/10.3934/mbe.2020086S163716581721. C. Bandt and B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett., 88 (2002), 174102.2. M. Zanin, L. Zunino, O. A. Rosso and D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review, Entropy, 14 (2012), 1553-1577.14. F. Siokis, Credit market jitters in the course of the financial crisis: A permutation entropy approach in measuring informational efficiency in financial assets, Phys. A Statist. Mechan. Appl., 499 (2018).15. A. F. Bariviera, L. Zunino, M. B. Guercio, L. Martinez and O. Rosso, Efficiency and credit ratings: A permutation-information-theory analysis, J. Statist. Mechan. Theory Exper., 2013 (2013), P08007.16. A. F. Bariviera, M. B. Guercio, L. Martinez and O. Rosso, A permutation information theory tour through different interest rate maturities: the libor case, Philos. Transact. Royal Soc. A Math. Phys. Eng. Sci., 373 (2015).20. B. Fadlallah, B. Chen, A. Keil and J. Príncipe, Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information, Phys. Rev. E, 87 (2013), 022911.Deng, B., Cai, L., Li, S., Wang, R., Yu, H., Chen, Y., & Wang, J. (2016). Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer’s disease. Cognitive Neurodynamics, 11(3), 217-231. doi:10.1007/s11571-016-9418-924. D. Cuesta-Frau, Permutation entropy: Influence of amplitude information on time series classification performance, Math. Biosci. Eng., 5 (2019), 1-16.25. F. Traversaro, M. Risk, O. Rosso and F. Redelico, An empirical evaluation of alternative methods of estimation for Permutation Entropy in time series with tied values, arXiv e-prints, arXiv:1707.01517 (2017).26. D. Cuesta-Frau, M. Varela-Entrecanales, A. Molina-Picó and B. Vargas, Patterns with equal values in permutation entropy: Do they really matter for biosignal classification?, Complexity, 2018 (2018), 1-15.29. D. Cuesta-Frau, A. Molina-Picó, B. Vargas and P. González, Permutation entropy: Enhancing discriminating power by using relative frequencies vector of ordinal patterns instead of their shannon entropy, Entropy, 21 (2019).30. H. Azami and J. Escudero, Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation, Comput. Meth. Program. Biomed., 128 (2016), 40-51.32. G. Manis, M. Aktaruzzaman and R. Sassi, Bubble entropy: An entropy almost free of parameters, IEEE Transact. Biomed. Eng., 64 (2017), 2711-2718.34. L. Zunino, F. Olivares, F. Scholkmann and O. A. Rosso, Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions, Phys. Lett. A, 381 (2017), 1883-1892.38. D. E. Lake, J. S. Richman, M. P. Griffin and J. R. Moorman, Sample entropy analysis of neonatal heart rate variability, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 283 (2002), R789-R797, PMID: 12185014.41. I. Unal, Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach, Comput. Math. Methods Med., 2017 (2017), 14.47. A. K. Jain, M. N. Murty and P. J. Flynn, Data clustering: A review, ACM Comput. Surv., 31 (1999), 264-323.51. J. Sander, M. Ester, H.-P. Kriegel and X. Xu, Density-based clustering in spatial databases: The algorithm gdbscan and its applications, Data Min. Knowl. Discov., 2 (1998), 169-194.52. J. Wu, Advances in K-means Clustering: A Data Mining Thinking, Springer Publishing Company, Incorporated, 2012.53. S. Panda, S. Sahu, P. Jena and S. Chattopadhyay, Comparing fuzzy-c means and k-means clustering techniques: A comprehensive study, in Advances in Computer Science, Engineering & Applications (eds. D. C. Wyld, J. Zizka and D. Nagamalai), Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, 451-460.54. A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, et al., PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals, Circulation, 101 (2000), 215-220.58. R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. E. Elger, Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64 (2001), 061907.60. N. Iyengar, C. K. Peng, R. Morin, A. L. Goldberger and L. A. Lipsitz, Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics, Am. J. Physiology-Regulatory Integrat. Comparat. Physiol., 271 (1996), R1078-R1084, PMID: 8898003

Brain entropy, fractal dimensions and predictability: a review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Development of nonlinear techniques based on time-frequency representation and information theory for the analysis of EEG signals to assess different states of consciousness

Electroencephalogram (EEG) recordings provide insight into the changes in brain activity associated with various states of anesthesia, epilepsy, brain attentiveness, sleep disorders, brain disorders, etc. EEG's are complex signals whose statistical properties depend on both space and time. Their randomness and non-stationary characteristics make them impossible to be described in an accurate way with a simple technique, requiring analysis and characterization involves techniques that take into account their non-stationarity. For that, new advanced techniques in order to improve the efficiency of the EEG based methods used in the clinical practice have to be developed. The main objective of this thesis was to investigate and implement different methods based on nonlinear techniques in order to develop indexes able to characterize the frequency spectrum, the nonlinear dynamics and the complexity of the EEG signals recorded in different state of consciousness. Firstly, a new method for removing peak and spike in biological signal based on the signal envelope was successfully designed and applied to simulated and real EEG signals, obtaining performances significantly better than the traditional adaptive filters. Then, several studies were carried out in order to extract and evaluate EEG measures based on nonlinear techniques in different contexts such as the automatic detection of sleepiness and the characterization and prediction of the nociceptive stimuli and the assessment of the sedation level. Four novel indexes were defined by calculating entropy of the Choi-Williams distribution (CWD) with respect to time or frequency, by using the probability mass function at each time instant taken independently or by using the probability mass function of the entire CWD. The values of these indexes tend to decrease, with different proportion, when the behavior of the signals evolved from chaos or randomness to periodicity and present differences when comparing EEG recorded in eyes-open and eyes-closed states and in ictal and non-ictal states. Measures obtained with time-frequency representation, mutual information function and correntropy, were applied to EEG signals for the automatic sleepiness detection in patients suffering sleep disorders. The group of patients with excessive daytime sleepiness presented more power in ¿ band than the group without sleepiness, which presented higher spectral and cross-spectral entropy in the frontal zone in d band. More complexity in the occipital zone was found in the group of patients without sleepiness in ß band, while a stronger nonlinear coupling between the occipital and frontal regions was detected in patients with excessive daytime sleepiness, in ß band. Time-frequency representation and non-linear measures were also used in order to study how adaptation and fatigue affect the event-related brain potentials to stimuli of different modalities. Differences between the responses to infrequent and frequent stimulation in different recording periods were found in series of averaged EEG epochs recorded after thermal, electrical and auditory stimulation. Nonlinear measures calculated on EEG filtered in the traditional frequency bands and in higher frequency bands improved the assessment of the sedation level. These measures were obtained by applying all the developed techniques on signals recorded from patients sedated, in order to predict the responses to pain stimulation such as nail bad compression and endoscopy tube insertion. The proposed measures exhibit better performances than the bispectral index (BIS), a traditional indexes used for hypnosis assessment. In conclusion, nonlinear measures based on time-frequency representation, mutual information functions and correntropy provided additional information that helped to improve the automatic sleepiness detection, the characterization and prediction of the nociceptive responses and thus the assessment of the sedation level.El registro de la señal Electroencefalografíca (EEG) proporciona información sobre los cambios en la actividad cerebral asociados con varios estados de la anestesia, la epilepsia, la atención cerebral, los trastornos del sueño, los trastornos cerebrales, etc. Los EEG son señales complejas cuyas propiedades estadísticas dependen del espacio y del tiempo. Sus características aleatorias y no estacionarias hacen imposible que el EEG se describa de forma precisa con una técnica sencilla requiriendo un análisis y una caracterización que implica técnicas que tengan en cuenta su no estacionariedad. Todo esto aumenta la necesidad de desarrollar nuevas técnicas avanzadas con el fin de mejorar la eficiencia de los métodos utilizados en la práctica clínica que son basados en el análisis de EEG. En esta tesis se han investigado y aplicado diferentes métodos utilizando técnicas no lineales con el fin de desarrollar índices capaces de caracterizar el espectro de frecuencias, la dinámica no lineal y la complejidad de las señales EEG registradas en diferentes estados de conciencia. En primer lugar, se ha desarrollado un nuevo algoritmo basado en la envolvente de la señal para la eliminación de ruido de picos en las señales biológicas. Este algoritmo ha sido aplicado a señales simuladas y reales obteniendo resultados significativamente mejores comparados con los filtros adaptativos tradicionales. Seguidamente, se han llevado a cabo varios estudios con el fin de extraer y evaluar las medidas de EEG basadas en técnicas no lineales en diferentes contextos. Se han definido nuevos índices mediante el cálculo de la entropía de la distribución de Choi-Williams (DCW) con respecto al tiempo o la frecuencia. Se ha observado que los valores de estos índices tienden a disminuir, en diferentes proporciones, cuando el comportamiento de las señales evoluciona de caótico o aleatorio a periódico. Además, se han encontrado valores diferentes de estos índices aplicados a la señal EEG registrada en diferentes estados. Diferentes medidas basadas en la representación tiempo-frecuencia, la función de información mutua y la correntropia se han aplicado al EEG para la detección automática de la somnolencia en pacientes que sufren trastornos del sueño. Se ha observado en la zona frontal que la potencia en la banda θ es mayor en los pacientes con somnolencia diurna excesiva, mientras que la entropía espectral y la entropía espectral cruzada en la banda δ es mayor en los pacientes sin somnolencia. En el grupo sin somnolencia se ha encontrado más complejidad en la zona occipital, mientras que el acoplamiento no lineal entre las regiones occipital y frontal ha resultado más fuerte en pacientes con somnolencia diurna excesiva, en la banda β. La representación tiempo-frecuencia y las medidas no lineales se han utilizado para estudiar cómo la adaptación y la fatiga afectan a los potenciales cerebrales relacionados con estímulos térmicos, eléctricos y auditivos. Analizando el promedio de varias épocas de EEG grabadas después de la estimulación, se han encontrado diferencias entre las respuestas a la estimulación frecuente e infrecuente en diferentes períodos de registro. Todas las técnicas que se han desarrollado, se han aplicado a señales EEG registradas en pacientes sedados, con el fin de predecir las respuestas a la estimulación del dolor. Un conjunto de medidas calculadas en señales EEG filtradas en diferentes bandas de frecuencia ha permitido mejorar la evaluación del nivel de sedación. Las medidas propuestas han presentado un mejor rendimiento comparado con el índice bispectral, un indicador de hipnosis tradicional. En conclusión, las medidas no lineales basadas en la representación tiempofrecuencia, funciones de información mutua y correntropia han proporcionado informaciones adicionales que contribuyeron a mejorar la detección automática de la somnolencia, la caracterización y predicción de las respuestas nociceptivas y por lo tanto la evaluación del nivel de sedación

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy

Background: As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. Methods: To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190–220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). Results: FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1–5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. Conclusion: The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data