Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System
Table 2
Accuracy of different methods in each functional module.
Index
Fast-CNN
QRS based by P&T
Se
PPV
Acc
F1
Se
PPV
Acc
F1
1
0.9953
0.9908
0.9863
0.9931
0.9958
0.9922
0.9881
0.994
2
0.9716
0.9941
0.966
0.9845
0.9857
0.9834
0.9695
0.9827
3
0.9752
0.9857
0.9616
0.9804
0.9079
0.9128
0.8354
0.9103
4
0.9953
0.9995
0.9948
0.9974
0.9995
0.9991
0.9986
0.9993
5
0.986
0.9754
0.9621
0.9807
0.9808
0.9586
0.941
0.9696
6
0.9645
0.9828
0.9484
0.9735
0.9774
0.9738
0.9524
0.9756
7
0.9919
0.986
0.9781
0.9889
0.9953
0.979
0.9745
0.9871
8
0.9881
0.9851
0.9736
0.9866
0.9902
0.9868
0.9772
0.9885
9
0.9827
0.9596
0.9437
0.971
0.9637
0.9529
0.9199
0.9583
10
0.9592
0.9929
0.9526
0.9895
0.9865
0.9924
0.9791
0.9757
11
0.9939
0.9747
0.9688
0.9842
0.9898
0.9099
0.9015
0.9482
12
0.9978
0.9974
0.9952
0.9976
0.9958
0.9908
0.9866
0.9933
13
0.991
0.9959
0.9869
0.9934
0.9943
0.9762
0.9708
0.9852
14
0.983
0.9862
0.9697
0.9846
0.9898
0.9728
0.9631
0.9812
15
0.9796
0.9572
0.9384
0.9682
0.985
0.9806
0.9662
0.9828
16
0.9402
0.9461
0.8924
0.974
0.9749
0.9731
0.9493
0.9432
17
0.9844
0.9818
0.9668
0.9831
0.9757
0.9636
0.941
0.9696
18
0.9489
0.9644
0.9168
0.9566
0.9758
0.9634
0.9409
0.9696
19
0.9815
0.9923
0.974
0.9869
0.9834
0.9814
0.9654
0.9824
20
0.9811
0.9907
0.9721
0.9858
0.9814
0.9816
0.9637
0.9815
AVR
0.9796
0.9819
0.9624
0.9807
0.9814
0.971
0.9542
0.9762
Convolutional Neural Network, CNN. Sensitivity, Se. Positive predictive value, PPV. Accuracy, Acc. F1- measure, change the value of F function by adjusting alpha, F1 when alpha = 1.
