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Biomedical subjects

M I Harba

Publications and source records attributed to M I Harba.

6 recordsLinked to original sources
PubMed1999

Reliability of measurement of muscle fiber conduction velocity using surface EMG.

Cross-correlating two surface EMG signals detected at two different locations along the path of flow of action potential enables the measurement of the muscle fiber average conduction velocity in those active motor units monitored by the electrodes. The position of the peak of the cross-correlation function is the time delay between the two signals and hence the velocity may be deduced. The estimated velocity using this technique has been observed previously to depend on the location of the electrodes on the muscle surface. Different locations produced different estimates. In this paper we present a measurement system, analyze its inherent inaccuracies and use it for the purpose of investigating the reliability of measurement of conduction velocity from surface EMG. This system utilizes EMG signals detected at a number of locations on the biceps brachii, when under light tension, to look for any pattern of variations of velocity as a function of location and time. It consists of a multi-electrode unit and a set of eight parallel on-line correlators. The electrode unit and the parallel correlators ensure that these measurements are carried out under the same physical and physiological conditions of the muscle. Further, the same detected signals are used in different measurement configurations to try to understand the reasons behind the observed variations in the estimated velocity. The results obtained seem to suggest that there will always be an unpredictable random component superimposed on the estimated velocity, giving rise to differences between estimates at different locations and differences in estimates with time at the same location. Many factors contribute to this random component, such as the non-homogeneous medium between the muscle fibers and the electrodes, the non-parallel geometry and non-uniform conduction velocity of the fibers, and the physical and physiological conditions of the muscle. While it is not possible to remove this random component completely from the measurement, the user must be aware of its presence and how to reduce its effects.

Action Potentials↗
PubMed1988

On-line measurement of muscle fibre conduction velocity: analysis and optimization of performance.

Measurement of the average muscle fibre conduction velocity from surface electromyographic signals has important applications in the study of muscle fatigue and in ergonomics. In this paper, a two-channel hardware polarity correlator (which estimates 256 correlation coefficients per channel at a sampling rate of 18.58 kHz) and a specially designed surface electrode unit, are used to investigate various factors affecting the measurement reliability: electrode dimensions; EMG preprocessing to make the estimated correlation function more suitable for time delay measurement; and the manner in which velocity estimates, measured simultaneously from two different sections of the same muscle, are related. All the tests were performed on the biceps brachii when under medium tension; some tests involved tensions which led to muscle fatigue. Results showed that smaller electrode dimensions (i.e. smaller electrode units) and EMG preprocessing to increase its effective bandwidth, give more reliable measurements. Further, it was found that the estimated mean velocity is dependent on the location of the electrode unit on the muscle, and that at certain locations no reliable estimates can be obtained. The preliminary results obtained with muscle fatigue indicate that the estimated conduction velocity decreases, but not uniformly, across the muscle.

Electrodes↗
PubMed1988

Fast on-line polarity correlation algorithms for muscle fibre conduction velocity measurement.

Polarity cross-correlation is a useful technique for the measurement of muscle fibre conduction velocity using surface electromyography. Owing to the nature and volume of computation involved in the correlation function, standard techniques for its estimation by a microprocessor are too slow for an on-line application. In this paper two algorithms suitable for on-line estimation of polarity function are presented. Some useful features of the correlation function, as well as careful programming and careful choice of instructions, made it possible to use a standard microprocessor to achieve higher sampling rates than those reported recently.

Action Potentials↗
PubMed1986

EMG processor based on the amplitude probability distribution.

The EMG signal is being used increasingly for the control of prostheses and for investigating and retraining human movements. The raw (unprocessed) EMG is normally unsuitable for such applications and some processing is necessary. Due to the statistical characteristics of the EMG, it is usually difficult to achieve a processing delay of less than 100 ms. We introduce here a new EMG signal processing technique, implemented on an 8-bit microprocessor. It uses changes in the amplitude probability distribution of the EMG signal to discriminate between a number of tension levels in an individual muscle. The program employs procedures often used in pattern and speech recognition, in that it is trained to identify several tension levels. With a processing delay of 100 ms, on-line tests show that the new processor achieves a recognition rate of 84.8% when discriminating between five tension levels (including relaxation) in the biceps brachii. When the number of tension levels is reduced to four, the recognition rate becomes 96.7%, which compares well with other similar systems, some of which were tested in parallel with the new technique.

Adult↗
PubMed1981

Optimizing the acquisition and processing of surface electromyographic signals.

Electromyographic (EMG) signals, detected on the skin surface over active muscles, are being used increasingly for the control of prostheses and for investigating and retraining human movement. However, the statistical characteristics of the signal make it difficult toi achieve a processing delay of less than about 100 ms, which is marginal in some applications. Two techniques for reducing this delay are described: (i) signal pre-whitening, based upon digital autoregressive modelling or analogue filtering; an (ii) the use of a multiple electrode array. Successful discrimination between several states in a single muscle is possible in about 50 ms.

Action Potentials↗