扫描心电图图象的鲁棒性识别算法

为了实现心电图与重建心电向量图的联合诊断，提出了一种由标准同步12异联心电图扫描图象，通过识别、判决、分类，直接恢复心电信息，以便重建出心电向量的解决方案。由于心电信号复杂多变易受噪声干扰，且12导联心电信号特征较为接近，分类特征不明显，因而使得心电图图象的识别工作较为困难。该方法是通过基于最小距离准则的鲁棒识别算法来对扫描心电图进行有效的分类线形识别，并对识别后的信号进行滤波处理，以消除粗大误差的干扰。实验结果证明，即使当心电信号本身存在各类疾病信息，或者被噪声干扰的情况下，仍可以取得良好的分类效果，且能基本保持信号原貌，表明该算法具有较强的鲁棒性。

A Robust Recognition Algorithm of Scanned ECG Images

To realize ECG (electrocardiogram) and VCGr (reconstructed vectorcardiogram) combined diagnose, a solution is put forward to recover cardio signals from scanned standard synchronous 12 lead ECG image directly in order to reduce fund devotion. The cardio signals are complex, diverse and sensitive to noises, the characteristics of 12 lead ECG signals are similar with each other, and the qualities of scanned image are variant. All of these factors lead ECG signals difficult to be identified. To resolve the problems, original color image is firstly transformed to black-white image according to the defined threshold section. Then the least distance rule is applied to search the corresponding pixels for leads one by one in adjacent fields. After searching all pixels in the black white image, some missed pixels are compensated by interpolation. So the standard synchronous 12 lead ECG base lines can be recognized and ascertained. At the end, a filter is applied to remove gross errors produced by previous steps. Experiments show that the algorithm is robust and efficient because it can identify and keep the shape of original signal even when the signal contains some disease information or is corrupted by noises.