Slow-phase onset influence on waveform identification and foveation time measure in congenital nystagmus

Congenital nystagmus (CN) is an ocular-motor disorder characterised by involuntary, conjugated ocular oscillations and its pathogenesis is still under investigation. This kind of nystagmus is termed congenital (or infantile) since it could be present at birth or it can arise in the first months of life. Most of CN patients show a considerable decrease of their visual acuity: image fixation on the retina is disturbed by nystagmus continuous oscillations, mainly horizontal. However, the image of a given target can still be stable during short periods in which eye velocity slows down while the target image is placed onto the fovea (called foveation intervals). To quantify the extent of nystagmus, eye movement recording are routinely employed, allowing physicians to extract and analyse nystagmus main features such as waveform shape, amplitude and frequency. Using eye movement recording, it is also possible to compute estimated visual acuity predictors: analytical functions which estimates expected visual acuity using signal features such as foveation time and foveation position variability. Use of those functions extend the information from typical visual acuity measurement (e.g. Landolt C test) and could be a support for therapy planning or monitoring. This study focuses on detection of CN patients' waveform type and on foveation time measure. Specifically, it proposes a robust method to recognize cycles corresponding to the specific CN waveform in the eye movement pattern and, for those cycles, evaluate the exact signal tracts in which a subject foveates. About 40 eyemovement recordings, either infrared-oculographic or electrooculographic, were acquired from 16 CN subjects. Results suggest that the use of an adaptive threshold applied to the eye velocity signal could improve the estimation of slow phase start point. This can enhance foveation time computing and reduce influence of repositioning saccades and data noise on the waveform type identification.

A simulation of the surface EMG for analysis of muscle activity during whole body vibratory stimulation

Many studies have accounted for whole body vibration effects in the fields of exercise physiology, sport and rehabilitation medicine. Generally, surface EMG is utilized to assess muscular activity during the treatment; however, large motion artifacts appear superimposed to the raw signal, making sEMG recording not suitable before any artifact filtering. Sharp notch filters, centered at vibration frequency and at its superior harmonics, have been used in previous studies, to remove the artifacts. [6, 10] However, to get rid of those artifacts some true EMG signal is lost. The purpose of this study was to reproduce the effect of motor-unit synchronization on a simulated surface EMG during vibratory stimulation. In addition, authors mean to evaluate the EMG power percentage in those bands in which are also typically located motion artifact components. Model characteristics were defined to take into account two main aspect: the muscle MUs discharge behavior and the triggering effects that appear during local vibratory stimulation. [7] Inter-pulse-interval, was characterized by a polimodal distribution related to the MU discharge frequency (IPI 55-80ms, σ=12ms) and to the correlation with the vibration period within the range of ±2 ms due to vibration stimulus. [1, 7] The signals were simulated using different stimulation frequencies from 30 to 70 Hz. The percentage of the total simulated EMG power within narrow bands centered at the stimulation frequency and its superior harmonics (± 1 Hz) resulted on average about 8% (± 2.85) of the total EMG power. However, the artifact in those bands may contain more than 40% of the total power of the total signal. [6] Our preliminary results suggest that the analysis of the muscular activity of muscle based on raw sEMG recordings and RMS evaluation, if not processed during vibratory stimulation may lead to a serious overestimation of muscular response.

Leg muscles motion during whole body linear frequency sweep vibration

Whole body vibration (WBV) aims to mechanically activate muscles by eliciting stretch reflexes. Mechanical vibrations are usually transmitted to the patient body standing on a oscillating plate. WBV is now more and more utilized not only for fitness but also in physical therapy, rehabilitation and in sport medicine. Effects depend on intensity, direction and frequency of vibration; however, the training frequency is one of the most important factors involved. A preliminary vibratory session can be dedicated to find the best vibration frequency for each subject by varying, stepwise, the stimulation frequency and analyzing the resulting EMG activity. This study concentrates on the analysis of muscle motion in response to a vibration frequency sweep, while subjects held two different postures. The frequency of a vibrating platform was increased linearly from 10 to 60 Hz in 26 s, while platform and single muscles (Rectus Femoris, Biceps Femoris - long head and Gastrocnemius Lateralis) motions were monitored using tiny, lightweight three-axial MEMS accelerometers. Displacements were estimated integrating twice the acceleration data after gravity contribution removal. Mechanical frequency response (amplitude and phase) of the mechanical chains ending at the single muscles was characterized. Results revealed a mechanical resonant-like behavior at some muscles, very similar to a second-order system in the frequency interval explored; resonance frequencies and dumping factors depended on subject and its positioning onto the vibrating platform. Stimulation at the resonant frequency maximizes muscle lengthening, and in turn muscle spindle solicitation, which produce muscle activation. © 2009 Springer-Verlag.

Comparison of software developed for FHR extraction from PCG signals

A valuable alternative to US cardiotocography, for fetal surveillance, can be offered by phonocardiography, a passive and low cost acoustic recording of fetal heart sounds. A crucial point is the exact recognizing of the fetal heart sounds, associated to each fetal heart beat, and then the estimation of FHR signal. In this work, software for FHR assessment from phonocardiographic signals was developed. To check the reliability of the software, obtained results were compared with those of simultaneously recorded cardiotocographic signals. Results seemed to be satisfying, as provided FHR series were almost all confined within FHR-CTG +/- 3 bpm, where FHR-CTG were FHR series provided by commercial US cardiotocographic devices, currently employed in clinical routine.