Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms
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 MET...
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| Format: | Article |
| Language: | English |
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Editora Champagnat
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| Series: | Fisioterapia em Movimento |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-51502013000300009&lng=en&tlng=en |
| _version_ | 1818002191983575040 |
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| author | Arthur de Sá Ferreira Fernando Silva Guimarães Manuel Armando Ribeiro Magalhães Regina Coeli Souza e Silva |
| author_facet | Arthur de Sá Ferreira Fernando Silva Guimarães Manuel Armando Ribeiro Magalhães Regina Coeli Souza e Silva |
| author_sort | Arthur de Sá Ferreira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T03:42:31Z |
| format | Article |
| id | doaj.art-a1989f650ed549e2b629b24bcb915e3a |
| institution | Directory Open Access Journal |
| issn | 1980-5918 |
| language | English |
| last_indexed | 2024-04-14T03:42:31Z |
| publisher | Editora Champagnat |
| record_format | Article |
| series | Fisioterapia em Movimento |
| spelling | doaj.art-a1989f650ed549e2b629b24bcb915e3a2022-12-22T02:14:26ZengEditora ChampagnatFisioterapia em Movimento1980-591826355956710.1590/S0103-51502013000300009S0103-51502013000300009Accuracy and learning curves of inexperienced observers for manual segmentation of electromyogramsArthur de Sá Ferreira0Fernando Silva Guimarães1Manuel Armando Ribeiro Magalhães2Regina Coeli Souza e Silva3Centro Universitário Augusto MottaCentro Universitário Augusto MottaCentro Universitário Augusto MottaCentro Universitário Augusto MottaINTRODUCTION: 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-51502013000300009&lng=en&tlng=enElectromyographyComputer simulationMuscle |
| spellingShingle | Arthur de Sá Ferreira Fernando Silva Guimarães Manuel Armando Ribeiro Magalhães Regina Coeli Souza e Silva Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms Fisioterapia em Movimento Electromyography Computer simulation Muscle |
| title | Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| title_full | Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| title_fullStr | Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| title_full_unstemmed | Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| title_short | Accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| title_sort | accuracy and learning curves of inexperienced observers for manual segmentation of electromyograms |
| topic | Electromyography Computer simulation Muscle |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-51502013000300009&lng=en&tlng=en |
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