BackgroundStride interval persistence, a term used to describe the correlation structure of stride interval time series, is thought to provide insight into neuromotor control, though its exact clinical meaning has not yet been realized. Since human locomotion is shaped by energy efficient movements, it has been hypothesized that stride interval dynamics and energy expenditure may be inherently tied, both having demonstrated similar sensitivities to age, disease, and pace-constrained walking.FindingsThis study tested for correlations between stride interval persistence and measures of energy expenditure including mass-specific gross oxygen consumption per minute (), mass-specific gross oxygen cost per meter (VO2) and heart rate (HR). Metabolic and stride interval data were collected from 30 asymptomatic children who completed one 10-minute walking trial under each of the following conditions: (i) overground walking, (ii) hands-free treadmill walking, and (iii) handrail-supported treadmill walking. Stride interval persistence was not significantly correlated with (p > 0.32), VO2 (p > 0.18) or HR (p > 0.56).ConclusionsNo simple linear dependence exists between stride interval persistence and measures of gross energy expenditure in asymptomatic children when walking overground and on a treadmill.

BackgroundElectrodermal reactions (EDRs) can be attributed to many origins, including spontaneous fluctuations of electrodermal activity (EDA) and stimuli such as deep inspirations, voluntary mental activity and startling events. In fields that use EDA as a measure of psychophysiological state, the fact that EDRs may be elicited from many different stimuli is often ignored. This study attempts to classify observed EDRs as voluntary (i.e., generated from intentional respiratory or mental activity) or involuntary (i.e., generated from startling events or spontaneous electrodermal fluctuations).MethodsEight able-bodied participants were subjected to conditions that would cause a change in EDA: music imagery, startling noises, and deep inspirations. A user-centered cardiorespiratory classifier consisting of 1) an EDR detector, 2) a respiratory filter and 3) a cardiorespiratory filter was developed to automatically detect a participant's EDRs and to classify the origin of their stimulation as voluntary or involuntary.ResultsDetected EDRs were classified with a positive predictive value of 78%, a negative predictive value of 81% and an overall accuracy of 78%. Without the classifier, EDRs could only be correctly attributed as voluntary or involuntary with an accuracy of 50%.ConclusionsThe proposed classifier may enable investigators to form more accurate interpretations of electrodermal activity as a measure of an individual's psychophysiological state.

BackgroundDual-axis swallowing accelerometry has recently been proposed as a tool for non-invasive analysis of swallowing function. Although swallowing is known to be physiologically modifiable by the type of food or liquid (i.e., stimuli), the effects of stimuli on dual-axis accelerometry signals have never been thoroughly investigated. Thus, the objective of this study was to investigate stimulus effects on dual-axis accelerometry signal characteristics. Signals were acquired from 17 healthy participants while swallowing 4 different stimuli: water, nectar-thick and honey-thick apple juices, and a thin-liquid barium suspension. Two swallowing tasks were examined: discrete and sequential. A variety of features were extracted in the time and time-frequency domains after swallow segmentation and pre-processing. A separate Friedman test was conducted for each feature and for each swallowing task.ResultsSignificant main stimulus effects were found on 6 out of 30 features for the discrete task and on 5 out of 30 features for the sequential task. Analysis of the features with significant stimulus effects suggested that the changes in the signals revealed slower and more pronounced swallowing patterns with increasing bolus viscosity.ConclusionsWe conclude that stimulus type does affect specific characteristics of dual-axis swallowing accelerometry signals, suggesting that associated clinical screening protocols may need to be stimulus specific.

Fast Hermite projections have been often used in image-processing procedures such as image database retrieval, projection filtering, and texture analysis. In this paper, we propose an innovative approach for the analysis of one-dimensional biomedical signals that combines the Hermite projection method with time-frequency analysis. In particular, we propose a two-step approach to characterize vibrations of various origins in swallowing accelerometry signals. First, by using time-frequency analysis we obtain the energy distribution of signal frequency content in time. Second, by using fast Hermite projections we characterize whether the analyzed time-frequency regions are associated with swallowing or other phenomena (vocalization, noise, bursts, etc.). The numerical analysis of the proposed scheme clearly shows that by using a few Hermite functions, vibrations of various origins are distinguishable. These results will be the basis for further analysis of swallowing accelerometry to detect swallowing difficulties.

The goal of this Chapter is to review the applications of the Thomson Multitaper analysis (Percival and Walden; 1993b), (Thomson; 1982) for problems encountered in communications (Thomson; 1998; Stoica and Sundin; 1999). In particular we will focus on issues related to channel modelling, estimation and prediction. Sum of Sinusoids (SoS) or Sum of Cisoids (SoC) simulators (Patzold; 2002; SCM Editors; 2006) are popular ways of building channel simulators both in SISO and MIMO case. However, this approach is not a very good option when features of communications systems such as prediction and estimation are to be simulated. Indeed, representation of signals as a sum of coherent components with large prediction horizon (Papoulis; 1991) leads to overly optimistic results. In this Chapter we develop an approach which allows one to avoid this difficulty. The proposed simulator combines a representation of the scattering environment advocated in (SCM Editors; 2006; Almers et al.; 2006; Molisch et al.; 2006; Asplund et al.; 2006; Molish; 2004) and the approach for a single cluster environment used in (Fechtel; 1993; Alcocer et al.; 2005; Kontorovich et al.; 2008) with some important modifications (Yip and Ng; 1997; Xiao et al.; 2005). The problem of estimation and interpolation of a moderately fast fading Rayleigh/Rice channel is important in modern communications. TheWiener filter provides the optimum solution when the channel characteristics are known (van Trees; 2001). However, in real-life applications basis expansions such as Fourier bases and discrete prolate spheroidal sequences (DPSS) have been adopted for such problems (Zemen and Mecklenbrauker; 2005; Alcocer-Ochoa et al.; 2006). If the bases and the channel under investigation occupy the same band, accurate

Auscultation is a useful procedure for diagnostics of pulmonary or cardiovascular disorders. The effectiveness of auscultation depends on the skills and experience of the clinician. Further issues may arise due to the fact that heart sounds, for example, have dominant frequencies near the human threshold of hearing, hence can often go undetected (1). Computer-aided sound analysis, on the other hand, allows for rapid, accurate, and reproducible quantification of pathologic conditions, hence has been the focus of more recent research (e.g., (1–5)). During computer-aided auscultation, however, lung sounds are often corrupted by intrusive quasiperiodic heart sounds, which alter the temporal and spectral characteristics of the recording. Separation of heart and lung sound components is a difficult task as both signals have overlapping frequency spectra, in particular at frequencies below 100 Hz (6). For lung sound analysis, signal processing strategies based on conventional time, frequency, or time-frequency signal representations have been proposed for heart sound cancelation. Representative strategies include entropy calculation (7) and recurrence time statistics (8) for heart sound detection-and-removal followed by lung sound prediction, adaptive filtering (e.g., (9; 10)), time-frequency spectrogram filtering (11), and time-frequency wavelet filtering (e.g., (12–14)). Subjective assessment, however, has suggested that due to the temporal and spectral overlap between heart and lung sounds, heart sound removal may result in noisy or possibly “non-recognizable" lung sounds (15). Alternately, for heart sound analysis, blind source extraction based on periodicity detection has recently been proposed for heart sound extraction from breath sound recordings (16); subjective listening tests, however, suggest that the extracted heart sounds are noisy and often unintelligible (17). In order to benefit fully from computer-aided auscultation, both heart and lung sounds should be extracted or blindly separated from breath sound recordings. In order to achieve such a difficult task, a few methods have been reported in the literature, namely, wavelet filtering (18), independent component analysis (19; 20), and more recently, modulation domain filtering (21). The motivation with wavelet filtering lies in the fact that heart sounds contain large components over several wavelet scales, while coefficients associated with lung sounds quickly decrease with increasing scale. Heart and lung sounds are iteratively separated based on an adaptive hard thresholding paradigm. As such, wavelet coefficients at each scale with amplitudes above the threshold are assumed to correspond to heart sounds and the remaining coefficients are associated with lung sounds. Independent component analysis, in turn, makes use

Background A common but debated technique in the management of swallowing difficulties is the chin tuck swallow, where the neck is flexed forward prior to swallowing. Natural variations in chin tuck angles across individuals may contribute to the differential effectiveness of the technique. Methodology To facilitate the study of chin tuck angle variations, we present a template tracking algorithm that automatically extracts neck angles from sagittal videos of individuals performing chin tuck swallows. Three yellow markers geometrically arranged on a pair of dark visors were used as tracking cues. Results The algorithm was applied to data collected from 178 healthy participants during neutral and chin tuck position swallows. Our analyses revealed no major influences of body mass index and age on neck flexion angles during swallowing, while gender influenced the average neck angle only during wet swallows in the neutral position. Chin tuck angles seem to be independent of anthropometry and gender in healthy adults, but deserve further study in pathological populations. Conclusion The proposed neck flexion angle extraction algorithm may be useful in future studies where strict participant compliance to swallowing task protocol can be assured.

The reassignment method is a widespread approach for obtaining high resolution time-frequency representations. Nevertheless, its performance is not always optimal and can deteriorate for low signal-to-noise ratio (SNR) values. In order to overcome these obstacles, a novel method for obtaining high resolution time-frequency representations is proposed in this paper. The new method implements recently proposed nonparametric snakes in order to obtain accurate locations of the signal ridges in the time-frequency domain. The results of numerical analysis show that the proposed method is capable of achieving significantly higher concentration of signals in the time-frequency domain in comparison to the spectrogram and the traditional reassignment method. Furthermore, the new scheme also maintains good performance for low SNR values, while the performance of the other two considered methods significantly diminishes. It is clear from the results that the proposed method might be of significance in applications where accurate estimation of the signal components is required for low SNR values.