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Optimization of threshold values for automated analysis of EMG recordings.

University of Zurich, Switzerland. Optimization of threshold values for automated analysis of EMG recordings. L.M. Gallo*, P. Rompré, G.J. Lavigne and S. Palla. Long-time EMG. Orofacial motor activity during sleep is still a controversial issue. Ambulant long-time EMG recording.

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Optimization of threshold values for automated analysis of EMG recordings.

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  1. University of Zurich, Switzerland Optimization of threshold values for automated analysis of EMG recordings. L.M. Gallo*, P. Rompré, G.J. Lavigne and S. Palla

  2. Long-time EMG Orofacial motor activityduring sleepis still a controversial issue.

  3. Ambulant long-time EMG recording • Solberg et al., 1975 • Burgar & Rugh, 1983 • Lotzmann et al., 1992 • Bowley JF et al., 1993 • Rivera-Morales & McCall, 1995 • Gallo & Palla, 1995

  4. Normative study Gallo LM, Salis Gross SS & Palla S Nocturnal masseter EMG activity of healthy subjects in a natural environment.J Dent Res78(8):1436-1444, 1999

  5. biosignal recorder personal computer EMG tracing Recording system

  6. Subjects of the normative study 21 asymptomatic volunteers (6 females and 15 males)mean age 31 (22 to 37)

  7. A [%MVC] 100 90 T T E1 E2 80 t>t 70 0 E Stand-by time t0 = 5 s 1 60 • contraction episodes 50 40 A 30 1 max 20 E A 2 2 max 10 A 0 t [s] 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Signal analysis • A0: amplitude threshold • t0: stand-by time • TE1, TE2: duration • Amax1, Amax2: maximum amplitude

  8. [mV] Signal levels

  9. Contraction episodes • N: 72 ± 29 per night 11 ± 4 per hour • Amean: 26 ± 6 %MVC • Amax: 38 ± 9 %MVC • T: 5 ± 2 s • I: 124 ± 58 %MVCs Normative study

  10. Aim of this study Optimization of a simple threshold criterionfor best separation in the automated analysesof EMG recordingsin bruxers and controls.

  11. 120 V right masseter 60 V left masseter 60 V right temporalis 60 V left temporalis 140 V digastric Polysomnography: EMG

  12. Present study Data from a single masseter channel of polysomnographic recordings.

  13. POLYSOM: ti = 7.8125 ms (128 Hz) 330 mV BSR: ti = 10 ms (100 Hz) BSR: ti = 100 ms (10 Hz) BSR: ti = 500 ms (2 Hz) Reformatting of POLYSOM signal

  14. Data analysis • N: number of episodes • Amean: mean amplitude • Amax: maximum amplitude • T: duration • I: integral amplitude over time

  15. Subjects 10 bruxers (5 f & 5 m, aged 23-39 y) 10 controls (5 f & 5 m, aged 21-45 y)

  16. 5% • 10% • 15% • 20% [du] [du] bruxers controls Variation of threshold A0

  17. Sleep duration bruxers: 8:24 ± 0:39controls: 8:25 ± 0:28

  18. Example: Amax [du] bruxers controls threshold

  19. Best separation of analysis parameters A0 = 10 %MVC

  20. Contraction episodes • N: 178 ± 37 per night 22 ± 4 per hour • Amean: 26 ± 4 %MVC • Amax: 46 ± 8 %MVC • T: 7 ± 2 s • I: 210 ± 65 %MVCs Bruxers (threshold A0 = 10 %MVC)

  21. Contraction episodes • N: 103 ± 30 per night 14 ± 3 per hour • Amean: 22 ± 2 %MVC • Amax: 36 ± 6 %MVC • T: 5 ± 1 s • I: 133 ± 28 %MVCs Controls (threshold A0 = 10 %MVC)

  22. Comparison bruxers-controls

  23. Contraction episodes • N/night: p < 0.01 • N/hour: p < 0.01 • Amean: p < 0.01 • Amax: p < 0.01 • T: p < 0.01 • I: p < 0.01 Difference controls-bruxers at 10 %MVC

  24. Separation bruxers/controls A lower threshold of 10 %MVCappears to be an effective criterion yielding the best separation between bruxers and controlsin an automated analysis of masseter EMG recordings.

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