Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms

Abstract

INTRODUCTION: The shape-varying format of surface electromyograms introduces errors in the detection of contraction events. OBJECTIVE: To investigate the accuracy and learning curves of inexperienced observers to detect the quantity of contraction events in surface electromyograms. MATERIALS AND METHODS: Six observers performed manual segmentation in 1200 shape-varying waveforms simulated using a phenomenological model with variable events, smooth changes in amplitude, marked on-off timing, and variable signal-to-noise ratio (0-39 dB). Segmentation was organized in four sessions with 15 blocks of 20 signals each. Accuracy and learning curves were modeled per block by linear and power regression models and tested for difference among sessions. Cut-off values of signal-to-noise ratio for optimal manual segmentation were also estimated. RESULTS: The accuracy curve showed no significant linear trend throughout blocks and no difference among sessions 1-2-3-4 (87% [85; 89], 87% [85; 89], 87% [85; 89], 87% [81; 88]; p = 0.691). Accuracy was low for detection of 1 event (AUC = 0.40; sensitivity = 44%; specificity = 43%; cut-off = 12.9 dB) but was high and affected by the signal-to-noise ratio for detection of two events (AUC = 0.82; sensitivity = 77%; specificity = 76%; cut-off = 7.0 dB). The learning curve showed a significant power regression (p < 0.001) with decreasing values of learning percentages (time duration to complete the task) among sessions 1-2-3-4 (86.5% [68; 94], 76% [68; 91], 62% [38; 77], and 57% [52; 75]; p = 0.002). CONCLUSION: Inexperienced observers exhibit high, not trainable accuracy and a practice-dependent shortening in the time spent to detect the quantity of contraction events in simulated surface electromyograms.

Electromyographic analysis of maximal voluntary contraction of female pelvic floor muscles: Intrarater and interrater reliability study

“…Thus, although there is no standard method to evaluate the quality of sEMG and based on our findings, we recommend the use of quantitative measures such as the SNR to evaluate the sEMG signal quality. Automated methods of onset and offset detection are fast and accurate, 18 but they require deeper knowledge of computational routine programming for more specific analysis. Differently, the visual inspection method of the onset and offset of contraction is subjective and dependent on the rater's experience, despite its convenience for clinical practice.…”

“…However, in our study, a relationship between qualitative and quantitative evaluation was found, i.e., the higher the SNR, the better the signal quality. According to Ferreira et al 18 accuracy for determining sEMG events by visual inspection is low when the SNR is low. Thus, although there is no standard method to evaluate the quality of sEMG and based on our findings, we recommend the use of quantitative measures such as the SNR to evaluate the sEMG signal quality.…”