Abstract
Waveform analysis of compound muscle action potential (CMAP) is important in the detailed analysis of conduction velocities of each axon as seen in temporal dispersion. This understanding is limited because conduction velocity distribution cannot be easily available from a CMAP waveform. Given the recent advent of artificial intelligence, this study aimed to assess whether conduction velocity (CV) distribution can be inferred from CMAP by the use of deep learning algorithms. Simulated CMAP waveforms were constructed from a single motor unit potential and randomly created CV histograms (n = 12,000). After training the data with various recurrent neural networks (RNNs), CV inference was tested by the network. Among simple RNNs, long short-term memory (LSTM) and gated recurrent unit, the best accuracy and loss profiles, were shown by two-layer bidirectional LSTM, with training and validation accuracies of 0.954 and 0.975, respectively. Training with the use of a recurrent neural network can accurately infer conduction velocity distribution in a wide variety of simulated demyelinating neuropathies. Using deep learning techniques, CV distribution can be assessed in a non-invasive manner.
Highlights
A diagnosis of demyelinating neuropathy is significantly dependent on neurophysiological test results, as noted in the diagnostic criteria for chronic inflammatory demyelinating polyneuropathy, and Guillain–Barré syndrome [1, 2]
temporal dispersion (TD) is obtained by summating the peaks of opposite polarity generated by fast- and slow-conducting axons in a normal condition as well as peripheral neuropathy, [3] while its magnitude is pathologically augmented by greater degrees of conduction slowing caused by demyelination
Preparation of Single Motor Unit Potential A representative single motor unit potential was obtained from a published waveform recording obtained by stimulating the median nerve, and recorded from the abductor pollicis brevis (APB) of a healthy subject [7]
Summary
A diagnosis of demyelinating neuropathy is significantly dependent on neurophysiological test results, as noted in the diagnostic criteria for chronic inflammatory demyelinating polyneuropathy, and Guillain–Barré syndrome [1, 2]. The morphological evaluation of compound muscle action potential (CMAP) is critical for understanding underlying demyelination in terms of its degree, and variability in patients with demyelinating neuropathies. Methods used to directly measure CVs from individual axons include the near nerve technique, selective nerve fiber stimulation using special tungsten microelectrodes, F-wave-based testing, and collision-based testing [4]. These techniques can be invasive and time-consuming, with a limited number of samples obtained, and are not widely utilized in clinical settings. Methods based on the analysis of compound muscle or sensory
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