Sympathetic determinants of resting blood pressure in males and females.

Discharge of postganglionic muscle sympathetic nerve activity (MSNA) is related poorly to blood pressure (BP) in adults. Whether neural measurements beyond the prevailing level of MSNA can account for interindividual differences in BP remains unclear. The current study sought to evaluate the relative contributions of sympathetic-BP transduction and sympathetic baroreflex gain on resting BP in young adults. Data were analyzed from 191 (77 females) young adults (18-39 years) who underwent continuous measurement of beat-to-beat BP (finger photoplethysmography), heart rate (electrocardiography), and fibular nerve MSNA (microneurography). Linear regression analyses were computed to determine associations between sympathetic-BP transduction (signal-averaging) or sympathetic baroreflex gain (threshold technique) and resting BP, before and after controlling for age, body mass index, and MSNA burst frequency. K-mean clustering was used to explore sympathetic phenotypes of BP control and consequential influence on resting BP. Sympathetic-BP transduction was unrelated to BP in males or females (both R2 < 0.01; P > 0.67). Sympathetic baroreflex gain was positively associated with BP in males (R2 = 0.09, P < 0.01), but not in females (R2 < 0.01; P = 0.80), before and after controlling for age, body mass index, and MSNA burst frequency. K-means clustering identified a subset of participants with average resting MSNA, yet lower sympathetic-BP transduction and lower sympathetic baroreflex gain. This distinct subgroup presented with elevated BP in males (P < 0.02), but not in females (P = 0.10). Sympathetic-BP transduction is unrelated to resting BP, while the association between sympathetic baroreflex gain and resting BP in males reveals important sex differences in the sympathetic determination of resting BP.NEW & NOTEWORTHY In a sample of 191 normotensive young adults, we confirm that resting muscle sympathetic nerve activity is a poor predictor of resting blood pressure and now demonstrate that sympathetic baroreflex gain is associated with resting blood pressure in males but not females. In contrast, signal-averaged measures of sympathetic-blood pressure transduction are unrelated to resting blood pressure. These findings highlight sex differences in the neural regulation of blood pressure.

Signal-averaged resting sympathetic transduction of blood pressure: is it time to account for prevailing muscle sympathetic burst frequency?

Calculating the blood pressure (BP) response to a burst of muscle sympathetic nerve activity (MSNA), termed sympathetic transduction, may be influenced by an individual's resting burst frequency. We examined the relationships between sympathetic transduction and MSNA in 107 healthy males and females and developed a normalized sympathetic transduction metric to incorporate resting MSNA. Burst-triggered signal averaging was used to calculate the peak diastolic BP response following each MSNA burst (sympathetic transduction of BP) and following incorporation of MSNA burst cluster patterns and amplitudes (sympathetic transduction slope). MSNA burst frequency was negatively correlated with sympathetic transduction of BP (r = -0.42; P < 0.01) and the sympathetic transduction slope (r = -0.66; P < 0.01), independent of sex. MSNA burst amplitude was unrelated to sympathetic transduction of BP in males (r = 0.04; P = 0.78), but positively correlated in females (r = 0.44; P < 0.01) and with the sympathetic transduction slope in all participants (r = 0.42; P < 0.01). To control for MSNA, the linear regression slope of the log-log relationship between sympathetic transduction and MSNA burst frequency was used as a correction exponent. In subanalysis of males (38 ± 10 vs. 14 ± 4 bursts/min) and females (28 ± 5 vs. 12 ± 4 bursts/min) with high versus low MSNA, sympathetic transduction of BP and sympathetic transduction slope were lower in participants with high MSNA (all P < 0.05). In contrast, normalized sympathetic transduction of BP and normalized sympathetic transduction slope were similar in males and females with high versus low MSNA (all P > 0.22). We propose that incorporating MSNA burst frequency into the calculation of sympathetic transduction will allow comparisons between participants with varying levels of resting MSNA.

Feature Extraction of Epileptic EEG using Wavelet Power Spectra and Functional PCA.

In this work, we propose a novel approach to analyze Epileptic EEG signals using wavelet power spectra and functional principal component analysis. Both continuous and discrete wavelet power spectra are considered. By transforming EEG signals into power spectra, we significantly enhance the functionality of random signals, which makes functional principal component analysis be a suitable technique for further extracting key signal features. We have tested our proposed method using a set of publicly available epileptic EEG. The obtained results demonstrate that wavelet power spectra and functional principal component analysis are promising for feature extraction of epileptic EEG, and they may be useful for epilepsy diagnosis problem.

Dynamic principal component analysis with nonoverlapping moving window and its applications to epileptic EEG classification.

Classification of electroencephalography (EEG) is the most useful diagnostic and monitoring procedure for epilepsy study. A reliable algorithm that can be easily implemented is the key to this procedure. In this paper a novel signal feature extraction method based on dynamic principal component analysis and nonoverlapping moving window is proposed. Along with this new technique, two detection methods based on extracted sparse features are applied to deal with signal classification. The obtained results demonstrated that our proposed methodologies are able to differentiate EEGs from controls and interictal for epilepsy diagnosis and to separate EEGs from interictal and ictal for seizure detection. Our approach yields high classification accuracy for both single-channel short-term EEGs and multichannel long-term EEGs. The classification performance of the method is also compared with other state-of-the-art techniques on the same datasets and the effect of signal variability on the presented methods is also studied.

Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis.

In epilepsy diagnosis or epileptic seizure detection, much effort has been focused on finding effective combination of feature extraction and classification methods. In this paper, we develop a wavelet-based sparse functional linear model for representation of EEG signals. The aim of this modeling approach is to capture discriminative random components of EEG signals using wavelet variances. To achieve this goal, a forward search algorithm is proposed for determination of an appropriate wavelet decomposition level. Two EEG databases from University of Bonn and University of Freiburg are used for illustration of applicability of the proposed method to both epilepsy diagnosis and epileptic seizure detection problems. For this data considered, we show that wavelet-based sparse functional linear model with a simple classifier such as 1-NN classification method leads to higher classification results than those obtained using other complicated methods such as support vector machine. This approach produces a 100% classification accuracy for various classification tasks using the EEG database from University of Bonn, and outperforms many other state-of-the-art techniques. The proposed classification scheme leads to 99% overall classification accuracy for the EEG data from University of Freiburg.

Signal feature extraction by multi-scale PCA and its application to respiratory sound classification.

Respiratory sound (RS) signals carry significant information about the underlying functioning of the pulmonary system by the presence of adventitious sounds. Although many studies have addressed the problem of pathological RS classification, only a limited number of scientific works have focused in multi-scale analysis. This paper proposes a new signal classification scheme for various types of RS based on multi-scale principal component analysis as a signal enhancement and feature extraction method to capture major variability of Fourier power spectra of the signal. Since we classify RS signals in a high dimensional feature subspace, a new classification method, called empirical classification, is developed for further signal dimension reduction in the classification step and has been shown to be more robust and outperform other simple classifiers. An overall accuracy of 98.34% for the classification of 689 real RS recording segments shows the promising performance of the presented method.

Sparse approximation of long-term biomedical signals for classification via dynamic PCA.

Sparse approximation is a novel technique in applications of event detection problems to long-term complex biomedical signals. It involves simplifying the extent of resources required to describe a large set of data sufficiently for classification. In this paper, we propose a multivariate statistical approach using dynamic principal component analysis along with the non-overlapping moving window technique to extract feature information from univariate long-term observational signals. Within the dynamic PCA framework, a few principal components plus the energy measure of signals in principal component subspace are highly promising for applying event detection problems to both stationary and non-stationary signals. The proposed method has been first tested using synthetic databases which contain various representative signals. The effectiveness of the method is then verified with real EEG signals for the purpose of epilepsy diagnosis and epileptic seizure detection. This sparse method produces a 100% classification accuracy for both synthetic data and real single channel EEG data.