Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (2): 608-615.DOI: 10.11772/j.issn.1001-9081.2019071172
• Frontier & interdisciplinary applications • Previous Articles Next Articles
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:
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:
CLC Number:
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.
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》唯一官方网站, 2020, 40(2): 608-615.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019071172
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 |
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 |
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) |
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 |
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 |
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% |
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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