基于改进样本熵的牵张反射起始点检测研究
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摘要
针对痉挛状态患者表面肌电(sEMG)信号质量差、易出现尖锐毛刺噪声且信号时序较短的问题,提出基于改进样本熵的牵张反射起始点(SRO)判定方法,利用固定长度的滑动窗对sEMG信号进行分帧,计算每帧信号改进样本熵,设定自适应阈值确定SRO,并分析对比了基于标准样本熵的SRO检测性能。实验结果表明,基于改进样本熵SRO最大识别率为89.06%,SRO识别能力优于标准样本熵(最大识别率为48.18%),且数据长度依赖性优于标准样本熵,在短时序列与含尖锐毛刺噪声sEMG信号处理上表现出更好的鲁棒性,为定量与细化上肢痉挛状态提供了基础。
Surface electromyography(sEMG) signals of spasticity patients can be problematic due to involuntary spikes and the poor signal quality. Also, the length of sEMG signals can be very short. In order to solve these problems, a stretch reflex onset(SRO) detection method based on modified sample entropy is proposed: Firstly, sEMG signals are framed by a fixed-length sliding window and the sample entropy of each frame is calculated. Afterwards, adaptive threshold is set to determine the SRO. The results show that the modified sample entropy achieves improved performance in SRO detection compared with the standard sample entropy, and shows better robustness in processing shorter time data series and against spurious background spikes. The recognition accuracy rate reaches 89.06% using modified sample entropy but only reaches 48.18% when using standard sample entropy. The findings from this study show that the proposed method can provide insight as to the mechanisms underlying the passive resistance.
Surface electromyography(sEMG) signals of spasticity patients can be problematic due to involuntary spikes and the poor signal quality. Also, the length of sEMG signals can be very short. In order to solve these problems, a stretch reflex onset(SRO) detection method based on modified sample entropy is proposed: Firstly, sEMG signals are framed by a fixed-length sliding window and the sample entropy of each frame is calculated. Afterwards, adaptive threshold is set to determine the SRO. The results show that the modified sample entropy achieves improved performance in SRO detection compared with the standard sample entropy, and shows better robustness in processing shorter time data series and against spurious background spikes. The recognition accuracy rate reaches 89.06% using modified sample entropy but only reaches 48.18% when using standard sample entropy. The findings from this study show that the proposed method can provide insight as to the mechanisms underlying the passive resistance.
引文
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[3] 秦文婷, 李放. 痉挛状态量化评定方法的进展[J]. 中华物理医学与康复杂志, 2017, 39(11): 870-872.QIN W T, LI F. Research progress in quantitative assessment method of spasticity [J]. Chinese Journal of Physical Medicine and Rehabilitation, 2017, 39(11): 870-872.
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[5] 成娟, 陈勋, 彭虎.基于样本熵的肌电信号起始点检测研究[J]. 电子学报, 2016, 44(2): 479-484.CHENG J, CHEN X, PENG H. An onset detection method for action surface electromyography based on sample entropy [J]. Acta Electronica Sinica, 2016, 44(2): 479-484.
[6] 董明利, 马闪闪, 张帆, 等. 基于核熵成分分析的流式数据自动分群方法[J]. 仪器仪表学报, 2017, 38(1): 206-211.DONG M L, MA SH SH, ZHNAG F, et al. Auto classification method of flow cytometry data based on kernel entropy component analysis [J]. Chinese Journal of Scientific Instrument, 2018, 38(1): 206-211.
[7] 吴正平, 魏欢, 杨翔宇, 等. 无线脑电睡眠分期系统设计及算法研究[J]. 电子测量与仪器学报, 2016, 30(12): 1861-1868.WU ZH P, WEI H, YANG X Y, et al. System design and algorithm research on wireless EEG signal sleep stage[J]. Journal of Electronic Measurement and Instrumentation, 2016,30(12):1861-1868.
[8] ZHANG X, ZHOU P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes [J]. Journal of Electromyography & Kinesiology, 2012, 22(6): 901-907.
[9] XIE H B, HE W X, LIU H. Measuring time series regularity using nonlinear similarity-based sample entropy [J]. Physics Letters A, 2008, 372(48): 7140-7146.
[10] MOLINA-PICó A, CUESTA-FRAU D, ABOY M, et al. Comparative study of approximate entropy and sample entropy robustness to spikes [J]. Artificial Intelligence in Medicine, 2011, 53(2): 97-106.
[11] MCKAY W B, OVECHKIN A V, VITAZ T W, et al. Long-lasting involuntary motor activity after spinal cord injury [J]. Spinal Cord. 2011, 49(1):87-93.
[12] LIU J, LIU Q P. Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury [J]. Biomedical Signal Processing & Control, 2016, 24(2):19-24.
[13] EDGERTON V R, ROLAND R R. Spasticity: A switch from inhibition to excitation [J]. Nature Medicine, 2010, 16(3): 270-271.
[14] ALCARAZ R, RIETA J J. A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms [J]. Biomedical Signal Processing and Control, 2010, 5(1): 1-14.
[15] YENTES J M, HUNT N, SCHMID K K, et al. The appropriate use of approximate entropy and sample entropy with short data sets [J]. Annals of Biomedical Engineering, 2013, 41(2): 349-365.
[16] ALCARAZ R, RIETA J J. A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation [J]. Medical & Biological Engineering & Computing, 2008, 46(7): 625-635.
[17] MALONE A, MELDRUM D, GLEESON J, et al. Reliability of surface electromyography timing parameters in gait in cervical spondylotic myelopathy [J]. Journal of Electromyography & Kinesiology, 2011, 21(6):1004-1010.
[18] XIE H B, GUO J Y, ZHENG Y P. Fuzzy approximate entropy analysis of chaotic and natural complex systems: Detecting muscle fatigue using electromyography signals [J]. Annals of Biomedical Engineering, 2010, 38(4): 1483-1496.
[19] WU S D, WU C W, LIN S G, et al. Analysis of complex time series using refined composite multiscale entropy [J]. Physics Letters A, 2014, 378(20): 1369-1374.
[20] CHEN W T, ZHUANG J, YU W X, et al. Measuring complexity using FuzzyEn, ApEn, and SampEn [J]. Medical Engineering & Physics, 2009, 31(1): 61-68.
[21] 李鹏. 基于熵测度的短时心脏动力学分析 [D]. 济南:山东大学,2014.LI P. Short-term analysis of carrier dynamics: based on entropy measures[D]. Ji’nan: Shandong University,2014.
[2] PARK H S, KIM J, DAMIANO D L. Development of a haptic elbow spasticity simulator (HESS) for improving accuracy and reliability of clinical assessment of spasticity[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(3): 361-370.
[3] 秦文婷, 李放. 痉挛状态量化评定方法的进展[J]. 中华物理医学与康复杂志, 2017, 39(11): 870-872.QIN W T, LI F. Research progress in quantitative assessment method of spasticity [J]. Chinese Journal of Physical Medicine and Rehabilitation, 2017, 39(11): 870-872.
[4] BAR-ON L, DESLOOVERE K, MOLENAERS G, et al. Identification of the neural component of torque during manually-applied spasticity assessments in children with cerebral palsy [J]. Gait & Posture, 2014, 40(3): 346-351.
[5] 成娟, 陈勋, 彭虎.基于样本熵的肌电信号起始点检测研究[J]. 电子学报, 2016, 44(2): 479-484.CHENG J, CHEN X, PENG H. An onset detection method for action surface electromyography based on sample entropy [J]. Acta Electronica Sinica, 2016, 44(2): 479-484.
[6] 董明利, 马闪闪, 张帆, 等. 基于核熵成分分析的流式数据自动分群方法[J]. 仪器仪表学报, 2017, 38(1): 206-211.DONG M L, MA SH SH, ZHNAG F, et al. Auto classification method of flow cytometry data based on kernel entropy component analysis [J]. Chinese Journal of Scientific Instrument, 2018, 38(1): 206-211.
[7] 吴正平, 魏欢, 杨翔宇, 等. 无线脑电睡眠分期系统设计及算法研究[J]. 电子测量与仪器学报, 2016, 30(12): 1861-1868.WU ZH P, WEI H, YANG X Y, et al. System design and algorithm research on wireless EEG signal sleep stage[J]. Journal of Electronic Measurement and Instrumentation, 2016,30(12):1861-1868.
[8] ZHANG X, ZHOU P. Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes [J]. Journal of Electromyography & Kinesiology, 2012, 22(6): 901-907.
[9] XIE H B, HE W X, LIU H. Measuring time series regularity using nonlinear similarity-based sample entropy [J]. Physics Letters A, 2008, 372(48): 7140-7146.
[10] MOLINA-PICó A, CUESTA-FRAU D, ABOY M, et al. Comparative study of approximate entropy and sample entropy robustness to spikes [J]. Artificial Intelligence in Medicine, 2011, 53(2): 97-106.
[11] MCKAY W B, OVECHKIN A V, VITAZ T W, et al. Long-lasting involuntary motor activity after spinal cord injury [J]. Spinal Cord. 2011, 49(1):87-93.
[12] LIU J, LIU Q P. Use of the integrated profile for voluntary muscle activity detection using EMG signals with spurious background spikes: A study with incomplete spinal cord injury [J]. Biomedical Signal Processing & Control, 2016, 24(2):19-24.
[13] EDGERTON V R, ROLAND R R. Spasticity: A switch from inhibition to excitation [J]. Nature Medicine, 2010, 16(3): 270-271.
[14] ALCARAZ R, RIETA J J. A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms [J]. Biomedical Signal Processing and Control, 2010, 5(1): 1-14.
[15] YENTES J M, HUNT N, SCHMID K K, et al. The appropriate use of approximate entropy and sample entropy with short data sets [J]. Annals of Biomedical Engineering, 2013, 41(2): 349-365.
[16] ALCARAZ R, RIETA J J. A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation [J]. Medical & Biological Engineering & Computing, 2008, 46(7): 625-635.
[17] MALONE A, MELDRUM D, GLEESON J, et al. Reliability of surface electromyography timing parameters in gait in cervical spondylotic myelopathy [J]. Journal of Electromyography & Kinesiology, 2011, 21(6):1004-1010.
[18] XIE H B, GUO J Y, ZHENG Y P. Fuzzy approximate entropy analysis of chaotic and natural complex systems: Detecting muscle fatigue using electromyography signals [J]. Annals of Biomedical Engineering, 2010, 38(4): 1483-1496.
[19] WU S D, WU C W, LIN S G, et al. Analysis of complex time series using refined composite multiscale entropy [J]. Physics Letters A, 2014, 378(20): 1369-1374.
[20] CHEN W T, ZHUANG J, YU W X, et al. Measuring complexity using FuzzyEn, ApEn, and SampEn [J]. Medical Engineering & Physics, 2009, 31(1): 61-68.
[21] 李鹏. 基于熵测度的短时心脏动力学分析 [D]. 济南:山东大学,2014.LI P. Short-term analysis of carrier dynamics: based on entropy measures[D]. Ji’nan: Shandong University,2014.