Iyer V K, Ploysongsang Y, Ramamoorthy P A
Criticare Systems Waukesha, Wisconsin.
Crit Rev Biomed Eng. 1990;17(6):531-84.
The high dependence of conventional optimal filtering methods on the a priori knowledge of the signal and noise statistics render them ineffective in dealing with signals whose statistics cannot be predetermined accurately. Adaptive filtering methods offer a better alternative, since the a priori knowledge of statistics is less critical, real time processing is possible, and the computations are less expensive for this approach. Adaptive filtering methods compute the filter coefficients "on-line", converging to the optimal values in the least-mean square (LMS) error sense. Adaptive filtering is therefore apt for dealing with the "unknown" statistics situation and has been applied extensively in areas like communication, speech, radar, sonar, seismology, and biological signal processing and analysis for channel equalization, interference and echo canceling, line enhancement, signal detection, system identification, spectral analysis, beamforming, modeling, control, etc. In this review article adaptive filtering in the context of biological signals is reviewed. An intuitive approach to the underlying theory of adaptive filters and its applicability are presented. Applications of the principles in biological signal processing are discussed in a manner that brings out the key ideas involved. Current and potential future directions in adaptive biological signal processing are also discussed.
传统的最优滤波方法高度依赖于信号和噪声统计特性的先验知识，这使得它们在处理统计特性无法准确预先确定的信号时效率低下。自适应滤波方法提供了更好的选择，因为统计特性的先验知识不那么关键，可以进行实时处理，并且这种方法的计算成本较低。自适应滤波方法“在线”计算滤波器系数，在最小均方（LMS）误差意义下收敛到最优值。因此，自适应滤波适用于处理“未知”统计情况，并已广泛应用于通信、语音、雷达、声纳、地震学以及生物信号处理与分析等领域，用于信道均衡、干扰和回声消除、线路增强、信号检测、系统识别、频谱分析、波束形成、建模、控制等。在这篇综述文章中，对生物信号背景下的自适应滤波进行了综述。介绍了一种对自适应滤波器基础理论及其适用性的直观方法。以揭示其中关键思想的方式讨论了这些原理在生物信号处理中的应用。还讨论了自适应生物信号处理的当前和潜在未来方向。
