Because of the need to control power consumption, in many biomedical applications asynchronous processing of the data is more appropriate. In this paper, we present a scale-based decomposition algorithm for analog signals similar to the wavelet decomposition. Our procedure uses asynchronous sigma delta modulators (ASDMs) to represent the amplitude of a signal using the zero-crossing times of a binary signal. Changing the zero-crossing times into random sequences of pulse widths, it can be shown to be equivalent to an optimal level-crossing sampler using local averages as the quantization levels. Applying the generation of multi-level signals from the output of ASDMs for different scale parameters we are able to obtain a decomposer that in a few stages provides a close representation of the signal. To illustrate the performance of the proposed decomposition, we consider its application to the representation of heart sounds.

Walking is a complex, rhythmic task performed by the locomotor system. However, natural gait rhythms can be influenced by metronomic auditory stimuli, a phenomenon of particular interest in neurological rehabilitation. In this paper, we examined the effects of aural, visual and tactile rhythmic cues on the temporal dynamics associated with human gait. Data were collected from fifteen healthy adults in two sessions. Each session consisted of five 15-minute trials. In the first trial of each session, participants walked at their preferred walking speed. In subsequent trials, participants were asked to walk to a metronomic beat, provided through visually, aurally, tactile or all three cues (simultaneously and in sync), the pace of which was set to the preferred walking speed of the first trial. Using the collected data, we extracted several parameters including: gait speed, mean stride interval, stride interval variability, scaling exponent and maximum Lyapunov exponent. The extracted parameters showed that rhythmic sensory cues affect the temporal dynamics of human gait. The auditory rhythmic cue had the greatest influence on the gait parameters, while the visual cue had no statistically significant effect on the scaling exponent. These results demonstrate that visual rhythmic cues could be considered as an alternative cueing modality in rehabilitation without concern of adversely altering the statistical persistence of walking.

A unified approach for the estimation of the first three phase derivatives of non-stationary signals is proposed in this paper. The possibility to accurately estimate phase derivatives is important in many applications dealing with objects velocity, acceleration and acceleration rate, such as the radar applications and mechanics. The estimation approach is based on definition of the complex-lag distribution. The proposed distribution is inspired by the concepts of complex analysis theory. The general form of distribution for the estimation of the first, second and third derivative of the phase is derived from the specific individual cases. The theoretical considerations are illustrated in the example with fast varying signal phase function.

The purpose of this study was to demonstrate the use of the self-organizing map (SOM) method for visualization, modeling, and comparison of trunk neuromuscular synergies during perturbed sitting. Thirteen participants were perturbed at the level of the sternum, in eight directions during sitting. Electromyographic (EMG) responses of ten trunk muscles involved in postural control were recorded. The SOM was used to encode the EMG responses on a 2-D projection (i.e., visualization). The result contains similar patterns mapped close together on the plot therefore forming clusters of data. Such visualization of ten EMG responses, following eight directional perturbations, allows for comparisons of direction-dependent postural synergies. Direction-dependent neuromuscular response models for each muscle were then constructed from the SOM visualization. The results demonstrated that the SOM was able to encode neuromuscular responses, and the SOM visualization showed direction-dependent differences in the postural synergies. Moreover, each muscle was modeled using the SOM-based method, and derived models showed that all muscles, except for one, produced a Gaussian fit for direction-dependent responses. Overall, SOM analysis offers a reverse engineering method for exploration and comparison of complex neuromuscular systems, which can describe postural synergies at a glance.

Head movements can greatly affect swallowing accelerometry signals. In this paper, we implement a spline-based approach to remove low frequency components associated with these motions. Our approach was tested using both synthetic and real data. Synthetic signals were used to perform a comparative analysis of the spline-based approach with other similar techniques. Real data, obtained data from 408 healthy participants during various swallowing tasks, was used to analyze the processing accuracy with and without the spline-based head motions removal scheme. Specifically, we analyzed the segmentation accuracy and the effects of the scheme on statistical properties of these signals, as measured by the scaling analysis. The results of the numerical analysis showed that the spline-based technique achieves a superior performance in comparison to other existing techniques. Additionally, when applied to real data, we improved the accuracy of the segmentation process by achieving a 27% drop in the number of false negatives and a 30% drop in the number of false positives. Furthermore, the anthropometric trends in the statistical properties of these signals remained unaltered as shown by the scaling analysis, but the strength of statistical persistence was significantly reduced. These results clearly indicate that any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy.

Continuous monitoring of physiological functions such as heart sounds can pose severe constraints on data acquisition and processing systems, especially if remote monitoring is desired. In this paper, we investigate the utility of a recently proposed compressive sensing (CS) algorithm based on modulated discrete prolate spheroidal sequences (MDPSS) for recovering sparsely sampled heart sounds. In particular, we investigate the recordings containing opening snap (OS) or the third heart sounds (S3) in addition to first and second heart sounds. The results of numerical analysis show that heart sounds can be accurately reconstructed even when the sampling rate is reduced to 40% of the original sampling frequency.

Continuous-time digital signal processors not only offer significant energy savings in important applications such as implantable biomedical devices, but can implement asynchronous procedures. In this paper, we propose an asynchronous signal decomposition for continuous-time signals based on scale rather than frequency. Because the implementation of the proposed procedure does not use a clock it is not affected by aliasing, and moreover no quantization is involved. Such procedure is specially applicable to biomedical signals delivering information in bursts rather than continuously. The decomposer consists of cascaded modules that expand the signal onto different resolution scales and each is composed of an asynchronous sigma delta modulator (ASDM) followed by a local averager and a low-pass filter. The ASDM is a non-linear feedback system used to represent the amplitude of a continuous-time signal by a binary signal whose zero-crossings are used to reconstruct the original signal. One of the parameters of the ASDM is used as a scaling parameter, permitting us to represent the signal by its local means -at different scales- and computed from the zero-crossing times of the output of the ASDM. We develop a compact signal representation that is described by a small number of scale parameters and contains information useful in the continuous-time processing and transmission of the data. The performance of the proposed procedure is illustrated using different types of signals. As a practical application, we consider the non-linear denoising of swallowing signals. Potentially our procedure will find application in asynchronous signal acquisition, continuous-time digital signal processing and transmission in low-power biomedical applications.