Jurandir Nadal

Calculation of area of stabilometric signals using principal component analysis

In stabilometry, the sway of the human body in an upright posture is studied by monitoring the displacement of its centre of pressure in the lateral (x) and anterio-posterior (y) directions. The area covered by this trace has been defined as that of an ellipse fitted to the data. Conventionally, its angle of inclination is found through linear regression (LR) on the data in the x-y plane. In the present paper, principal component analysis (PCA) is proposed as providing a more suitable basis for the estimation of angle and area. Results of simulations and stabilometric tests confirm large differences between area and angle estimates obtained by regression of x over y, and y over x, with PCA generally agreeing with either one or the other of the LRs. The PCA technique is therefore recommended as an improved basis for measuring area and inclination of stabilograms, or similar data sets.

Application of principal component analysis in vertical ground reaction force to discriminate normal and abnormal gait

Discrete parameters from ground reaction force (GRF) are been considered in gait analysis studies. However, principal component analysis (PCA) may provide additional insight into gait analysis for considering the complete pattern of GRF. This study aimed at testing the application of PCA to discriminate the vertical GRF pattern between control group (CG) and patients with lower limb fractures (FG), as well as proposing a score to quantify the abnormality of gait. Thirty-eight healthy subjects participated of CG and 13 subjects in FG, five subjects from FG were also evaluated after physiotherapeutic treatment (FGA). The GRF was measured by an instrumented treadmill. Principal component coefficients (PCCs) were obtained by singular value decomposition using GRF of complete stride. Two, four and six PCCs were used to obtain the standard distance (D). The classification between groups was mainly given by the first PC, which indicated higher loading factors during push off of affected side and heel strike of unaffected side. The classification performance achieved 92.2% accuracy with two PCCs, 94.1% with four PCCs and 96.1% with six PCCs. Four subjects reached normal boundary after treatment, with all FGA subjects presenting decreased D. This study demonstrates that PCA is an adequate method for discriminating normal and abnormal gait and D allows an objective evaluation of the progress and effectiveness of rehabilitation treatment.

Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait

Deep brain stimulation of the subthalamic nucleus (DBS-STN) is an approved treatment for advanced Parkinson disease (PD) patients; however, there is a need to further evaluate its effect on gait. This study compares logistic regression (LR), probabilistic neural network (PNN) and support vector machine (SVM) classifiers for discriminating between normal and PD subjects in assessing the effects of DBS-STN on ground reaction force (GRF) with and without medication. Gait analysis of 45 subjects (30 normal and 15 PD subjects who underwent bilateral DBS-STN) was performed. PD subjects were assessed under four test conditions: without treatment (mof-sof), with stimulation alone (mof-son), with medication alone (mon-sof), and with medication and stimulation (mon-son). Principal component (PC) analysis was applied to the three components of GRF separately, where six PC scores from vertical, one from anterior-posterior and one from medial-lateral were chosen by the broken stick test. Stepwise LR analysis employed the first two and fifth vertical PC scores as input variables. Using the bootstrap approach to compare model performances for classifying GRF patterns from normal and untreated PD subjects, the first three and the fifth vertical PCs were attained as SVM input variables, while the same ones plus the first anterior-posterior were selected as PNN input variables. PNN performed better than LR and SVM according to area under the receiver operating characteristic curve and the negative likelihood ratio. When evaluating treatment effects, the classifiers indicated that DBS-STN alone was more effective than medication alone, but the greatest improvements occurred with both treatments together.

Digital Butterworth filter for subtracting noise from low magnitude surface electromyogram

This work presents a digital filter designed to delimitate the frequency band of surface electromyograms (EMG) and remove the mains noise and its harmonics, focusing the signal analysis during reduced muscle activity. A Butterworth filter was designed as the frequency-domain product of a second order, high-pass filter with cutoff frequency 10 Hz, an eighth order low-pass filter, with cutoff at 400 Hz and six stop-band filters, second order, centered at the 60 Hz mains noise and its harmonics until 360 Hz. The resulting filter was applied in both direct and reverse directions of the signals to avoid phase distortions. The performance was evaluated with a simulated EMG signal with additive noise in multiples of 60 Hz. A qualitative assessment was made with real EMG data, acquired from 16 subjects, with age from 20 to 32 years. Subjects were positioned in orthostatic position during 21s, being only the last second analyzed to assure stationarity. EMG were collected by Ag/AgCl electrodes on right lateral gastrocnemius, amplified with gain 5000, filtered in the band from 10 Hz to 1 kHz, and thus digitized with 2ksamples/s. The filter effectively removed the mains noise components, with attenuations greater than 96.6%. The attenuation of the simulated signal at frequencies below 15 Hz and at 60 Hz caused only a small reduction of total power, preserving the original spectrum. Thus, the filter resulted suitable to the proposed application.

Postural sway changes during pregnancy: A descriptive study using stabilometry

OBJECTIVE This study aims to analyse changes in body sway over the course of pregnancy. STUDY DESIGN This is a descriptive study in which stabilometric tests were applied at three stages of pregnancy and with a combination of different visual conditions (eyes open/closed) and support base configuration (feet together/apart). Twenty healthy pregnant women participated in the study. Changes in postural control with pregnancy were analysed via the elliptical area of the stabilograms and spectral analysis of the displacements of the centre of pressure (COP) along the lateral and anterior/posterior directions. RESULTS The elliptical area encompassing the COP significantly increased over the course of the pregnancy for the feet apart and eyes closed test protocols. The spectral analysis revealed a significant increase of COP oscillations along the anterior-posterior direction when subjects stood with the eyes open/feet together and feet apart. A reduction (significant) of the lateral oscillations of COP was observed for the eyes open/feet together protocol. CONCLUSION Pregnancy induced significant changes in the postural control when pregnant women stood with a reduced support base or with eyes closed.

An overview of age-related changes in postural control during quiet standing tasks using classical and modern stabilometric descriptors

Age-related changes in postural control during quiet standing likely result from underlying pathological conditions or from the low specificity of classical stabilometric parameters, which are vulnerable to base of support configurations and anthropometric differences. This study focuses on the identification of changes in postural control with natural aging by using conventional and recent stabilometric analysis, and on the interpretation of the stabilometric parameters according to a recently proposed framework of postural control. Quiet standing stabilometric tests were applied to 57 subjects equally divided into young, middle-aged and aged groups (19-29, 38-51 and 65-73 years, respectively) with eyes open and closed conditions. In addition to estimation of classical descriptors, center of pressure time series were approached according to a diffusion-like process and the recently proposed sway density curve. Two out of 10 estimated descriptors identified between-group differences. Aged subjects exhibited higher sway frequencies, possibly resulting from the increase of torque bursts produced by the plantar flexors, and stronger negative correlation between consecutive center of pressure displacements observed for long time intervals, likely due to higher amplitude of plantar flexors torque. Aging itself does not result in major changes of postural stability, but reflects a small increase in plantar flexion torque amplitude and frequency of torque adjustments, probably to compensate for the lower stiffness of calf muscle tendon in aged subjects.

Efeitos da idade e da aptidão aeróbica na recuperação da frequência cardíaca em homens adultos

BACKGROUND Physiological aging leads to cardiac autonomic dysfunction, which is associated with the onset and worsening of cardiovascular disease and an increased risk of death. Currently, physical exercise is considered a cardioprotective strategy and more research is needed on its benefit on cardiac autonomic function. OBJECTIVE To evaluate the autonomic control of heart rate in healthy young and middle-aged volunteers with different levels of aerobic fitness. METHODS The study included 68 volunteers, stratified for age and level of aerobic fitness. Based on aerobic fitness assessed by the submaximal exercise test, subjects were separated into two groups, good fitness and poor fitness. Assessment of cardiac autonomic control was performed based on measurements of heart rate variability at rest and heart rate recovery post-exercise. Analysis of variance with two factors was used to compare the variables investigated. RESULTS The heart rate variability is significantly lower in middle-aged volunteers than in young individuals, regardless of the aerobic fitness level (p <0.01). Higher levels of aerobic fitness in middle-aged volunteers are associated with earlier post-effort vagal reentry - rate of HR decline after 1min30s: 39.6% good aerobic fitness vs. poor 28.4% (p < 0.01). CONCLUSION Better levels of aerobic fitness act beneficially on the autonomic control of post-exercise heart rate, preserving the vagal reentry velocity in healthy middle-aged volunteers. However, it does not attenuate the decrease in heart rate variability due to the natural aging process.

Estimation procedures affect the center of pressure frequency analysis

Even though frequency analysis of body sway is widely applied in clinical studies, the lack of standardized procedures concerning power spectrum estimation may provide unreliable descriptors. Stabilometric tests were applied to 35 subjects (20-51 years, 54-95 kg, 1.6-1.9 m) and the power spectral density function was estimated for the anterior-posterior center of pressure time series. The median frequency was compared between power spectra estimated according to signal partitioning, sampling rate, test duration, and detrending methods. The median frequency reliability for different test durations was assessed using the intraclass correlation coefficient. When increasing number of segments, shortening test duration or applying linear detrending, the median frequency values increased significantly up to 137%. Even the shortest test duration provided reliable estimates as observed with the intraclass coefficient (0.74-0.89 confidence interval for a single 20-s test). Clinical assessment of balance may benefit from a standardized protocol for center of pressure spectral analysis that provides an adequate relationship between resolution and variance. An algorithm to estimate center of pressure power density spectrum is also proposed.

Classification of cardiac arrhythmias based on principal component analysis and feedforward neural networks

In previous work (see Proc. 13/sup th/ Ann. Int. Conf. of IEEE/EMBS, vol. 1, p. 580-1, 1991), the authors created a database containing the first 10 principal component coefficients and the relative RR intervals of P-QRS complexes from all the patients of the MIT-BIH Arrhythmia Database. Here, the authors use logistic regression and feedforward neural networks for classifying the heart beats of patient 208, based on these principal component coefficients. The feedforward neural network technique is presented as an extension of the concept of logistic regression, applicable for classifying patterns into more than two classes. The results indicate the potential of the approach for the classification of cardiac arrhythmias.<<ETX>>

Assessing blood flow control through a bootstrap method

In order to assess blood flow control, the relationship between blood pressure and blood flow can be modeled by linear filters. We present a bootstrap method, which allows the statistical analysis of an index of blood flow control that is obtained from constrained system identification using an established set of pre-defined filters.