Temporal dynamics of the circadian heart rate following low and high volume exercise training in sedentary male subjects.

PURPOSE: Increased risk of arrhythmic events occurs at certain times during the circadian cycle with the highest risk being in the second and fourth quarter of the day. Exercise improves treatment outcome in individuals with cardiovascular disease. How different exercise protocols affect the circadian rhythm and the associated decrease in adverse cardiovascular risk over the circadian cycle has not been shown. METHODS: Fifty sedentary male participants were randomized into an 8-week high volume and moderate volume training and a control group. Heart rate was recorded using Polar Electronics and investigated with Cosinor analysis and by Poincaré plot derived features of SD1, SD2 and the complex correlation measure (CCM) at 1-h intervals over the 24-h period. RESULTS: Moderate exercise significantly increased vagal modulation and the temporal dynamics of the heart rate in the second quarter of the circadian cycle (p = 0.004 and p = 0.007 respectively). High volume exercise had a similar effect on vagal output (p = 0.003) and temporal dynamics (p = 0.003). Cosinor analysis confirms that the circadian heart rate displays a shift in the acrophage following moderate and high volume exercise from before waking (1st quarter) to after waking (2nd quarter of day). CONCLUSIONS: Our results suggest that exercise shifts vagal influence and increases temporal dynamics of the heart rate to the 2nd quarter of the day and suggest that this may be the underlying physiological change leading to a decrease in adverse arrhythmic events during this otherwise high-risk period.

Surface EMG based hand gesture identification using semi blind ICA: validation of ICA matrix analysis.

Surface electromyogram (sEMG) has numerous applications. It has been widely used in various biosignal and neuro rehabilitation applications. There is an urgent need for establishing a simple yet robust system that can be used to identify subtle complex hand actions and gestures for control of prosthesis and other computer assisted devices. Earlier work to identify the hand actions and gestures based on sEMG suffers from limitation that these are suitable for gross actions where there is only one prime-mover muscle involved and not suitable for small subtle and complex muscle contraction. This paper presents the hand gesture identification using sEMG decomposed using semi-blind independent component analysis combined with neural network based classifier. The aim was to provide reliable and natural control for rehabilitation and human computer interaction applications. We have proposed a model based approach where the hand muscle anatomy is known. The system was tested on 5 subjects and with experiments repeated on different days. The system was compared with raw sEMG as used by other researchers. The system is able to classify the different hand actions 100%. In comparison, the classification of the traditional ICA and raw sEMG for the same experiments and similar features was a poor 65% and 60% respectively. This research demonstrates that sEMG can be decomposed to the individual muscle activities using semi-blind ICA. The muscle activity after decomposition can be used to accurately identify small and subtle hand actions and gestures. Finally the ICA source separation was validated with mixing matrix analysis.

Limitations and applications of ICA for surface electromyogram.

Surface electromyogram (SEMG) has numerous applications, but the presence of artefacts and noise, especially at low level of muscle activity make the recordings unreliable. Spectral and temporal overlap can make the removal of artefacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, numbers of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artefacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and number of sources. The paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. The paper also reports tests using Zibulevsky's technique of temporal plotting to identify number of independent sources in SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results identify the unsuitability of ICA when the number of sources is greater than the number of recording channels. The results also demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. The paper determines the suitability of the use of error measure using simulated mixing matrix and the estimated unmixing matrix as a means identifying the quality of separation of the output. The work demonstrates that even at extremely low level of muscle contraction, and with filtering using wavelets and band pass filters, it is not possible to get the data sparse enough to identify number of independent sources using Zibulevs.ky's technique.

Bemnisia tabaci feeding induces pathogenesis-related proteins in cassava (Manihot esculenta Crantz).

Cassava (Manihot esculenta Cranzts) plants fed upon by whitefly Bemisia tabaci showed increased levels of pathogenesis-related (PR) proteins, such as beta-1, 3-glucanase, peroxidase and chitinase activities, as compared to uninfested plants. The enzymes increased in specific activities from 2 to 7 fold and protein content in leaf extracts decreased in whitefly-infested plants, compared to uninfested plants. Among the three PR proteins, B. tabaci feeding induced significantly higher beta-1, 3-glucanase activities, when compared with other two PR proteins. Study also discussed the possible application of PR proteins in whitefly control program.

Assessing the development of fetal myocardial function by a novel Doppler myocardial performance index.

Doppler-derived myocardial performance index are used to assess aspects of systolic and diastolic function in adult as well as fetal heart. The Tei Index (TI) is a useful and non-invasive tool. The aim of this study was to determine normal values of fetal left ventricular (LV) TI in early (16-32 weeks) and late trimester (35-41 weeks) fetuses and to develop a new myocardial performance index (K-index; KI) which focuses on fetal diastolic interval when oxygenated blood from the placenta filled in the LV. Simultaneous recordings of Doppler Ultrasound Signal (DUS) of the LV outflow tracts and fetal electrocardiogram signals were obtained in 55 early- and late-trimester fetuses. The LV isovolumic contraction time (ICT), isovolumic relaxation time (IRT), ventricular ejection time (VET) and ventricular filling time (VFT) were measured from amplitudes and peaks of high frequency component of DUS signals by an automated model. The TI and the new index were calculated by using the formula (ICT + IRT)/VET and (ICT + IRT)/VFT respectively. The TI did not show any significant change from early-to late trimester fetuses (0.74± 0.057 vs 0.75±0.07; NS). On the other hand, KI showed significant decline in values (1.00±0.24 vs 0.87±0.23; p<;0.05) during this development period. The correlation of KI with gestational ages was found to be negative and significant (r=-0.35; p=0.007). No significant correlation of gestational ages with TI was found. Both TI and KI can be easily obtained in the fetus from DUS recording without the need for precise anatomic imaging. In conclusion, KI may be a useful tool to explore the healthy development of fetal myocardial function. More research is needed to check if KI could recognize the compromised fetuses from normal ones in future.

Heart rate independent QT variability component can detect subclinical cardiac autonomic neuropathy in diabetes.

Cardiac autonomic neuropathy (CAN) may lead to life threatening arrhythmia due to denervation of both the parasympathetic and sympathetic branches of autonomic nervous system innervating the heart. CAN is a frequently under diagnosed complication of diabetes, because a patient can have asymptomatic CAN for several years before it is clinically apparent. However, detection of CAN at the early or subclinical stage leads to more effective treatment outcomes. Cardiac autonomic reflex tests (CART) (i.e. Ewing test battery) are normally used for the detection and staging of CAN. These tests have limitations with the necessity of active participation of the patients for test maneuvers, as a majority of patients will not be able to complete all five tests required due to comorbidities such as frailty, obesity or cardiorespiratory disease. CAN affects both heart rate (measured by RR interval dynamics) and ventricular repolarization function (i.e. QT interval dynamics) of the heart, which can be efficiently analyzed from surface ECG. Therefore, ECG based diagnosis techniques of CAN analysis are becoming popular as they can reduce the limitations of CARTs used traditionally for CAN detection and it complements CART results. In this study, the performance of an ECG based QTV feature derived using a model free approach, which can quantify the QTV component not affected directly by the heart rate (HR) variation, is compared with some other measures of QTV and HRV in subclinical CAN detection in diabetes. Short-term ECGs (i.e. 5 min long) of 60 diabetic subjects without CAN and 50 diabetic subjects detected with early level of CAN determined by CART were analyzed. The proposed measure for quantifying the QTV component independent of HR denoted as QTV~RR stands out to be more discriminatory than other existing variability measures of QTV and HRV in subclinical detection of CAN.

Incremental training of support vector machines.

We propose a new algorithm for the incremental training of support vector machines (SVMs) that is suitable for problems of sequentially arriving data and fast constraint parameter variation. Our method involves using a "warm-start" algorithm for the training of SVMs, which allows us to take advantage of the natural incremental properties of the standard active set approach to linearly constrained optimization problems. Incremental training involves quickly retraining a support vector machine after adding a small number of additional training vectors to the training set of an existing (trained) support vector machine. Similarly, the problem of fast constraint parameter variation involves quickly retraining an existing support vector machine using the same training set but different constraint parameters. In both cases, we demonstrate the computational superiority of incremental training over the usual batch retraining method.

Phase asymmetry of heart rate variability signal.

Heart rate asymmetry (HRA) is considered as a physiological phenomenon in healthy subjects. In this article, we propose a novel HRA index, Slope Index (SI), to quantify phase asymmetry of heart rate variability (HRV) system. We assessed the performance of proposed index in comparison with conventional (Guzik's Index (GI) and Porta's Index (PI)) HRA indices. As illustrative examples, we used two case studies: (i) differentiate physiologic RR series from synthetic RR series; and (ii) discriminate arrhythmia subjects from Healthy using beat-to-beat heart rate time series. The results showed that SI is a superior parameter than GI and PI for both case studies with maximum ROC area of 0.84 and 0.82 respectively. In contrast, GI and PI had ROC areas {0.78, 0.61} and {0.50, 0.56} in two case studies respectively. We also performed surrogate analysis to show that phase asymmetry is caused by a physiologic phenomena rather than a random nature of the signal. In conclusion, quantification of phase asymmetry of HRV provides additional information on HRA, which might have a potential clinical use to discriminate pathological HRV in future.

Distortion properties of the interval spectrum of IPFM generated heartbeats for heart rate variability analysis.

The integral pulse frequency modulation (IPFM) model converts a continuous-time signal into a modulated series of event times, often represented as a pulse train. The IPFM process is important to the field of heart rate variability (HRV) as a simple model of the sinus modulation of heart rate. In this paper, we discuss the distortion properties associated with employing the interval spectrum for the recovery of the input signal from an IPFM process's output pulse train. The results state, in particular for HRV, how precisely the interval spectrum can be used to infer the modulation signal responsible for a series of heartbeats. We have developed a detailed analytical approximation of the interval spectrum of an IPFM process with multiple sinusoids as the input signal. Employing this result, we describe the structure and the distortion of the interval spectrum. The distortion properties of the interval spectrum are investigated systematically for a pair of frequency components. The effects of linear and nonlinear distortion of the fundamentals, the overall contribution of harmonic components to the total power, the relative contribution of "folded back" power due to aliasing and the total distortion of the input spectrum are investigated. We also provide detailed comparisons between the interval spectrum and the spectrum of counts (SOC). The spectral distortion is significant enough that caution should be taken when interpreting the interval spectrum, especially for high frequencies or large modulation amplitudes. Nevertheless, the distortion levels are not significantly larger than those of the SOC. Therefore, the spectrum of intervals may be considered a viable technique that suffers more distortion than the SOC.

Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability?

Heart rate variability (HRV) is concerned with the analysis of the intervals between heartbeats. An emerging analysis technique is the Poincaré plot, which takes a sequence of intervals and plots each interval against the following interval. The geometry of this plot has been shown to distinguish between healthy and unhealthy subjects in clinical settings. The Poincaré plot is a valuable HRV analysis technique due to its ability to display nonlinear aspects of the interval sequence. The problem is, how do we quantitatively characterize the plot to capture useful summary descriptors that are independent of existing HRV measures? Researchers have investigated a number of techniques: converting the two-dimensional plot into various one-dimensional views; the fitting of an ellipse to the plot shape; and measuring the correlation coefficient of the plot. We investigate each of these methods in detail and show that they are all measuring linear aspects of the intervals which existing HRV indexes already specify. The fact that these methods appear insensitive to the nonlinear characteristics of the intervals is an important finding because the Poincaré plot is primarily a nonlinear technique. Therefore, further work is needed to determine if better methods of characterizing Poincaré plot geometry can be found.

Neural techniques for combinatorial optimization with applications.

After more than a decade of research, there now exist several neural-network techniques for solving NP-hard combinatorial optimization problems. Hopfield networks and self-organizing maps are the two main categories into which most of the approaches can be divided. Criticism of these approaches includes the tendency of the Hopfield network to produce infeasible solutions, and the lack of generalizability of the self-organizing approaches (being only applicable to Euclidean problems). This paper proposes two new techniques which have overcome these pitfalls: a Hopfield network which enables feasibility of the solutions to be ensured and improved solution quality through escape from local minima, and a self-organizing neural network which generalizes to solve a broad class of combinatorial optimization problems. Two sample practical optimization problems from Australian industry are then used to test the performances of the neural techniques against more traditional heuristic solutions.

Effects of moving the center's in an RBF network.

In radial basis function (RBF) networks, placement of centers is said to have a significant effect on the performance of the network. Supervised learning of center locations in some applications show that they are superior to the networks whose centers are located using unsupervised methods. But such networks can take the same training time as that of sigmoid networks. The increased time needed for supervised learning offsets the training time of regular RBF networks. One way to overcome this may be to train the network with a set of centers selected by unsupervised methods and then to fine tune the locations of centers. This can be done by first evaluating whether moving the centers would decrease the error and then, depending on the required level of accuracy, changing the center locations. This paper provides new results on bounds for the gradient and Hessian of the error considered first as a function of the independent set of parameters, namely the centers, widths, and weights; and then as a function of centers and widths where the linear weights are now functions of the basis function parameters for networks of fixed size. Moreover, bounds for the Hessian are also provided along a line beginning at the initial set of parameters. Using these bounds, it is possible to estimate how much one can reduce the error by changing the centers. Further to that, a step size can be specified to achieve a guaranteed, amount of reduction in error.

An adaptive tracking controller using neural networks for a class of nonlinear systems.

A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme.

Spatio-temporal feature maps using gated neuronal architecture.

In this paper, Kohonen's self-organizing feature map is modified by a novel technique of allowing the neurons in the feature map to compete in a selective manner. The selective competition is achieved by grating the N-dimensional feature space using a spatial frequency and setting a criterion for the neurons to compete based on the region in which the input pattern resides. The spatial grating and selective competition are achieved by introducing a gated neuronal architecture in the feature map. As the selection criterion changes with time, it generates a time sequence of winning node indexes providing more input information and potentially allowing higher classification performance. These time sequences are then used to predict the class label of the input pattern more accurately. Three possible class label prediction algorithms are formulated based on evidential reasoning method and Bayes conditional probability theorem. These are tested on real world 8-class texture and a synthetic 12-class 3D object recognition problems. The classification performance is then compared with the results obtained by using a standard statistical linear discriminant analysis.

Bemisia tabaci (Homoptera: Aleyrodidae) biotypes in India.

Host plant performance, esterase, and virus transmission tests revealed cassava-strain and sweetpotato-strain populations of whitefly Bemisia tabaci (Gennadius) biotypes in India. Individuals from the sweetpotato-reared population did not breed on cassava, Manihot esculenta Crantz, and the cassava-strain-reared individuals failed to develop on sweetpotato, Ipomoea batatus (L.) Lam. Eggplant, Solanum melongena L., and tobacco, Nicotiana tabacum L., were common hosts for both biotypes. The cassava-strain whiteflies but not the sweetpotato-reared whiteflies successfully transmitted cassava mosaic virus from disease-infected cassava seedlings to healthy cassava seedlings. Presence of biotypes in B. tabaci is reported for the first time from India.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions.

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects' ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT-RR and a trivariate [Formula: see text] model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects' data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT-RR-RESP and QT-RR-EDR) gave significantly better fit value (p < 0.05) than that of found from the QT-RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Risk stratification of cardiac autonomic neuropathy based on multi-lag Tone-Entropy.

Cardiac autonomic neuropathy (CAN) is an irreversible condition affecting the autonomic nervous system, which leads to abnormal functioning of the visceral organs and affects critical body functions such as blood pressure, heart rate and kidney filtration. This study presents multi-lag Tone-Entropy (T-E) analysis of heart rate variability (HRV) at multiple lags as a screening tool for CAN. A total of 41 ECG recordings were acquired from diabetic subjects with definite CAN (CAN+) and without CAN (CAN-) and analyzed. Tone and entropy values of each patient were calculated for different beat sequence lengths (len: 50-900) and lags (m: 1-8). The CAN- group was found to have a lower mean tone value compared to that of CAN+ group for all m and len, whereas the mean entropy value was higher in CAN- than that in CAN+ group. Leave-one-out (LOO) cross-validation tests using a quadratic discriminant (QD) classifier were applied to investigate the performance of multi-lag T-E features. We obtained 100 % accuracy for tone and entropy with len = 250 and m = {2, 3} settings, which is better than the performance of T-E technique based on lag m = 1. The results demonstrate the usefulness of multi-lag T-E analysis over single lag analysis in CAN diagnosis for risk stratification and highlight the change in autonomic nervous system modulation of the heart rate associated with cardiac autonomic neuropathy.

Estimating relative respiratory effort from features of Photo-Plethysmography signal.

The gold standard method for measuring respiratory effort (esophageal pressure measurement) is invasive, uncomfortable, and itself can disrupt sleep. As a consequence, majority of sleep studies use an alternate sensor, typically respiratory bands, which, however, do not measure respiratory effort. Typically they indicate changes in thoracic volume, and so are more a secondary sensor of respiratory movement rather than respiratory effort. In this study, we aim to look at how features extracted from finger Photo Plethysmogram (PPG) signals correlate with changes in esophageal pressure signal. Principle component analysis was used to derive the relative respiratory effort signals using pulse to pulse intervals, pulse wave amplitudes, area of pulse and wavelet decomposed band (0.15~0.4 Hz) of PPG signals.

Investigating relative respiratory effort signals during mixed sleep apnea using photoplethysmogram.

Sleep disordered breathing does show different types of events. These are obstructive apnea events, central apnea events and mixed sleep apnea (MSA) which have a central component with a pause in airflow without respiratory effort followed by an obstructive component with respiratory effort. The esophageal pressure (Pes) is the accurate method to assess respiratory effort. The aim of the present study is to investigate whether the features extracted from photo-plethysmogram (PPG) could relate with the changes in Pes during MSA. Therefore, Pes and PPG signals during 65 pre-scored MSA events and 10 s preceding the events were collected from 8 patients. Pulse intervals (PPI), Pulse wave amplitudes (PWA) and wavelet decomposition (Wv) of PPG signals at level 8 (0.15-0.32 Hz) were derived from PPG signals. Results show that significant correlations (r = 0.63, p < 0.01; r = 0.42, p < 0.05; r = 0.8, p < 0.01 for OSA part) were found between reductions in Pes and that in PPG based surrogate respiratory signals PPI, PWA and Wv. Results suggest that PPG based relative respiratory effort signal can be considered as an alternative to Pes as a means of measuring changes in inspiratory effort when scoring OSA and CSA parts of MSA events.

A note on octonionic support vector regression.

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual "kernel trick" breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR.