Relationship between EMG-detected and ultrasound-detected fasciculations in amyotrophic lateral sclerosis: A prospective cohort study

OBJECTIVES

Fasciculation potentials (FP) are an important consideration in the electrophysiological diagnosis of ALS. Muscle ultrasonography (MUS) has a higher sensitivity in detecting fasciculations than electromyography (EMG), while in some cases, it is unable to detect EMG-detected fasciculations. We aimed to investigate the differences of FP between the muscles with and without MUS-detected fasciculations (MUS-fas).

METHODS

Thirty-one consecutive patients with sporadic ALS were prospectively recruited and in those, both needle EMG and MUS were performed. Analyses of the amplitude, duration, and number of phases of EMG-detected FPs were performed for seven muscles per patient, and results were compared between the muscles with and without MUS-fas in the total cohort.

RESULTS

The mean amplitude and phase number of FP were significantly lower in patients with EMG-detected FP alone (0.39 ± 0.25 mV and 3.21 ± 0.88, respectively) than in those with both FP and MUS-fas (1.22 ± 0.92 mV and 3.74 ± 1.39, respectively; p < 0.0001 and p = 0.017, Welch's t-test).

CONCLUSION

Small FP may be undetectable with MUS. MUS cannot replace EMG in the diagnostic approach for ALS.

SIGNIFICANCE

Clinicians should use a combination of EMG and MUS for the detection and quantitative analysis of fasciculation in ALS.

Quantitative analysis of the features of fasciculation potentials and their relation with muscle strength in amyotrophic lateral sclerosis

This study aimed to quantitatively analyze fasciculation potentials (FPs) and to investigate their relationship with muscle strength in amyotrophic lateral sclerosis (ALS). Fifty-one patients with sporadic ALS or progressive muscular atrophy (25 men, 26 women, mean age of 68 years) underwent needle EMG. We determined the duration, phase number, and amplitude of FPs from three muscles (upper trapezius, biceps brachii, and tibialis anterior) and examined their relations with muscle strength. In total, 878 FPs were analyzed. FP duration displayed a significant negative relation with the strength of all three muscles; the weaker muscles showed longer durations of FPs than the muscles with normal strength. The amplitude and phase number were not related with muscle strength, but there were significant correlations between the duration and amplitude of FPs in the trapezius and tibialis anterior muscles. The longer duration of FPs in muscles with weak strength suggests that the morphological changes of FPs were caused by temporal dispersion through progressively degenerating and/or immature reinnervating motor branches, and were observed uniformly in different muscles along with disease progression.

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

BACKGROUND

Fasciculation 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.

METHODS

This 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.

RESULTS

Ten 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.

CONCLUSION

The developed Matlab program is an efficient toolbox for classification of FPs.