目的探讨癫痫患者的MMSE和MoCA评估的特点,研究两者的相关性,并评价其临床价值。方法选择69例癫痫患者作为癫痫组,35例正常人为对照组,均进行简易精神状态检查量表(Min imental state examination,MMSE)测查,排除得分低于正常值者,再进...目的探讨癫痫患者的MMSE和MoCA评估的特点,研究两者的相关性,并评价其临床价值。方法选择69例癫痫患者作为癫痫组,35例正常人为对照组,均进行简易精神状态检查量表(Min imental state examination,MMSE)测查,排除得分低于正常值者,再进行蒙特利尔认知评估(Montreal cognitive assessment,MoCA),记录各项得分。结果癫痫组的MMSE总分低于对照组(P<0.05),计算力和回忆认知功能水平下降(P<0.05);MoCA总分低于对照组(P<0.05),执行、注意和延迟回忆认知功能水平下降(P<0.05)。癫痫组和对照组的MoCA与MMSE总得分成正相关(r=0.626,P<0.05),MoCA对MMSE达到正常值范围内的癫痫组的认知功能障碍检出率是49.3%。结论癫痫患者的MMSE和MoCA评分均有降低,但MoCA相对MMSE敏感性较高,对评价其认知功能损害程度相对更为全面和客观。展开更多
In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT...In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT coefficients of clean speech are modeled by a Laplacian or a Gamma distribution and the DCT coefficients of the noise are Gaussian distributed. Then,MMSE estimators under speech presence uncertainty are derived. Furthermore,the proper estimators of the speech statistical parameters are proposed. The speech Laplacian factor is estimated by a new deci-sion-directed method. The simulation results show that the proposed algorithm yields less residual noise and better speech quality than the Gaussian based speech enhancement algorithms proposed in recent years.展开更多
基金the Natural Science Foundation of Jiangsu Province (No.BK2006001).
文摘In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT coefficients of clean speech are modeled by a Laplacian or a Gamma distribution and the DCT coefficients of the noise are Gaussian distributed. Then,MMSE estimators under speech presence uncertainty are derived. Furthermore,the proper estimators of the speech statistical parameters are proposed. The speech Laplacian factor is estimated by a new deci-sion-directed method. The simulation results show that the proposed algorithm yields less residual noise and better speech quality than the Gaussian based speech enhancement algorithms proposed in recent years.
