Steen Andreassen

Quantitative analysis of individual motor unit potentials: a proposition for standardized terminology and criteria for measurement

The physiology of the motor unit potential (MUP) is reviewed. The aim is to identify the electrophysiological events in the motor unit that generate the individual parts of the MUP. This is based on insight gained from new experimental techniques, such as single-fiber electromyography (EMG), scanning EMG, and simulation studies of the MUP. A terminology for the different parts of the MUP is also suggested, and nine parameters used to describe different features of the MUP are delineated: duration, spike duration, amplitude, area, spike area, phases, turns, satellites, and variability. Technical aspects, such as electrode type, filtering, and sampling rate of the computers, are discussed as well. In Appendix A, different manual and computer-aided methods for quantitative MUP analysis are described. Despite minor systematic differences between the methods, MUP durations measured by different methods correlate highly with each other (Appendix B). The manual and computer-aided methods have comparable variability between repeated measurements.

Impaired regulation of force and firing pattern of single motor units in patients with spasticity.

Patients with spasticity were unable to maintain a constant force of the anterior tibial muscle. The force at maximal effort was reduced to less than 40% of normal, partly because motor units fired at a reduced rate even at high levels of contraction. Force and instantaneous frequency fluctuated slowly. The fast regulation of the firing rate, which characterises normal muscle, was absent. The variation between successive intervals was less than normal and the serial correlation coefficient between intervals increased.

A munin network for the median nerve-a case study on loops

Causal probabilistic networks have proved to be a useful knowledge representation tool for domains having a natural description in terms of causal relations involving uncertainty between domain concepts. This article describes a network modeling diseases affecting the median nerve. The qualitative structure of the model and the quantitative pathophysiological

Limitations in the servo-regulation of soleus muscle stiffness in premammillary cats

It has recently been proposed that proprioceptive reflexes may regulate muscle stiffness. To test this hypothesis, we have studied the effects of the amplitude, shape and velocity of length perturbations, as well as the reflex response to muscle potentiation, in the soleus muscle of cats decerebrated at the premammillary level. A twenty-fold increase in stretch amplitude (from 0.1 to 2.0 mm) caused a two-fold decrease in the reflex-mediated incremental stiffness. Although the intrinsic muscle stiffness doubled for triangular ramp stretches (0.5–5.0 mm/sec) compared to rectangular stretches of the same amplitude (1.0 mm), the net reflex component of stiffness was unaltered. The only singularity was observed using 10 Hz perturbations, a frequency that was presumed to cause resonance around the segmental reflex loop. The intrinsic stiffness of soleus potentiated by 32–66% during prolonged muscle activation, whereas the reflex component was essentially unchanged. These data suggest that in the premammillary decerebrate the gain of Golgi tendon organ feedback is negligibly low; the observed reflex effects are attributed to spindle group Ia and II feedback.

Recording from a Single Motor Unit During Strong Effort

During strong voluntary effort it is rarely possible to identify the action potentials from single motor units. In large muscles the most selective recordings are obtained with bipolar wire electrodes. To elucidate this experimental finding we have calculated the extracellular field around a single muscle fiber from an intracellular muscle action potential. This model showed that the selectivity of a bipolar electrode is high provided: i) the diameter of the recording surfaces is less than half the diameter of the muscle fibers; ii) the center distance between the recording surfaces is of the same order or smaller than the diameter of the muscle fibers, and when iii) the center-line between the recording surfaces is oriented perpendicular to the direction of the muscle fibers.

Using physiological models and decision theory for selecting appropriate ventilator settings.

ObjectiveTo present a decision support system for optimising mechanical ventilation in patients residing in the intensive care unit.MethodsMathematical models of oxygen transport, carbon dioxide transport and lung mechanics are combined with penalty functions describing clinical preference toward the goals and side-effects of mechanical ventilation in a decision theoretic approach. Penalties are quantified for risk of lung barotrauma, acidosis or alkalosis, oxygen toxicity or absorption atelectasis, and hypoxaemia.ResultsThe system is presented with an example of its use in a post-surgical patient. The mathematical models describe the patient’s data, and the system suggests an optimal ventilator strategy in line with clinical practice.ConclusionsThe system illustrates how mathematical models combined with decision theory can aid in the difficult compromises necessary when deciding on ventilator settings.

A model-based approach to insulin adjustment

A differential equation model of carbohydrate metabolism was implemented in the form of a causal probabilistic network. This permitted explicit represen-tations of the uncertainties associated with model based predictions of 24-hour blood glucose profiles. In addition, the implementation gave automatic learning and adjustment of model parameters based on measured blood glucose profiles. Insulin therapy was adjusted using a decision theoretical approach. Losses were assigned to blood glucose values that deviated from normal, and the insulin therapy was adjusted to minimize the expected total loss. The system was tested retrospectively on cases from 12 insulin dependent patients and seemed to compare favourably with clinical practice.

MEDICAL EXPERT SYSTEMS BASED ON CAUSAL PROBABILISTIC NETWORKS

Abstract Causal probabilistic networks (CPNs) offer new methods by which you can build medical expert systems that can handle all types of medical reasoning within a uniform conceptual framework. Based on the experience from a commercially available system and a couple of large prototype systems, it appears that CPNs are now an attractive alternative to other methods. A CPN is an intensional model of a domain. and it is therefore conceptually much closer to qualitative reasoning systems and to simulation systems than to rule-based or logic-based systems. Recent progress in Bayesian inference in networks has yielded computationally efficient methods. The inference method used follows the fundamental axioms of probability theory, and gives a sound framework for causal and diagnostic (deductive and abductive) reasoning under uncertainty. Experience with the prototypes indicates that it may be possible to use decision theory as a rational approach to test planning and therapy planning. The way in which knowledge is acquired and represented in CPNs makes it easy to express ‘deep knowledge’ for example in the form of physiological models, and the facilities for learning make it possible to make a smooth transition from expert opinion to statistics based on empirical data.

Mechanical and electromyographic responses to stretch of the human anterior tibial muscle at different levels of contraction

SummaryThe EMG response and the mechanical response to 2 degree stretch of the human anterior tibial muscle was studied during contractions ranging from 0% to 80% of maximal voluntary contraction (MVC). The EMG response showed three distinct peaks M1, M2, and M3 with peak latencies of 59 ms, 86 ms, and 120 ms respectively. At low background torques M1 dominated while M2 and M3 were small or absent. M2 and M3 dominated above 40% of MVC and M2 in particular showed “automatic gain compensation”, i.e. it constituted a — more or less — constant proportion of the background EMG for all contraction levels. The ratio between M1 amplitude and background EMG steadily decreased with contraction level. Even though the summed contributions of M1, M2, and M3 to some degree showed automatic gain compensation, this was not the case for the mechanical response to stretch. Between 0% and 30% of MVC the reflex mediated mechanical response increased approximately in proportion to the contraction level, but the reflex mediated mechanical response peaked at 40% of MVC and declined to zero at 80% of MVC. This discrepancy between EMG and mechanical response was explained by a simple model. The regression line between rectified and filtered tibialis anterior EMG and torque was used to predict the mechanical response from the EMG response. At increasing contraction levels the twitch elicited by supramaximal electrical stimulation decreases, and we reduced the predicted mechanical response by the same factor as the twitch. This simple model predicted the mechanical response for all contraction levels, making it possible to assess the “functionality” of reflexes even when accurate measurements of muscle force or intrinsic muscle properties are not possible.

The automatic lung parameter estimator (ALPE) system: non-invasive estimation of pulmonary gas exchange parameters in 10-15 minutes.

Objective.Clinical measurements of pulmonary gas exchangeabnormalities might help prevent hypoxaemia and be useful in monitoringthe effects of therapy. In clinical practice single parameters are oftenused to describe the abnormality e.g., the “effectiveshunt.” A single parameter description is often insufficient,lumping the effects of several abnormalities. A more detailed picturecan be obtained from experiments where FIO2 is varied and twoparameters estimated. These experiments have previously taken30–40 minutes to complete, making them inappropriate for routineclinical use. However with automation of data collection and parameterestimation, the experimental time can be reduced to 10–15 minutes.Methods.A system has been built for non-invasive, Automatic,Lung Parameter Estimation (ALPE). This system consists of a ventilator,a gas analyser with pulse oximeter, and a computer. Computer programscontrol the experimental procedure, collect data from the ventilator andgas analyser, and estimate pulmonary gas exchange parameters. Use of theALPE system, i.e. in estimating gas exchange parameters and reducingexperimental time, has been tested on five normal subjects, two patientsbefore and during diuretic therapy, and on 50 occasions in patientsbefore and after surgical intervention. Results.The ALPE systemprovides estimation of pulmonary gas exchange parameters from a simple,clinical, non-invasive procedure, automatically and quickly. For normalsubjects and in patients receiving diuretic therapy, data collection byclinicians familiar with ALPE took (mean ± SD) 13 min 40 sec± 1 min 23 sec. For studies on patients before and after surgery,data collection by an intensive care nurse took (mean ± SD) 10min 47 sec ± 2 min 14 sec. Parameter estimates were: for normalsubjects, shunt = 4.95% ± 2.64% and fA2 = 0.89± 0.01; for patients with heart failure prior to diuretictherapy, patient 1, shunt = 11.50% fA2 = 0.41, patient 2 shunt =11.61% fA2 = 0.55; and during therapy: patient 1, shunt =11.51% fA2 = 0.71, patient 2, shunt = 11.22% fA2 = 0.49.Conclusions.The ALPE system provides quick, non-invasiveestimation of pulmonary gas exchange parameters and may have severalclinical applications. These include, monitoring pulmonary gas exchangeabnormalities in the ICU, assessing post-operative gas exchangeabnormalities, and titrating diuretic therapy in patients with heartfailure.