Recurrent Quantification Analysis Based Study of Wide-Field Calcium Imaging Data in Relation to Brain's Wake/Sleep Condition

Wide-Field Calcium Images (WFCI) directly reflect neuronal excitation, but their poor frame rate could be a drawback for time series analysis. This work was aimed at exploring the diagnostic capability retained by a time series obtained from calcium imaging data. To that purpose, we analyzed publicly available data from 2.88 hour continuous recordings of calcium images obtained from seven mice at different wake/sleep stages. Data were obtained from the Physionet portal and were submitted to Recurrence Quantification Analysis (RQA). The association between retrosplenial and parietal areas was also assessed. Nonlinear RQA analysis allowed to identify the right retrosplenial and parietal areas as particularly sensitive to changes in sleep walking condition. Specifically, our results suggested that the RQA feature lmean decreases in non-REM sleep_1 stage as compared to waking stage. Sleep (both sleep_1 stage and REM) apparently elicits an increase in the association between retrosplenial and parietal areas. Overall, these results suggest that RQA and association analysis are appropriate to assess modifications associated to changes in brain condition, in spite of the low sampling rate of WFCI signals.

Las Imágenes de Calcio de Campo Ancho (Wide-Field Calcium Images, WFCI) reflejan directamente la excitación neuronal, pero su escasa resolución temporal pudiera resultar un impedimento para el análisis de series temporales. El presente trabajo tuvo por finalidad explorar la capacidad diagnostica que retiene una serie temporal extraída de imágenes de calcio. Para ello, se estudió una base de datos disponible en la red que contiene registros de 2.88 horas de duración de imágenes de calcio correspondientes a 7 ratones transgénicos a diferentes estadios de sueño/vigilia. Los datos fueron descargados del portal Physionet y sometidos a Análisis de Cuantificación Recurrente (Recurrent Quantification Analysis, RQA). La asociación entre las áreas retrosplenial y parietal derechas fue también evaluada. El análisis no lineal mediante RQA permitió identificar las áreas retrosplenial y parietal derechas como zonas particularmente sensibles a cambios en el estado de sueño/vigilia. Específicamente, nuestros resultados sugieren que el índice l mean se redujo en el estadio 1 de sueño no REM en comparación con el estado de vigilia. El estado de sueño, tanto REM como no-REM aparentemente induce un reforzamiento en la apreciación entre las áreas retrosplenial y parietal derechas. En su conjunto, estos resultados apuntan que el análisis de RQA y de asociación entre áreas son pertinentes para sensar las modificaciones asociadas a cambios en el estado del cerebro, a pesar de la baja resolución temporal de las señales WFCI.

Electroencephalogram feature extraction and classification of autistic children based on recurrence quantification analysis / 生物医学工程学杂志

Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.

The temporal stability of recurrence quantification analysis attributes from chronic atrial fibrillation electrograms

Abstract Introduction The temporal behavior of atrial electrograms (AEGs) collected during persistent atrial fibrillation (persAF) directly affects ablative treatment outcomes. We investigated different durations of AEGs collected during persAF using recurrence quantification analysis (RQA). Methods 797 bipolar AEGs with different durations (from 0.5 s to 8 s) from 18 patients were investigated. Four RQA-based attributes were evaluated based on AEG durations: determinism (DET); recurrence rate (RR); laminarity (LAM); and diagonal lines' entropy (ENTR). The Spearman correlation (ρ) between each duration versus 8 s was calculated. AEG classification was performed following the CARTO criteria (Biosense Webster) and receiving operating characteristic (ROC) curves were created for the RQA variables. Results The RQA variables successfully discriminated the AEGs: the area under the ROC curves were as high as 0.70 for AEGs with 3.5 s or greater. Three types of AEGs were found using these variables: normal, fractionated and temporally unstable. The number of unstable AEGs decreased with longer AEG segments. Different AEG durations significantly affected the RQA variables (P<0.0001), with no statistical difference between the durations 6 s, 7 s and 8 s for DET, LAM and ENTR, and no difference between 7 s and 8 s for RR (P<0.0001). AEGs with 3 s or longer have shown ρ ≥ 80% for all variables. Conclusion The RQA variables have been shown effective in the characterization of AEGs collected during persAF with a shorter duration than current recommendations, which motivates their use for the characterization of atrial substrate during persAF ablation.

Estimación de la edad cardiovascular a partir de tacogramas de 2 minutos de duración mediante Análisis de Cuantificación de Recurrencias / Estimation of cardiovascular age from 2 minute tachograms via Recurrence Quantification Analysis

Se han reportado diferentes intentos de estimar la edad cardiovascular a partir de señales electrofisiológicas. Las propuestas publicadas presentan inconvenientes para su aplicación en la práctica clínica. Nos hemos propuesto estimar la edad cardiovascular a partir de tacogramas de 2 minutos de duración utilizando una medida no lineal (logaritmo del porcentaje de determinismo, log (DET)), obtenido a partir del Análisis de Cuantificación de Recurrencias. En una muestra de 62 sujetos presuntamente sanos con edades entre 9 y 79 años se obtuvo una excelente correlación entre edad y log (DET) ) r = 0.7981, comparable a los mejores valores reportados en la literatura utilizando técnicas más costosas. A partir de esos resultados se propone una vía para estimar los valores normativos para una población presuntamente sana. Se concluye que a partir de tacogramas de dos minutos de duración es posible obtener un buen estimador de la edad cardiovascular. El Análisis de Cuantificación de Recurrencias es una vía apropiada para estimar la edad cardiovascular, y la pérdida del porcentaje de determinismo con la edad posiblemente refleje una mayor contribución de los componentes estocásticos a la regulación del ritmo cardiaco en ancianos(AU)

Various attempts have been reported to estimate the cardiovascular age from different electrophysiological signals. Proposals published are hampered by drawbacks for application in clinical practice. We have proposed to estimate cardiovascular age from two- minutes tachograms using a non-linear measure (logarithm of the percentage of determinism, log (DET)) obtained from Recurrence Quantification Analysis. In a sample of 62 allegedly healthy subjects,9 to 79 years an excellent correlation between age and log (DET)) r = 0.7981 - comparable to the best values reported in the literature using more expensive techniques- was obtained. From these results the way is proposed to estimate the normative values for a presumably healthy population. It is concluded that from two-minutes tachograms it is possible to obtain a good estimate of the cardiovascular age. Recurrence Quantification Analysis of is an appropriate way to estimate cardiovascular age, and reduction in the percentage of determinism with age may reflect a greater contribution of stochastic regulation of heart rate components in the elderly subject(AU)