Computer dissection of peripheral nerve bundle activity

Multiple action potential waveforms of single units in man as signs of variability in conductivity of their myelinated fibres

Percutaneous microneurography was performed with concentric needle electrodes to record neural activity from myelinated fibres in human peripheral nerves. Template matching techniques were used together with interspike interval analysis and studies on functional class, receptive field characteristics, conduction velocities and other single fibre properties to classify single units. Sometimes the same fibres exhibited different action potentials at the same time. The potentials had some common features, but differed either in their waveform types or only in duration. There was a correlation between the occurrence of the different potential shapes and firing frequency of the studied unit. The outcome of the studies suggested that there was a common denominator which could explain the observations. Most likely, momentary fluctuations in excitability of the myelinated fibres occurring during the relative refractory period or the supernormal period were responsible for the variations in complexity of the studied units due to a partial block of fibre propagation probably caused by the recording electrode. Thus, action potentials deriving from the same axon may not always have the same shapes. Methods for unit classification, such as template matching, are discussed in the light of our findings.

Separation of action potentials in multiunit intrafascicular recordings

Classification of action potentials in multiunit recordings was based on the use of various types of features to uniquely characterize action potentials from different cells. We compared classification results obtained using three types of descriptive features: digitized data points, amplitude and duration (time domain) parameters, and fast Fourier transform (FFT) coefficients. Digitized data points used as descriptive features provided good classification success and required minimal computation. Time-domain features gave comparable results but required more computation. FFT coefficients were less effective than the other features. As the signal-to-noise ratio of the recordings increased, smaller differences in feature values could be discriminated.

A technique for the detection, decomposition and analysis of the EMG signal

In the present paper we have described a system for acquiring, processing and decomposing EMG signals for the purpose of extracting as many motor unit action potential trains as possible with the greatest level of accuracy. This system consists of 4 main sections. The first section consists of methodologies for signal acquisition and quality verification. Three channels of EMG signals are acquired using a quadripolar needle electrode designed to enhance discrimination among different MUAPs. An automated experiment control system is devised to free the experimenter from the burden of experiment detailed surveillance and bookkeeping; and to allow on-line assessment of the EMG signal quality in terms of decomposition suitability. The second section consists of methodologies for signal sampling and conditioning. The EMG signal is bandpass filtered (between 1 kHz and 10 kHz), sampled and compressed by eliminating parts of the signal under a preset threshold level. The third section consists of a signal decomposition technique where motor unit action potential trains are extracted from the EMG signal using a highly computer assisted interactive algorithm. The algorithm uses a continuously updated template matching routine and firing statistics to identify MUAPs in the EMG signal. The templates of the MUAPs are continuously updated to enable the algorithm to function even when the shape of a specific MUAP undergoes slow variations. The fourth section deals with ways in which to analyze and display the results. The more frequently used representation formats are: (1) display of MUAP wave shapes; (2) impulse trains representing motor unit firings; (3) IPI plots where time interval between successive firings of the same motor unit is plotted vs. time of the muscle contraction; (4) firing rate plots where the estimated time-varying mean firing rate of the detected motor units is plotted vs. time of the muscle contraction. The performance of the system has been tested in terms of: (1) consistency among results obtained by different operators; (2) accuracy evaluated on synthetic EMG signal; (3) indirect measure of accuracy on real EMG signal by comparing results pertaining the same motor unit action potential trains derived by two EMG signals, independently and simultaneously recorded from two different electrodes.