Spike sorting paradigm for classification of multi-channel recorded fasciculation potentials

• Distribution of ‘amplitude’ and ‘area’ can be used to discriminate waveforms recorded by HDsEMG.
• Interactive modification module increases the accuracy of classification and is efficient time-wise.
• The spike sorting output, tested by two-source method, is reproducible.
• In each subject, only 1–2 classes were continuingly firing, albeit numerous classes had been derived.
• The final result of this program will further be subject to many other research studies.

BackgroundFasciculation potentials (FPs) are important in supporting the electrodiagnosis of Amyotrophic Lateral Sclerosis (ALS). If classified by shape, FPs can also be very informative for laboratory-based neurophysiological investigations of the motor units.MethodsThis study describes a Matlab program for classification of FPs recorded by multi-channel surface electromyogram (EMG) electrodes. The program applies Principal Component Analysis on a set of features recorded from all channels. Then, it registers unsupervised and supervised classification algorithms to sort the FP samples. Qualitative and quantitative evaluation of the results is provided for the operator to assess the outcome. The algorithm facilitates manual interactive modification of the results. Classification accuracy can be improved progressively until the user is satisfied. The program makes no assumptions regarding the occurrence times of the action potentials, in keeping with the rather sporadic and irregular nature of FP firings.ResultsTen sets of experimental data recorded from subjects with ALS using a 20-channel surface electrode array were tested. A total of 11891 FPs were detected and classified into a total of 235 prototype template waveforms. Evaluation and correction of classification outcome of such a dataset with over 6000 FPs can be achieved within 1–2 days. Facilitated interactive evaluation and modification could expedite the process of gaining accurate final results.ConclusionThe developed Matlab program is an efficient toolbox for classification of FPs.