## 4 Discussion
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Ref.** & **Year** & **Authors** & **Databases** & **Methodology** & **Classifiers** & **Validation** & **Ac** & **Se** & **Sp** \\  & & & & & & & & (**c**)** & **(**c**)** & **(**c**)** & **(**c**)** \\ \hline
[20] & 2011 & [PERSON] et al. & UCD-DB &
\begin{tabular}{l} Nonlinear features from ECG: Apfin, fractal dimension, \\ correlation dimension, Lyapunov exponents, Hurst exponents \\ \end{tabular} & ANN & 3-fold cross-validation & 89.1 & 100.0 & 95.0 \\ \hline
[22] & 2014 & [PERSON] et al. & \
\begin{tabular}{l} Apnea-ECG \\ (closed seq) \\ \end{tabular} &
\begin{tabular}{l} Recurrence Quantification Analysis (RQ) from HRV with Fixed \\ Amount of Neighbor thresholding \\ \end{tabular} & SVM, ANN & 3-fold cross-validation & 85.2 & 86.37 & 83.47 \\ \hline
[90] & 2016 & [PERSON] et al. & \begin{tabular}{l} Apnea-ECG \\ (released set) \\ \end{tabular} & \
\begin{tabular}{l} Heterogeneous Recurrence Analysis on HRV series with PCA \\ \end{tabular} & LR &
\begin{tabular}{l} Average of 100 iterations of \\ randomly selected training \\ and testing datasets \\ \end{tabular} & 85.0 & 83.0 & 82.0 \\ \hline
[23] & 2016 & [PERSON] and [PERSON] & \begin{tabular}{l} Apnea-ECG \\ (released set), UCD-DB, \\ Custom recorded \\ \end{tabular} & \
\begin{tabular}{l} Characterization of the HRV state space with RP (RQ) \\ \end{tabular} & SVM &
\begin{tabular}{l} Tested on Apnea-ECG \\ custom dataset \\ \end{tabular} & 83.6 & - & - \\
[87] & 2017 & [PERSON] et al. & \begin{tabular}{l} Apnea-ECG, \\ HuoC(DCN2014 \\ \end{tabular} & \
\begin{tabular}{l} Fibtbank, Cepstrum, DFA, and QOA on HRV \\ \end{tabular} & LDA, QOA, LR &
\begin{tabular}{l} Areno-ECG as Training \\ +, The MCD2014 as \\ Testing set \\ \end{tabular} & 84.76 & 81.45 & 86.82 \\ \hline
[24] & 2018 & [PERSON] et al. & \begin{tabular}{l} Apnea-ECG, \\ HuoC(DCN2014 \\ \end{tabular} & \
\begin{tabular}{l} Recurrence Quantification Analysis (RQ) based on HRV \\ \end{tabular} & LDA &
\begin{tabular}{l} Train with Apnea-ECG, \\ text with HidiC(DCN2014 \\ \end{tabular} & 86.3 & - & - \\
[18] & 2021 & [PERSON] et al. & MIT-BHL UCD-DB & \
\begin{tabular}{l} Reproductive RP from ECG, EEG, and respiration signals. Transfer \\ learning with fine-tuning and Weighted Majority Using (WMV) \\ \end{tabular} & CNN &
\begin{tabular}{l} 10-fold cross-validation on \\ MIT-BHL \\ \end{tabular} & 90.72 & 89.61 & 89.29 \\ \hline
[91] & 2021 & [PERSON] et al. & \
\begin{tabular}{l} Apnea-ECG \\ models. All adapted to input 1-D ECG \\ \end{tabular} & CNN &
\begin{tabular}{l} Used original training \\ (stressed) and testing \\ (withheld) proportion \\ \end{tabular} & 85.58 & 88.26 & - \\ \hline
[91] & 2022 & [PERSON] et al. & Apnea-ECG & Custom CNN, ECG’s distance matrix as input & CNN & 5-fold cross-validation & 93.33 & - & 93.6 \\ \hline \hline \end{tabular} \begin{tabular}{l} \
\begin{tabular}{l} \(\ast\) \\ \end{tabular} & 2004 & [PERSON] et al. &
\begin{tabular}{l} Apnea-ECG, MIT-BHL \\ UCD-DB \\ \end{tabular} & Custom CNN, HRV’s DM as input & CNN & External validation & 74.72 & 73.99 & 75.17 \\ \hline \end{tabular}
\end{table} 
Table 6: Previous works on apnea detection based on recurrence information analysis and ECG.