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Biomedical subjects

G M Lyons

Publications and source records attributed to G M Lyons.

6 recordsLinked to original sources
PubMed2002

A versatile drop foot stimulator for research applications.

Drop Foot Stimulators are used to correct hemiplegic drop foot by synchronising the application of Functional Electrical Stimulation (FES) of the Common Peroneal Nerve (CPN) to the swing phase of the gait cycle. A research Drop Foot Stimulator (DFS) has been developed with a very flexible architecture to enable the investigation of a variety of gait-correction strategies. The portable unit has been carefully designed to optimise functionality while keeping its size and power consumption to a minimum. The device has two channels of stimulation, with all parameters of stimulation for each channel independently programmable. Four analogue and four digital sensor input channels are provided with a wide variety of sensor types possible. A microcontroller core is utilised to enable the implementation of different control algorithms. A PC-based user interface enables easy programming of the system configuration.

Electric Stimulation Therapy↗
PubMed2001

Stimulus artifact removal using a software-based two-stage peak detection algorithm.

The analysis of stimulus evoked neuromuscular potentials or m-waves is a useful technique for improved feedback control in functional electrical stimulation systems. Usually, however, these signals are contaminated by stimulus artifact. A novel software technique, which uses a two-stage peak detection algorithm, has been developed to remove the unwanted artifact from the recorded signal. The advantage of the technique is that it can be used on all stimulation artifact-contaminated electroneurophysiologic data provided that the artifact and the biopotential are non-overlapping. The technique does not require any estimation of the stimulus artifact shape or duration. With the developed technique, it is not necessary to record a pure artifact signal for template estimation, a process that can increase the complexity of experimentation. The technique also does not require the recording of any external hardware synchronisation pulses. The method avoids the use of analogue or digital filtering techniques, which endeavour to remove certain high frequency components of the artifact signal, but invariably have difficulty, resulting in the removal of frequencies in the same spectrum as the m-wave. With the new technique the signal is sampled at a high frequency to ensure optimum fidelity. Instrumentation saturation effects due to the artifact can be avoided with careful electrode placement. The technique was fully tested with a wide variety of electrical stimulation parameters (frequency and pulse width) applied to the common peroneal nerve to elicit contraction in the tibialis anterior. The program was also developed to allow batch processing of multiple files, using closed loop feedback correction. The two-stage peak detection artifact removal algorithm is demonstrated as an efficient post-processing technique for acquiring artifact free m-waves.

Action Potentials↗
PubMed2001

Data logging technology in ambulatory medical instrumentation.

This paper reviews the advancements made in ambulatory data logging used in the study of human subjects since the inception of the analogue tape based data logger in the 1960s. Research into the area of ambulatory monitoring has been rejuvenated due to the development of novel storage technologies during the 1990s. Data logging systems that were previously impractical due to lack of processing power, practical size and cost are now available to the practitioner. An overview of the requirements of present day ambulatory data logging is presented and analogue tape, solid-state memory and disk drive storage recording systems that have been described in the literature are investigated in detail. It is proposed that digital based technology offers the best solution to the problems encountered during human based data logging. The appearance of novel digital storage media will continue the trend of increased recording durations, signal resolution and number of parameters thus allowing the momentum gained throughout the last several decades to continue.

Humans↗
PubMed2000

Finite state control of functional electrical stimulation for the rehabilitation of gait.

Finite state control is an established technique for the implementation of intention detection and activity co-ordination levels of hierarchical control in neural prostheses, and has been used for these purposes over the last thirty years. The first finite state controllers (FSC) in the functional electrical stimulation of gait were manually crafted systems, based on observations of the events occurring during the gait cycle. Subsequent systems used machine learning to automatically learn finite state control behaviour directly from human experts. Recently, fuzzy control has been utilised as an extension of finite state control, resulting in improved state detection over standard finite state control systems in some instances. Clinical experience over the last thirty years has been positive, and has shown finite state control to be an effective and intuitive method for the control of functional electrical stimulation (FES) in neural prostheses. However, while finite state controlled neural prostheses are of interest in the research community, they are not widely used outside of this setting. This is largely due to the cumbersome nature of many neural prostheses which utilise externally mounted gait sensors and FES electrodes. FES-based control of movement has been subject to the constraints of artificial sensor and FES actuator technologies. However, continued advances in natural sensors and implanted multi-channel stimulators are broadening the boundaries of artificial control of movement, driving an evolutionary process towards increasingly human-like control of FES-based gait rehabilitation systems.

Electric Stimulation Therapy↗
PubMed1990

Decelerative changes in heart rate are associated with performance on tasks that assess intelligence.

Changes in heart rate, chin electromyographic (EMG) activity, and respiration rate were monitored during the performance of three tasks that are commonly employed to assess intelligence in human adults. Stimuli from the digit span subtest (Expt. 1) of the Wechsler scales of intelligence, the picture completion subtest (Expt. 2), and the picture arrangement subtest (Expt. 3) were employed. A 10-s warning preceding the onset of each stimulus was also used in each of the experiments. During all three tasks there was an initial increase in heart rate during the first 3-4 s of the warning signal, followed by a decrease in heart rate during the last 5-6 s of the warning signal. Mean heart rate during the 5 s immediately preceding the presentation of stimuli to which subjects gave correct responses was significantly lower than mean heart rate during the 5 s immediately preceding the presentation of stimuli to which subjects gave incorrect responses. In addition, mean heart rate during the 5 s immediately preceding the presentation of stimuli to which subjects gave correct responses decreased significantly below the pre-warning baseline level, whereas mean heart rate during the 5 s preceding the presentation of stimuli to which subjects gave incorrect responses did not decrease below the baseline level. The differential heart rate results for correct and incorrect responses were consistent across the three tasks. No significant changes in chin EMG and respiration rate were noted during any of the tasks. Relationships among heart rate, attention to environmental information, and the role of attention in measures of intelligence are discussed.

Adolescent↗