基于模式识别和集成CNN-LSTM的阵发性房颤预测模型

杨萍,王丹,康子健,李童,付利华,余悦任

Prediction model of paroxysmal atrial fibrillation based on pattern recognition and ensemble CNN-LSTM

Ping YANG,Dan WANG,Zi-jian KAGN,Tong LI,Li-hua FU,Yue-ren YU

表 2 不同模式匹配长度对应的稠密指数

Tab.2 Density index values corresponding to different symbol matching lengths

p n t DIp 1 4.90 5 0.980 0 2 18.90 25 0.750 0 3 51.30 125 0.410 0 4 98.69 625 0.160 0 5 171.25 3 125 0.050 0 6 270.31 15 625 0.020 0 7 398.43 78 125 0.010 0 8 546.88 390 625 0.001 0 9 585.94 1 953 125 0.000 3 10 828.13 9 765 625 <0.000 1