## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Analysis Method** & **Samples** & **Compared Against** & **Performance/Accuracy** \\ \hline [PERSON] (2020) [11] & Binary K-means classifier & N/A & Expectation maximization (EM) & Overall accuracy: 80\% \\  & & & & Category-wise confusion: \\  & & & & 22.2\%, synchronous breathing: \\  & & & & 16.3\%, asynchronous breathing: 24.2\%, \\  & & & & unknown: 25.5\% \\ \hline [PERSON] (2020) [36] & Machine learning & \begin{tabular}{c} 5 repeats of 10 different activities \\ activities \\ \end{tabular} & Actual human activity & Overall accuracy: 95.4\% \\ \hline [PERSON] (2013) [57] & Support vector machine (SVM) classifiers & \begin{tabular}{c} 5 min of 12 specific activities, \\ 19.56 h of total recorded data, \\ 21,411 respiratory cycles \\ \end{tabular} & N/A & \begin{tabular}{c} Recognition of smoke inhalation: \\ highest precision and recall with IAR \\ model (90.11\% and 90.04\%, \\ respectively) \\ \end{tabular} \\ \hline [PERSON] (2015) [52] & Bagging classifier, 33 extracted features & Recording during polysomnography & PSG 1 validated by expert physicians & Accuracy: 77.85 \(\pm\) 6.63 (mean \(\pm\) SD), [PERSON]’s kappa: 0.59 \(\pm\) 0.11 with \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] (2019) & Machine learning, SVM, and CNN LSTM & 120 smoking sessions, 1694 smoke inhalations & Actual smoking events & SVM: precision: 0.53, accuracy: 0.8, recall: 0.83, F1 score: 0.63; CNN-LSTM: precision: 0.68, accuracy: 0.55, recall: 0.74, F1 score: 0.72 \\ \hline [PERSON] (2021) [56] & \begin{tabular}{c} Nox BodySleepTM \\ algorithm implementing ML \\ \end{tabular} & Overnight PSG & PSG & Average sensitivity and specificity of sleep stages: 0.70 and 0.66, respectively \\  & & & & \begin{tabular}{c} AHI index: \(\tau=0.91\) between Nox \\ BodySleepTM and manual scoring \\ \end{tabular} \\ \hline [PERSON] (2022) [60] & Machine learning predictive models & 21 datasets & Actual data & Moderate accuracy of prediction, best R2 = 0.56 with CNN with 6 feature set Mean absolute percentage error: 0.20-0.24 \\ \hline ElMoaqet (2020) & Recurrent neural network, LSTM, and BiLSTM 2 & Overnight PSG & PSG & Accuracy of apnea detection: 84.4\%, true-positive rate: 78.5\%, true-negative rate: 85.9\%, F1 score: 67.4\% with abdominal RIP data \\  & & & & \begin{tabular}{c} Moderate accuracy of prediction, best \\ R2 = 0.56 with CNN with 6 feature set \\ Mean absolute percentage error: 0.20-0.24 \\ \end{tabular} \\ \hline ElMoaqet (2020) [61] & Recurrent neural network, LSTM, and BiLSTM 2 & Overnight PSG & PSG & Accuracy of apnea detection: 84.4\%, true-positive rate: 78.5\%, true-negative rate: 85.9\%, F1 score: 67.4\% with abdominal RIP data \\  & & & & \begin{tabular}{c} Moderate accuracy of prediction, best \\ R2 = 0.56 with CNN with 6 feature set \\ Mean absolute percentage error: 0.75 \\ \end{tabular} \\ \hline Guede- (2019) [62] & \begin{tabular}{c} Novel algorithm, machine learning, binary classifier \\ \end{tabular} & 36 separate tests & External observers & \begin{tabular}{c} Specificity: 96.6\%, sensitivity: 90.3\%, \\ [PERSON]’s kappa: 0.75 \\ \end{tabular} \\ \hline [PERSON] (2021) [42] & Machine learning model & \begin{tabular}{c} 20 training datasets, 31 test \\ datasets (11 healthy + 20 NM 3 patients) \\ \end{tabular} & Expert scorers & Accuracy: 61.3\%, sensitivity: 45.5\%, selective: 70\% with phase-angle feature set \\  & & 20 NM 3 patients) & Expert scorers & feature set Accuracy: 90.3\%, sensitivity: 100\%, specificity: 85\% with ICP 4 feature set \\ \hline [PERSON] (2012) [41] & \
\begin{tabular}{c} Support vector machine \\ (SVM) classifier, \\ AUREA [91] \\ \end{tabular} & 3000 samples of AUREA & Actual success/failure of extubation & Training accuracy for success class: 89.7\%, failure class: 85.4\% Testing accuracy for success class: 73.6\%, failure class: 83.2\% \\ \hline [PERSON] (2018) [63] &
\begin{tabular}{c} Hilbert-Huang transform, \\ K-means classifier \\ \end{tabular} & 49 datasets (2 from each subject, 3 excluded) & Actual diagnosis of COPD stage & Class-wise error: 20\%, 34\%, and 25\% \\  & & & & Error with no-classifying model: 36\% \\ \hline \hline \end{tabular}  
* PSG: polysomnography. 2 LSTM: long short-term memory, BiLSTM: bidirectional long short-term memory. 3 NM: neuromuscular. 4 ICP: inverse cumulative percentage.  
\end{table}