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Abstract: We propose a systematic ECG quality classification method based on a kernel support vector machine (KSVM) and genetic algorithm (GA) to determine whether ECGs collected via mobile phone are acceptable or not. This method includes mainly three modules, i.e., lead-fall detection, feature extraction, and intelligent classification. First, lead-fall detection is executed to make the initial classification. Then the power spectrum, baseline drifts, amplitude difference, and other time-domain features for ECGs are analyzed and quantified to form the feature matrix. Finally, the feature matrix is assessed using KSVM and GA to determine the ECG quality classification results. A Gaussian radial basis function (GRBF) is employed as the kernel function of KSVM and its performance is compared with that of the Mexican hat wavelet function (MHWF). GA is used to determine the optimal parameters of the KSVM classifier and its performance is compared with that of the grid search (GS) method. The performance of the proposed method was tested on a database from PhysioNet/Computing in Cardiology Challenge 2011, which includes 1500 12-lead ECG recordings. True positive (TP), false positive (FP), and classification accuracy were used as the assessment indices. For training database set A (1000 recordings), the optimal results were obtained using the combination of lead-fall, GA, and GRBF methods, and the corresponding results were: TP 92.89%, FP 5.68%, and classification accuracy 94.00%. For test database set B (500 recordings), the optimal results were also obtained using the combination of lead-fall, GA, and GRBF methods, and the classification accuracy was 91.80%.

基于非线性支持向量机和遗传算法的移动ECG质量评估

研究目的：为减少移动设备采集的ECG信号造成的误报警，避免远程心电监控中心的误诊和诊疗资源浪费，提高诊断准确率和效率，首先必须评估ECG信号质量。本文采用频域、时域相结合的ECG特征分析，结合非线性支持向量机（kernelsupportvectormachine, KSVM）和遗传算法，实现对ECG的质量分类。
创新要点：运用频域和时域相结合的ECG特征分析。对具有易于识别特征（如导联脱落）的ECG信号，直接依据该特征得出分类结果；对依据简单特征无法评判的ECG信号，提取复杂时、频域特征组成特征矩阵，运用KSVM进行分类。
方法提亮：根据ECG特征是否易于识别，分步骤采用根据特征直接分类和非线性支持向量机智能分类技术，降低算法复杂度和运算量（图1）。结合频域和时域，在ECG特征空间选择与扩展上进行了有效提升（公式4）。运用遗传算法优化了SVM参数。
重要结论：时、频域结合的特征分析能够较全面地反映ECG特征。KSVM智能分类技术能够有效提高分类精度。
ECG质量评估；非线性支持向量机；遗传算法；功率谱；交叉验证

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