Optimized Symbolic Dynamics Approach for the Analysis of the Respiratory Pattern

The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis

In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers' inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.

Assessment of Joint Interactions between Respiration and Baroreflex Activity using Joint Symbolic Dynamics in Heart Failure Patients

In this paper we employed a novel approach based on joint symbolic dynamics (JSD) to study interaction between respiratory phase and baroreflex activity. Electrocardiogram (ECG) and blood pressure recordings from six participants with history of heart failure were included in this study. First, the ECG R-peaks and systolic blood pressure indices were detected using parabolic fitting. Second, the respiratory signal was derived from Frank orthogonal ECG leads using QRS slopes and R-wave angles. Third, time series of R-R intervals and systolic blood pressure (SBP) were extracted, and respiratory phases were obtained using the Hilbert transform. Subsequently, each series was transformed into binary symbol vectors based on their successive changes and words of length '2' were formed. Baroreflex patterns were studied using word combinations representing baroreflex activity for specific changes in respiratory phases. Baroreflex activity was significantly higher for alternating low-high/high-low heart rate and SBP during inspiration as compared to continuous increase or decrease in heart rate and SBP ([Formula: see text], w^RP=11: 39.1±9.3% vs. [Formula: see text], w^RP=11: 6.4±3.9%, p<0.0001).

A scalable distribution network risk evaluation framework via symbolic dynamics

Background

Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk.

Methods

This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method.

Conclusion

Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic.

Effect of resistive inspiratory and expiratory loading on cardio-respiratory interaction in healthy subjects

Resistive loading affects the breathing pattern and causes an increase in negative intrathoracic pressure. The aim of this paper was to study the influence inspiratory and expiratory loading on cardio-respiratory interaction. We recorded electrocardiogram (ECG) and respiratory inductance plethysmogram (RIP) in 11 healthy male subjects under normal and resistive loading conditions. The R-R time series were extracted from the ECG and respiratory phases were calculated from the ribcage and abdominal RIP using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length `3 digits' were formed and the correspondence between words of the two series was determined to quantify cardio-respiratory interaction. Adding inspiratory and expiratory resistive loads resulted in an increase in inspiratory and expiatory time, respectively. Furthermore, we observed a significant increase in cardio-respiratory interaction during inspiratory resistive loading as compared to expiratory resistive loading (ribcage: 22.1±7.2 vs. 12.5±4.3 %, p<;0.0001; abdomen: 18.8±8.5 vs. 12.1±3.1 %, p<;0.05, respectively). Further studies may aid in better understanding the underlying physiological mechanisms and management of patients with breathing disorders.

Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance

In this study, the best combination of short-term heart rate variability (HRV) measures was investigated to distinguish 29 patients with congestive heart failure from 54 healthy subjects in the control group. In the analysis performed, wavelet packet transform based frequency-domain measures and several non-linear parameters were used in addition to standard HRV measures. The backward elimination and unpaired statistical analysis methods were used to select the best one among all possible combinations of these measures. Five distinct typical classifiers with different parameters were evaluated in discriminating these two groups using the leave-one-out cross validation method. Each algorithm was tested 30 times to determine the repeatability of the results. The results imply that the backward elimination method gives better performance when compared to the statistical significance method in the feature selection stage. The best performance (82.75%, 96.29%, and 91.56% for the sensitivity, specificity, and accuracy) was obtained by using the SVM classifier with 27 selected features including non-linear and wavelet-based measures.

Effect of spontaneous arousals on cardio-respiratory interaction in healthy children

The aim of this paper was to study the effect of spontaneous arousals during night-time sleep on the interactions between R-R intervals and respiratory phases in healthy children. We collected overnight polysomnography data of 40 healthy children and investigated cardio-respiratory interaction before and after spontaneous arousals during stage 2 sleep using joint symbolic dynamics. The R-R time series were extracted from electrocardiograms (ECG) and respiratory phases were obtained from abdominal sensors using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length '2' were formed and the correspondence between words of the two series was determined to quantify cardio-respiratory interaction for pre- and post-spontaneous arousal episodes. We observed a brief but significant shortening in R-R and respiratory intervals after arousal. There was also a significant short-term increase in cardio-respiratory interaction during the first 30-second post-arousal episode as compared to 30-second pre-arousal episode (7.5±3.4 vs. 5.2±3.7%, p&#60;0.0001, respectively). In conclusion, spontaneous arousals in healthy children during night-time sleep are associated with a temporal but significant increase in cardio-respiratory interaction.

Quantification of cardio-respiratory interactions in healthy children during night-time sleep using joint symbolic dynamics

The aim of this paper was to study interactions between R-R intervals and respiratory phases in healthy children during night-time sleep using a novel technique based on joint symbolic dynamics. We investigated overnight polysomnography data of 40 healthy children. The R-R time series were extracted from electrocardiograms (ECG) and respiratory phases were obtained from abdominal sensors using the Hilbert transform. Both the series were transformed into ternary symbol vectors based on the changes between two successive R-R intervals or respiratory phases, respectively. Subsequently, words of length '2' were formed and the correspondence between words of the two series for each sleep stage was determined to quantify cardio-respiratory interaction. We found a significantly higher percentage of similarity in the joint symbolic dynamics of R-R intervals and respiratory phases during slow-wave (SW) sleep compared to any other sleep stage. There was, however, no significant effect of age, gender or BMI on cardio-respiratory interaction. In conclusion, joint symbolic dynamics provides a novel efficient technique for the analysis of cardio-respiratory interaction.

Symbolic dynamic analysis of relations between cardiac and breathing cycles in patients on weaning trials

Traditional time-domain techniques of data analysis are often not sufficient to characterize the complex dynamics of the cardiorespiratory interdependencies during the weaning trials. In this paper, the interactions between the heart rate (HR) and the breathing rate (BR) were studied using joint symbolic dynamic analysis. A total of 133 patients on weaning trials from mechanical ventilation were analyzed: 94 patients with successful weaning (group S) and 39 patients that failed to maintain spontaneous breathing (group F). The word distribution matrix enabled a coarse-grained quantitative assessment of short-term nonlinear analysis of the cardiorespiratory interactions. The histogram of the occurrence probability of the cardiorespiratory words presented a higher homogeneity in group F than in group S, measured with a higher number of forbidden words in group S as well as a higher number of words whose probability of occurrence is higher than a probability threshold in group S. The discriminant analysis revealed the best results when applying symbolic dynamic variables. Therefore, we hypothesize that joint symbolic dynamic analysis provides enhanced information about different interactions between HR and BR, when comparing patients with successful weaning and patients that failed to maintain spontaneous breathing in the weaning procedure.

Time-frequency analysis of cardiac and respiratory parameters for the prediction of ventilator weaning

Mechanical ventilators are used to provide life support in patients with respiratory failure. Assessing autonomic control during the ventilator weaning provides information about physiopathological imbalances. Autonomic parameters can be derived and used to predict success in discontinuing from the mechanical support. Time-frequency analysis is used to derive cardiac and respiratory parameters, as well as their evolution in time, during ventilator weaning in 130 patients. Statistically significant differences have been observed in autonomic parameters between patients who are considered ready for spontaneous breathing and patients who are not. A classification based on respiratory frequency, heart rate and heart rate variability spectral components has been proposed and has been able to correctly classify more than 80% of the cases.