Table 3 Overview of published results of the existing methods using the same databases
From: Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy
Method | Features | Year | Database | Key techniques | Results | |||
|---|---|---|---|---|---|---|---|---|
Se(%) | Sp(%) | PPV(%) | ACC(%) | |||||
Lee, et al [25] * | RRI | 2013 | AFDB†+NSRDB | Sample entropy | 97.26 | 95.91 | – | 96.14 |
Huang, et al [23] | RRI | 2011 | AFDB | Histogram+SD analysis+... | 96.1 | 98.1 | – | – |
NSRDB | NA | 97.9 | NA | NA | ||||
Lake, et al [22] | RRI | 2011 | AFDB | COSEn | 91 | 94 | – | – |
Lian, et al [21] * | RRI | 2011 | AFDB | Map of RdR | 95.8 | 96.4 | – | – |
MITDB | 98.9 | 78.8 | – | – | ||||
NSRDB | NA | 90.0 | NA | NA | ||||
Parvaresh, et al [20] * | AR | 2011 | AFDB‡ | LDA classifier | 96.14 | 93.20 | 90.09 | – |
Babaeizadeh, et al [16] | RRI/AA | 2011⋆ | AFDB‡ | Markov | 87.27⋆ | 95.47⋆ | 92.75⋆ | – |
(FSA) | 2009 | 92 | – | 97 | – | |||
Couceiro, et al [15] | RRI/AA | 2011⋆ | AFDB‡ | Neural network classifier | 96.58⋆ | 82.66⋆ | 78.76⋆ | – |
(PWA/FSA) | 2008 | 93.8 | 96.09 | – | – | |||
Schmidt,et al [14] | RRI/AA | 2011⋆ | AFDB‡ | Markov+Templete matching+... | 89.20⋆ | 94.58⋆ | 91.62⋆ | – |
(PWA/FSA) | 2008 | |||||||
Tatento, et al [13] * | RRI | 2011⋆ | AFDB | Kolmogorov-Smirnov test | 91.20⋆ | 96.08⋆ | 90.32⋆ | – |
2001 | 94.4 | 97.2 | 96.0 | – | ||||
Slocum, et al [12] | AA | 2011⋆ | AFDB‡ | Power percentage | 62.80⋆ | 77.46⋆ | 64.90⋆ | – |
(PWA/FSA) | 1992 | |||||||
Dash, et al [11] | RRI | 2009 | AFDB† | RMSSD+TPR+SE | 94.4 | 95.1 | – | – |
MITDB | 90.2 | 91.2 | – | – | ||||
Kikillus, et al [10] * | RRI | 2007 | AFDB+NSRDB | Histogram+DIFF.+pNN200 | 94.1 | 93.4 | – | – |