《计算机应用》唯一官方网站 ›› 2020, Vol. 40 ›› Issue (2): 608-615.DOI: 10.11772/j.issn.1001-9081.2019071172
收稿日期:
2019-07-08
修回日期:
2019-08-17
接受日期:
2019-08-27
发布日期:
2019-09-11
出版日期:
2020-02-10
通讯作者:
师丽
作者简介:
王治忠(1982—),男,山东蓬莱人,副教授,博士,主要研究方向:生物信号检测与处理基金资助:
Zhizhong WANG1, Longlong QIAN1, Chuang HAN1, Li SHI2()
Received:
2019-07-08
Revised:
2019-08-17
Accepted:
2019-08-27
Online:
2019-09-11
Published:
2020-02-10
Contact:
Li SHI
About author:
WANG Zhizhong, born in 1982, Ph. D., associate professor. His research interests include biological signal detection and processing.Supported by:
摘要:
针对心肌梗死临床诊断过程中临床实用性和准确率不高的问题,提出一种基于12导联心电图(ECG)的心肌梗死的辅助诊断方法。首先,对12导联ECG信号进行去噪和数据增强处理;其次,分别对各导联ECG信号提取包含标准差、峰度系数、偏度系数的统计特征,以此反映信号的形态特征;同时,提取包含香农熵、样本熵、模糊熵、近似熵和排列熵的熵特征,以此表征ECG信号时间序列的时间与频谱复杂性、新模式产生的概率、规律性和不可预测性以及检测ECG信号的微小变化;然后,融合ECG信号的统计特征和熵特征;最后,基于随机森林算法在病人内和病人间两种模式下对算法进行分析和验证,并通过交叉验证防止过拟合。实验结果表明,病人内模式下算法准确率和F1值分别为99.98%和99.99%,病人间模式下算法准确率和F1值分别为94.56%和97.05%;与基于单导联ECG的诊断方法相比,采用12导联ECG诊断心肌梗死更符合医生临床诊断逻辑。
中图分类号:
王治忠, 钱龙龙, 韩闯, 师丽. 基于统计特征和熵特征融合的心肌梗死辅助诊断方法[J]. 计算机应用, 2020, 40(2): 608-615.
Zhizhong WANG, Longlong QIAN, Chuang HAN, Li SHI. Auxiliary diagnosis method of myocardial infarction based on fusion of statistical features and entropy features[J]. Journal of Computer Applications, 2020, 40(2): 608-615.
MI | HC | |||||
---|---|---|---|---|---|---|
病人号 | 数据增强前记录数 | 数据增强结果 | 病人号 | 数据增强前记录数 | 数据增强结果 | |
1 | 3 | 374 | 104 | 1 | 114 | |
2 | 1 | 534 | 105 | 1 | 143 | |
3 | 1 | 303 | 116 | 1 | 114 | |
4 | 2 | 833 | 117 | 2 | 250 | |
… | … | … | … | … | … | |
290 | 1 | 84 | 267 | 1 | 106 | |
291 | 1 | 107 | 276 | 1 | 147 | |
292 | 2 | 119 | 277 | 1 | 95 | |
293 | 2 | 117 | 279 | 4 | 364 | |
294 | 1 | 100 | 284 | 3 | 431 | |
总数 | 142 | 367 | 50 818 | 52 | 77 | 9 912 |
表1 数据增强前后心电记录数量
Tab. 1 Number of ECG recordings before and after data enhancement
MI | HC | |||||
---|---|---|---|---|---|---|
病人号 | 数据增强前记录数 | 数据增强结果 | 病人号 | 数据增强前记录数 | 数据增强结果 | |
1 | 3 | 374 | 104 | 1 | 114 | |
2 | 1 | 534 | 105 | 1 | 143 | |
3 | 1 | 303 | 116 | 1 | 114 | |
4 | 2 | 833 | 117 | 2 | 250 | |
… | … | … | … | … | … | |
290 | 1 | 84 | 267 | 1 | 106 | |
291 | 1 | 107 | 276 | 1 | 147 | |
292 | 2 | 119 | 277 | 1 | 95 | |
293 | 2 | 117 | 279 | 4 | 364 | |
294 | 1 | 100 | 284 | 3 | 431 | |
总数 | 142 | 367 | 50 818 | 52 | 77 | 9 912 |
信号类型 | 样本总数 | 训练集样本数 | 测试集样本数 |
---|---|---|---|
MI | 50 020 | 45 018 | 5 002 |
HC | 9 990 | 8 991 | 999 |
表2 病人内每折验证数据集分布
Tab.2 Distribution of validation datasets per fold in intra-patient mode
信号类型 | 样本总数 | 训练集样本数 | 测试集样本数 |
---|---|---|---|
MI | 50 020 | 45 018 | 5 002 |
HC | 9 990 | 8 991 | 999 |
真实情况 | 预测结果 | |
---|---|---|
MI | HC | |
MI | TP(真正例) | FN(假反例) |
HC | FP(假正例) | TN(真反例) |
表3 混淆矩阵
Tab.3 Confusion matrix
真实情况 | 预测结果 | |
---|---|---|
MI | HC | |
MI | TP(真正例) | FN(假反例) |
HC | FP(假正例) | TN(真反例) |
分类器 | 折数 | 真实值 | 病人内预测结果 | 病人间预测结果 | acc(病人内/间)/% | sen(病人内/间)/% | spe(病人内/间)/% | F1(病人内/间)/% | ||
---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 1 | |||||||
BPNN | 1 | 0 | 5 002 | 0 | 5 464 | 174 | 100.00 | 100.00 | 100.00 | 100.00 |
1 | 0 | 999 | 270 | 259 | 92.80 | 96.91 | 48.96 | 96.09 | ||
2 | 0 | 5 002 | 0 | 5 881 | 2 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 99 | 420 | 98.42 | 99.96 | 80.92 | 99.14 | ||
3 | 0 | 5 001 | 1 | 7 497 | 110 | 99.98 | 99.98 | 100.00 | 99.99 | |
1 | 0 | 999 | 216 | 282 | 95.97 | 98.55 | 56.62 | 97.87 | ||
4 | 0 | 5 002 | 0 | 5 924 | 149 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 159 | 378 | 95.34 | 97.54 | 70.39 | 97.46 | ||
5 | 0 | 5 002 | 0 | 3 180 | 225 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 5 | 534 | 94.16 | 93.39 | 99.07 | 96.50 | ||
6 | 0 | 5 001 | 1 | 7 107 | 586 | 99.98 | 99.98 | 100.00 | 99.99 | |
1 | 0 | 999 | 297 | 277 | 89.31 | 92.38 | 48.25 | 94.15 | ||
7 | 0 | 4 992 | 10 | 6 270 | 1154 | 99.83 | 99.80 | 100.00 | 99.89 | |
1 | 0 | 999 | 125 | 454 | 84.01 | 84.45 | 78.41 | 90.74 | ||
8 | 0 | 5 002 | 0 | 2 623 | 62 | 99.98 | 100.00 | 99.90 | 99.99 | |
1 | 1 | 998 | 122 | 387 | 94.23 | 97.69 | 76.03 | 96.61 | ||
9 | 0 | 5 002 | 0 | 1 339 | 157 | 99.98 | 100.00 | 99.90 | 99.99 | |
1 | 1 | 998 | 140 | 372 | 85.20 | 89.50 | 72.65 | 90.01 | ||
10 | 0 | 5 002 | 0 | 1 754 | 151 | 99.96 | 100.00 | 99.80 | 99.98 | |
1 | 2 | 997 | 278 | 210 | 82.07 | 92.07 | 43.03 | 89.10 | ||
RF | 1 | 0 | 5 002 | 0 | 5 606 | 32 | 99.95 | 100.00 | 99.69 | 99.97 |
1 | 3 | 996 | 280 | 249 | 94.94 | 99.43 | 47.06 | 97.29 | ||
2 | 0 | 5 002 | 0 | 5 883 | 0 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 4 | 515 | 99.93 | 100.00 | 99.22 | 99.96 | ||
3 | 0 | 5 002 | 0 | 7 600 | 7 | 99.95 | 100.00 | 99.69 | 99.97 | |
1 | 3 | 996 | 371 | 127 | 95.33 | 99.90 | 25.50 | 97.57 | ||
4 | 0 | 5 002 | 0 | 6 025 | 48 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 203 | 334 | 96.20 | 99.20 | 62.19 | 97.95 | ||
5 | 0 | 5 002 | 0 | 3 362 | 43 | 99.95 | 100.00 | 99.69 | 99.97 | |
1 | 3 | 996 | 253 | 286 | 92.49 | 98.73 | 53.06 | 95.78 | ||
6 | 0 | 5 002 | 0 | 7 522 | 171 | 99.96 | 100.00 | 99.79 | 99.98 | |
1 | 2 | 997 | 299 | 275 | 94.31 | 97.77 | 47.90 | 96.97 | ||
7 | 0 | 5 002 | 0 | 7 175 | 249 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 177 | 402 | 94.67 | 96.64 | 69.43 | 97.11 | ||
8 | 0 | 5 002 | 0 | 2 685 | 0 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 236 | 273 | 92.61 | 100.00 | 53.63 | 95.79 | ||
9 | 0 | 5 002 | 0 | 1 446 | 50 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 274 | 238 | 83.86 | 96.65 | 46.48 | 89.92 | ||
10 | 0 | 5 002 | 0 | 1 884 | 21 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 277 | 211 | 87.47 | 98.89 | 43.23 | 92.67 | ||
KNN | 1 | 0 | 4 971 | 31 | 5 055 | 583 | 99.31 | 99.38 | 98.99 | 99.58 |
1 | 10 | 989 | 249 | 280 | 86.50 | 89.65 | 52.93 | 92.39 | ||
2 | 0 | 4 978 | 24 | 5 182 | 701 | 99.53 | 99.52 | 99.59 | 99.71 | |
1 | 4 | 995 | 122 | 397 | 87.14 | 88.08 | 76.49 | 92.64 | ||
3 | 0 | 4 977 | 25 | 6 921 | 686 | 99.40 | 99.50 | 98.89 | 99.36 | |
1 | 11 | 988 | 397 | 101 | 86.63 | 90.98 | 20.28 | 92.74 | ||
4 | 0 | 4 974 | 28 | 4 999 | 1074 | 99.38 | 99.44 | 99.09 | 99.62 | |
1 | 9 | 990 | 268 | 269 | 79.69 | 82.31 | 50.09 | 88.16 | ||
5 | 0 | 4 981 | 21 | 3 329 | 76 | 99.53 | 99.58 | 99.29 | 99.71 | |
1 | 7 | 992 | 318 | 221 | 90.01 | 97.76 | 41.00 | 94.41 | ||
6 | 0 | 4 967 | 35 | 6 745 | 948 | 99.31 | 99.30 | 99.39 | 99.58 | |
1 | 6 | 993 | 294 | 280 | 84.97 | 87.67 | 48.78 | 91.56 | ||
7 | 0 | 4 985 | 17 | 5 969 | 1455 | 99.56 | 99.66 | 99.09 | 99.73 | |
1 | 9 | 990 | 12 | 567 | 81.66 | 80.40 | 97.92 | 89.05 | ||
8 | 0 | 4 982 | 20 | 2 033 | 652 | 99.55 | 99.60 | 99.29 | 99.72 | |
1 | 7 | 992 | 402 | 107 | 67.00 | 75.71 | 21.02 | 79.41 | ||
9 | 0 | 4 989 | 13 | 1 496 | 0 | 99.63 | 99.74 | 99.09 | 99.78 | |
1 | 9 | 990 | 158 | 354 | 92.13 | 100.00 | 69.14 | 94.98 | ||
10 | 0 | 4 977 | 25 | 1 720 | 185 | 99.45 | 99.50 | 99.19 | 99.66 | |
1 | 8 | 991 | 336 | 152 | 78.22 | 90.28 | 31.14 | 86.84 |
表4 性能分析
Tab.4 Performance analysis
分类器 | 折数 | 真实值 | 病人内预测结果 | 病人间预测结果 | acc(病人内/间)/% | sen(病人内/间)/% | spe(病人内/间)/% | F1(病人内/间)/% | ||
---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 1 | |||||||
BPNN | 1 | 0 | 5 002 | 0 | 5 464 | 174 | 100.00 | 100.00 | 100.00 | 100.00 |
1 | 0 | 999 | 270 | 259 | 92.80 | 96.91 | 48.96 | 96.09 | ||
2 | 0 | 5 002 | 0 | 5 881 | 2 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 99 | 420 | 98.42 | 99.96 | 80.92 | 99.14 | ||
3 | 0 | 5 001 | 1 | 7 497 | 110 | 99.98 | 99.98 | 100.00 | 99.99 | |
1 | 0 | 999 | 216 | 282 | 95.97 | 98.55 | 56.62 | 97.87 | ||
4 | 0 | 5 002 | 0 | 5 924 | 149 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 159 | 378 | 95.34 | 97.54 | 70.39 | 97.46 | ||
5 | 0 | 5 002 | 0 | 3 180 | 225 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 5 | 534 | 94.16 | 93.39 | 99.07 | 96.50 | ||
6 | 0 | 5 001 | 1 | 7 107 | 586 | 99.98 | 99.98 | 100.00 | 99.99 | |
1 | 0 | 999 | 297 | 277 | 89.31 | 92.38 | 48.25 | 94.15 | ||
7 | 0 | 4 992 | 10 | 6 270 | 1154 | 99.83 | 99.80 | 100.00 | 99.89 | |
1 | 0 | 999 | 125 | 454 | 84.01 | 84.45 | 78.41 | 90.74 | ||
8 | 0 | 5 002 | 0 | 2 623 | 62 | 99.98 | 100.00 | 99.90 | 99.99 | |
1 | 1 | 998 | 122 | 387 | 94.23 | 97.69 | 76.03 | 96.61 | ||
9 | 0 | 5 002 | 0 | 1 339 | 157 | 99.98 | 100.00 | 99.90 | 99.99 | |
1 | 1 | 998 | 140 | 372 | 85.20 | 89.50 | 72.65 | 90.01 | ||
10 | 0 | 5 002 | 0 | 1 754 | 151 | 99.96 | 100.00 | 99.80 | 99.98 | |
1 | 2 | 997 | 278 | 210 | 82.07 | 92.07 | 43.03 | 89.10 | ||
RF | 1 | 0 | 5 002 | 0 | 5 606 | 32 | 99.95 | 100.00 | 99.69 | 99.97 |
1 | 3 | 996 | 280 | 249 | 94.94 | 99.43 | 47.06 | 97.29 | ||
2 | 0 | 5 002 | 0 | 5 883 | 0 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 4 | 515 | 99.93 | 100.00 | 99.22 | 99.96 | ||
3 | 0 | 5 002 | 0 | 7 600 | 7 | 99.95 | 100.00 | 99.69 | 99.97 | |
1 | 3 | 996 | 371 | 127 | 95.33 | 99.90 | 25.50 | 97.57 | ||
4 | 0 | 5 002 | 0 | 6 025 | 48 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 203 | 334 | 96.20 | 99.20 | 62.19 | 97.95 | ||
5 | 0 | 5 002 | 0 | 3 362 | 43 | 99.95 | 100.00 | 99.69 | 99.97 | |
1 | 3 | 996 | 253 | 286 | 92.49 | 98.73 | 53.06 | 95.78 | ||
6 | 0 | 5 002 | 0 | 7 522 | 171 | 99.96 | 100.00 | 99.79 | 99.98 | |
1 | 2 | 997 | 299 | 275 | 94.31 | 97.77 | 47.90 | 96.97 | ||
7 | 0 | 5 002 | 0 | 7 175 | 249 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 177 | 402 | 94.67 | 96.64 | 69.43 | 97.11 | ||
8 | 0 | 5 002 | 0 | 2 685 | 0 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 236 | 273 | 92.61 | 100.00 | 53.63 | 95.79 | ||
9 | 0 | 5 002 | 0 | 1 446 | 50 | 99.98 | 100.00 | 99.89 | 99.99 | |
1 | 1 | 998 | 274 | 238 | 83.86 | 96.65 | 46.48 | 89.92 | ||
10 | 0 | 5 002 | 0 | 1 884 | 21 | 100.00 | 100.00 | 100.00 | 100.00 | |
1 | 0 | 999 | 277 | 211 | 87.47 | 98.89 | 43.23 | 92.67 | ||
KNN | 1 | 0 | 4 971 | 31 | 5 055 | 583 | 99.31 | 99.38 | 98.99 | 99.58 |
1 | 10 | 989 | 249 | 280 | 86.50 | 89.65 | 52.93 | 92.39 | ||
2 | 0 | 4 978 | 24 | 5 182 | 701 | 99.53 | 99.52 | 99.59 | 99.71 | |
1 | 4 | 995 | 122 | 397 | 87.14 | 88.08 | 76.49 | 92.64 | ||
3 | 0 | 4 977 | 25 | 6 921 | 686 | 99.40 | 99.50 | 98.89 | 99.36 | |
1 | 11 | 988 | 397 | 101 | 86.63 | 90.98 | 20.28 | 92.74 | ||
4 | 0 | 4 974 | 28 | 4 999 | 1074 | 99.38 | 99.44 | 99.09 | 99.62 | |
1 | 9 | 990 | 268 | 269 | 79.69 | 82.31 | 50.09 | 88.16 | ||
5 | 0 | 4 981 | 21 | 3 329 | 76 | 99.53 | 99.58 | 99.29 | 99.71 | |
1 | 7 | 992 | 318 | 221 | 90.01 | 97.76 | 41.00 | 94.41 | ||
6 | 0 | 4 967 | 35 | 6 745 | 948 | 99.31 | 99.30 | 99.39 | 99.58 | |
1 | 6 | 993 | 294 | 280 | 84.97 | 87.67 | 48.78 | 91.56 | ||
7 | 0 | 4 985 | 17 | 5 969 | 1455 | 99.56 | 99.66 | 99.09 | 99.73 | |
1 | 9 | 990 | 12 | 567 | 81.66 | 80.40 | 97.92 | 89.05 | ||
8 | 0 | 4 982 | 20 | 2 033 | 652 | 99.55 | 99.60 | 99.29 | 99.72 | |
1 | 7 | 992 | 402 | 107 | 67.00 | 75.71 | 21.02 | 79.41 | ||
9 | 0 | 4 989 | 13 | 1 496 | 0 | 99.63 | 99.74 | 99.09 | 99.78 | |
1 | 9 | 990 | 158 | 354 | 92.13 | 100.00 | 69.14 | 94.98 | ||
10 | 0 | 4 977 | 25 | 1 720 | 185 | 99.45 | 99.50 | 99.19 | 99.66 | |
1 | 8 | 991 | 336 | 152 | 78.22 | 90.28 | 31.14 | 86.84 |
分类器 | acc(病人内/间)/% | sen(病人内/间)/% | spe(病人内/间)/% | F1(病人内/间)/% |
---|---|---|---|---|
BPNN | 99.97 | 99.98 | 99.96 | 99.98 |
91.87 | 94.44 | 67.62 | 94.45 | |
RF | 99.98 | 100.00 | 99.85 | 99.99 |
94.56 | 98.75 | 55.07 | 97.05 | |
KNN | 99.47 | 99.52 | 99.20 | 99.68 |
83.82 | 87.23 | 51.63 | 90.69 |
表5 各分类器在十折交叉验证下的平均性能
Tab.5 Average performance of each classifier under ten-fold cross-validation
分类器 | acc(病人内/间)/% | sen(病人内/间)/% | spe(病人内/间)/% | F1(病人内/间)/% |
---|---|---|---|---|
BPNN | 99.97 | 99.98 | 99.96 | 99.98 |
91.87 | 94.44 | 67.62 | 94.45 | |
RF | 99.98 | 100.00 | 99.85 | 99.99 |
94.56 | 98.75 | 55.07 | 97.05 | |
KNN | 99.47 | 99.52 | 99.20 | 99.68 |
83.82 | 87.23 | 51.63 | 90.69 |
来源文献 | 核心方法 | 导联数 | 交叉验证 | 性能(病人内/间) |
---|---|---|---|---|
文献[ | QRS波检测,时域特征,KNN | 12导联 | 否 | sen=99.97%/无; spe=99.90%/无 |
文献[ | ST段检测,多示例学习,SVM | 12导联 | 否 | sen=92.60%/无; spe=82.40%/无 |
文献[ | 用47个特征进行心肌梗死诊断,KNN | 12导联 | 否 | acc=98.80%/无; sen=99.45%/无; spe=96.27%/无 |
文献[ | CNN | II导联 | 否 | acc=93.53%/无; sen=93.71%/无; spe=92.83%/无 |
文献[ | FAWT和Sent特征,LS-SVM | I导联 | 否 | acc=99.31%/无; |
文献[ | SVM | 8导联 | 否 | sen=93.30%/无; spe=89.70%/无 |
文献[ | MFB-CNN | 12导联 | 否 | acc=99.95%/98.79% |
文献[ | 相位特征,阈值分类器 | 3导联 | 否 | acc=95.60%/无; sen=96.50%/无; spe=92.70%/无 |
本文方法 | 统计特征和熵值特征,RF、BPNN、KNN | 12导联 | 是 | acc=99.98%/99.93%; sen=100%/100%; spe=99.85%/99.22%; F1=99.99%/99.96% |
表6 本文方法与不同文献方法的结果对比
Tab.6 Result comparison of the proposed method with different methods in references
来源文献 | 核心方法 | 导联数 | 交叉验证 | 性能(病人内/间) |
---|---|---|---|---|
文献[ | QRS波检测,时域特征,KNN | 12导联 | 否 | sen=99.97%/无; spe=99.90%/无 |
文献[ | ST段检测,多示例学习,SVM | 12导联 | 否 | sen=92.60%/无; spe=82.40%/无 |
文献[ | 用47个特征进行心肌梗死诊断,KNN | 12导联 | 否 | acc=98.80%/无; sen=99.45%/无; spe=96.27%/无 |
文献[ | CNN | II导联 | 否 | acc=93.53%/无; sen=93.71%/无; spe=92.83%/无 |
文献[ | FAWT和Sent特征,LS-SVM | I导联 | 否 | acc=99.31%/无; |
文献[ | SVM | 8导联 | 否 | sen=93.30%/无; spe=89.70%/无 |
文献[ | MFB-CNN | 12导联 | 否 | acc=99.95%/98.79% |
文献[ | 相位特征,阈值分类器 | 3导联 | 否 | acc=95.60%/无; sen=96.50%/无; spe=92.70%/无 |
本文方法 | 统计特征和熵值特征,RF、BPNN、KNN | 12导联 | 是 | acc=99.98%/99.93%; sen=100%/100%; spe=99.85%/99.22%; F1=99.99%/99.96% |
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