Table 7 Comparison of noise detection performance between the proposed and existing methods.

From: Design and evaluation of a knowledge-based ECG noise filtering framework

Methods and Year

Type

Data

No. of datasets

Acc. (%)

Sen. (%)

Sp. (%)

F1 (%)

11

CEEMD (fixed threshold)

CINC-2011, MIT-BIH arrhythmia ,MIT-STC, fantasia, PTB

5

99.35

99.5

98.88

N/R

16

ML

Private dataset

1

95.63

97.41

86.50

N/R

25

ML

CINC-2011, CINC-2014 , TeleECG, Private dataset

4

97.15

89.08 *

88.15 *

N/R

42

DL

CINC-2011

1

93.09

97.67

84.73

84.72

43

ML

Private dataset

1

95.23 *

N/R

N/R

N/R

44

Wavelet (fixed threshold)

CINC-2011

1

85.75

92.00 *

67.00 *

N/R

45

DL Two data input (Raw and feature)

MIT-BIH noise stress test, MIT-BIH arrhythmia

2

100.00

99.00

100.00

N/R

46

DL

Private, CINC-2011

2

94.55

85.68

97.02

94.36

47

DL

TeleECG,MIT-BIH arrhythmia, , BUT QDB, NSRDB

4

97.00

95.50

97.00

N/R

Proposed

ML

ECG-ID, BIDMC, CINC-2011, CINC-2014, MIT/BIH noise stress, TeleECG, MIT-BIH arrhythmia

7

99.82

99.27

99.94

99.88

  1. ML refers to machine learning, DL refers to deep learning, and CEEMD refers to complementary ensemble empirical mode decomposition.
  2. * N/R : Not received.
  3. \(*^{*}\) :Average result