Computer programms implement the adaptive algorithms for ECG signal real-time filtering

In this version of the computer programs, the data type has been changed from “integer” to “longint” for processing longer signals. Multilevel noise estimation and, respectively, adaptive switching of a larger number of filter sets are used. The filter parameters are adjusted by numerical simulation for a very wide range of variance of the additive Gaussian noise from its absence and very high input SNRs to their negative values. An increase in the number of possible parameter values that can be adaptively switched during processing improves the filters dynamic and statistical properties, and does not significantly decrease the processing speed. The algorithms parameters are given in the “filters.txt” file. Optionally, the number of parameters can be reduced by setting the same filter parameters for the next sets. For the parameters adjustment an optimization algorithm was not used. Therefore, the parameters only close to optimal have been selected. A typical ECG cycle is used as a model signal for numerical simulation and evaluation of the filter efficiency. As examples, the parameters of the proposed filtering algorithms are adjusted for the signals from the NSTB and PTB Physionet databases at the sampling rates of 360 Hz and 1000 Hz. The advantages of the proposed algorithms for non-stationary noise suppression in ECG are their high efficiency and low processing delay, allowing high-speed performances in real time mode.在本版本的计算机程序中，为处理更长时长的信号，已将数据类型从“整数（integer）”调整为“长整数（longint）”。本程序采用多级噪声估计技术，并针对更多滤波器组实现了自适应切换。滤波器参数通过数值仿真进行调整，仿真覆盖了加性高斯噪声的极宽方差范围：从无噪声、极高输入信噪比（Signal-to-Noise Ratio, SNR）直至信噪比为负值的场景。提升处理过程中可自适应切换的参数取值数量，可改善滤波器的动态与统计特性，且不会显著降低处理速度。算法参数已存储于"filters.txt"文件中。可选地，若为后续滤波器组设置相同的参数，可缩减所需调整的参数总量。本次参数调整未采用优化算法，因此仅选取了接近最优值的参数配置。本次数值仿真与滤波器性能评估以典型心电图（Electrocardiogram, ECG）周期作为模型信号。以采样率分别为360 Hz与1000 Hz的NSTB、PTB生理信号数据库（PhysioNet）中的信号为例，对所提出的滤波算法参数进行了调整。所提出的算法用于心电图非平稳噪声抑制的优势在于：滤波效率高且处理延迟低，可满足实时场景下的高速处理需求。