Biomedical subjects

D B Popović

Publications and source records attributed to D B Popović.

9 recordsLinked to original sources

PubMed2006

Symmetry of post-movement beta-ERS and motor recovery from stroke: a low-resolution EEG pilot study.

The inter-hemispheric symmetry of electroencephalographic (EEG) post-movement beta-event-related synchronization (PMBS) after movements on a drawing board was studied in eight acute stroke subjects with mild hemiparesis and eight normal subjects. A follow-up testing was conducted 3 months after the initial recordings with a twofold purpose: (1) to validate the reproducibility of the experimental protocol in normal subjects; and (2) to study changes of inter-hemispheric PMBS-symmetry as a response to recovery of motor function. PMBS values were calculated and their topographic distributions illustrated at various time instances following movement offset. Significant PMBS patterns were present in all normal subjects, with only minor differences within consecutive recordings. The side of hemiparesis in acute stroke subjects could be distinguished (P = 0.04) on the basis of the signed symmetry index, a quantitative measure of lateralization. The follow-up testing on three recovered stroke subjects revealed a trend of changes in the lateralization towards the contralateral side of movement, an indication that the cortical organization of movement following recovery turned out as reported for normal subjects. Further clinical investigations need to be carried out to evaluate the relationship between recovery and PMBS symmetry on a large number of subjects, using the method presented here.

Aged↗

PubMed1996

Estimating mechanical parameters of leg segments in individuals with and without physical disabilities.

Methods are described for estimating the inertia, viscosity, and stiffness of the lower leg around the knee and of the whole leg around the hip that are applicable even to persons with considerable spasticity. These involve: 1) a "pull" test in which the limb is slowly moved throughout its range of motion while measuring angles (with an electrogoniometer) and torques (with a hand-held dynamometer) to determine passive stiffness and 2) a "pendulum" test in which the limb is moved against gravity and then dropped, while again measuring angles and torques. By limiting the extent of the movement and choosing a direction (flexion or extension) that minimizes reflex responses, the mechanical parameters can be determined accurately and efficiently using computer programs. In the sample of subjects studied (nine with disability related to spinal cord injury, head injury, or stroke, and nine with no neurological disability), the inertia of the lower leg was significantly reduced in the subjects with disability (p < 0.05) as a result of atrophy, but the stiffness and viscosity were within normal limits. The values of inertia were also compared with anthropometric data in the literature. The identification of these passive parameters is particularly important in designing systems for functional electrical stimulation of paralyzed muscles, but the methods may be widely applicable in rehabilitation medicine.

Adult↗

PubMed1995

Machine learning in control of functional electrical stimulation systems for locomotion.

Two machine learning techniques were evaluated for automatic design of a rule-based control of functional electrical stimulation (FES) for locomotion of spinal cord injured humans. The task was to learn the invariant characteristics of the relationship between sensory information and the FES-control signal by using off-line supervised training. Sensory signals were recorded using pressure sensors installed in the insoles of a subject's shoes and goniometers attached across the joints of the affected leg. The FES-control consisted of pulses corresponding to time intervals when the subject pressed on the manual push-button to deliver the stimulation during FES-assisted ambulation. The machine learning techniques used were the adaptive logic network (ALN) [1] and the inductive learning algorithm (IL) [2]. Results to date suggest that, given the same training data, the IL learned faster than the ALN, while both performed the test rapidly. The generalization was estimated by measuring the test errors and it was better with an ALN, especially if past points were used to reflect the time dimension. Both techniques were able to predict future stimulation events. An advantage of the ALN over the IL was that ALN's can be retrained with new data without losing previously collected knowledge. The advantages of the IL over the ALN were that the IL produces small, explicit, comprehensible trees and that the relative importance of each sensory contribution can be quantified.

Adult↗

PubMed1995

A novel electric design for electromagnetic stimulation--the Slinky coil.

A novel coil design for inductive electromagnetic stimulation of neural cells has been simulated and experimentally tested. This coil improves the focal effect of a magnetic stimulator, and it reduces its inductance, hence the efficiency of the system is improved. The basic structure of the device is derived from the popular "Slinky" toy. The actual device is formed by winding different numbers of loops forming a helical coil on a half torus. The loops are bunched at the axis of the torus. The coil, due to its geometry, generates a unique distribution of eddy currents in nearby tissues which is favorable compared to a solenoid type stimulator. This renders the Slinky coil more selective than conventional coils used for magnetic stimulation. The distribution of eddy currents was analyzed using Matlab, following Faraday's Law of Induction. Improved focality permits the current through the coil to be reduced for the same effect. In addition, the reduced inductance of the Slinky coil decreases the power requirement; thus, the improved efficiency of the system may allow the generation of bursts of pulses, and expand the utilization of the system to possible functional activation of certain neuro-muscular structures when peripheral nerves are stimulated.

Computer Simulation↗

PubMed1994

Instrumentation for ENG and EMG recordings in FES systems.

An electronic circuit for analog processing of neural (electroneurogram or ENG) and muscular (electromyogram or EMG) signals in functional electrical stimulation (FES) systems is described in this paper. The basic circuit consists of a low-noise gated preamplifier, band-pass filter, amplifier, and a blanking circuit to minimize stimulation artifacts during electrical stimulation. This device was tested in chronic recordings using a triphasic cuff electrode for nerves and epimysial electrodes for muscles in the hind limbs of cats. The device was used for nerve recordings in the presence of electrical stimulation of muscles in the same leg. The recordings showed rejection of stimulation and muscle (M-wave) artifacts, while retaining the information of interest.

Amplifiers, Electronic↗

PubMed1993

Finite state model of locomotion for functional electrical stimulation systems.

A finite state model of locomotion was developed to simplify a controller design for motor activities of handicapped humans. This paper presents a model developed for real time control of locomotion with functional electrical stimulation (FES) assistive systems. Hierarchical control of locomotion was adopted with three levels: voluntary, coordination and actuator level. This paper deals only with coordination level of control. In our previous studies we demonstrated that a skill-based expert system can be used for coordination level of control in multi-joint FES systems. Basic elements in this skill-based expert system are production rules. Production rules have the form of If-Then conditional expressions. A technique of automatic determination of these conditional expressions is presented in this paper. This technique for automatic synthesis of production rules uses fuzzy logic and artificial neural networks (ANN). The special class of fuzzy logic elements used in this research is called preferential neurons. The preferential neurons were used to estimate the relevance of each of the sensory inputs to the recognition of patterns defined as finite states. The combination of preferential neurons forms a preferential neural network. The preferential neural network belongs to a class of ANNs. The preferential neural network determined the set of finite states convenient for a skill-based expert system for different modalities of locomotion.

Ankle Joint↗

PubMed1993

Output space tracking control for above-knee prosthesis.

The control of a knee joint in an active above-knee prosthesis has been designed using the Lyapunov tracking method. A simulation of locomotion was done to prove that the tracking control in output space is a valuable real time control method for artificial legs. The data used for simulation was collected in able-bodied subjects while they walked on a powered treadmill. Human volunteers were braced with an ankle splint (limiting dorsi- and plantar flexion) and with a knee cage (limiting knee movements to the lateral plane). We studied the achieved tracking of the prescribed knee motion, deviations of the thigh movement from the prescribed trajectory, maximal angular deviations from the desired trajectory and the power consumption as functions of a limited maximal knee torque and a damping constant in the knee actuator. We found that the use of output tracking method is suitable for the design of appropriate hardware of an above-knee prosthesis and for real-time control.

Artificial Limbs↗

PubMed1993

Sensory nerve recording for closed-loop control to restore motor functions.

A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation we designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. We used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed-loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.

Action Potentials↗

PubMed1993

Electrical systems for improving locomotion after incomplete spinal cord injury: an assessment.

Simple systems for electrical stimulation (1-4 channels) with either surface, percutaneous, or implanted electrodes during locomotion were assessed in 10 subjects who had chronic, incomplete spinal cord injury (SCI). On average, the speed of locomotion was increased by 4 m/min independently of the subject's speed of locomotion without stimulation (0-50 m/min) while oxygen consumption was reduced somewhat. These simple systems can provide practical help, particularly for incomplete SCI subjects who can stand but are lacking or have very limited ability to walk. Further improvement in locomotion requires stabilization and reduction in the duration of the stance phase of locomotion.

Adult↗