Biomedical subjects

J M Spyers-Ashby

Publications and source records attributed to J M Spyers-Ashby.

3 recordsLinked to original sources

PubMed2000

Reliability of tremor measurements using a multidimensional electromagnetic sensor system.

OBJECTIVE: To investigate the reliability of repeated measurements of normal physiological tremor made with a multidimensional measurement system. EQUIPMENT: Measurements of postural upper limb tremor at the hand were made in 10 normal subjects using a 3Space Fastrak (Polhemus, Inc.) which detects movement over six degrees-of-freedom (three of the measurement directions were examined). DATA COLLECTION: Reliability was assessed for two alternative upper limb postures (arm straight or bent) and data were collected on two days, twice at each session, to determine the repeatability between and within recording sessions. DATA ANALYSIS: The data were split into segments and subjected to autoregressive (AR) modelling. Three parameters (one for each of the measurement directions examined) were extracted from the models and used as variables for the reliability analysis. STATISTICAL TESTS: Variation within and between sessions was assessed by finding the median differences between efforts and days for each subject and then finding the overall median value and the corresponding 97.9% confidence intervals for each movement. This produced estimates of the population median value and indicated the precision of the estimates. RESULTS: All the confidence intervals encompassed the zero median difference point indicating that, in the population, this technique would produce repeatable results. For between-efforts comparisons there was some evidence that data collected for the bent arm posture were more repeatable than for a straight arm. CONCLUSION: Normal physiological tremor can be measured reliably, within and between sessions, using the 3Space Fastrak system.

Adult↗

PubMed1999

Classification of normal and pathological tremors using a multidimensional electromagnetic system.

A new multidimensional movement analysis system was used to record limb tremor over six degrees-of-freedom, and signal processing techniques were explored to develop a suitable classification method to distinguish between different types of tremor. The specific aims were to investigate the ability of the system to screen for differences between normal subjects and a group of neurological patients, and then to differentiate between three diagnostic groups of patients. Postural tremor at the hand was recorded in normal subjects (n=24) and patients with essential tremor (n=21), multiple sclerosis (n=17) and parkinsonism (n=19). Data were collected using a 3Space Fastrak((R)) (Polhemus, Inc.) over six degrees-of-freedom (three translational directions and three rotations). Spectral estimates produced measures of tremor frequency and amplitude. Mathematical models of the data, using autoregressive modelling and K-nearest neighbour classification, produced parameters used to classify, (1) the normal subjects and 24 patients (using the three rotational movements), and (2) the three patient groups (using all six movement directions). Results were given in terms of the probability of each subject belonging to the groups being classified. 70%). The diagnostic classification produced clear differences between the patient groups (60% for essential tremor, 80% for multiple sclerosis and 60% for parkinsonism). The ability of this assessment technique to distinguish between postural tremor in normal subjects and neurological patients suggests that it could be developed as a screening tool. Classification of tremors between the patients groups, with a high degree of sensitivity, indicates the potential for further development of the system as a diagnostic aid.

Adult↗

PubMed1998

A comparison of fast Fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data.

This review outlines the theory of spectral estimation techniques based on the fast Fourier transform (FFT) and autoregressive (AR) model and their application to the analysis of human tremor data. Two FFT-based spectral estimation techniques are presented, the Blackman-Tukey and periodogram methods. Factors that influence the quality of spectral estimates are discussed including the choice of windowing function. The theory of parametric modelling is introduced and AR modelling identified as the technique best suited to the analysis of tremor data. The processes of parameter estimation and model order selection are described. The theory of AR spectral estimation is outlined and differences between the AR and FFT-based spectral estimates are summarised. A brief guide to the implementation of FFT-based and AR spectral estimation techniques is given concentrating on data analysis packages that require little or no programming expertise. This review concludes that the AR modelling approach can produce tremor spectra that are superior to those from FFT-based methods for short data sequences. Although the spectral estimates are improved, the benefits of AR modelling for providing information about the physiological mechanisms of tremor generation are not yet clear.

Algorithms↗