## 6 Results
### Classification Results
Notably, CNN-based approaches could not produce results due to excessive memory demands, indicating potential computational inefficiencies when handling multi-class classification with these methods. In general, the empty/movement task exhibits the highest overall classification accuracy across models, while the empty/respiration/movement task, particularly for LSTM (EMD), demonstrates the lowest performance. CNN-based methods tend to achieve higher accuracy, whereas the TCN (EMD) model appears to be more sensitive to frequency variations, exhibiting the lowest accuracy in certain scenarios. However, the absence of detailed error breakdowns limits interpretability, particularly for misclassification analysis in more complex classification tasks. Consequently, identifying the underlying factors contributing to misclassifications remains challenging, which could impact the generalizability of these models in real-world applications. Figure 12: Confusion matrices of the DL models for Subset 3: (a) LSTM. (b) TCN. Figure 13: Confusion matrices of the DL models for Subset 4: (a) LSTM. (b) TCN. (c) CNN (with STFT data). (d) CNN (with CWT data). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline
**ID** & **Problem** & **Class 1** & **Class 2** & **Class 3** & \begin{tabular}{c} **\# of** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **Frag.** \\ **[MHz]** \\ \end{tabular} & **Phase** & \begin{tabular}{c} **TCN** \\ **(EMD] \%** \\ \end{tabular} & \begin{tabular}{c} **LSTM** \\ **(EMD] \%** \\ \end{tabular} & \
\begin{tabular}{c} **CNN** \\ **(STFT) \%** \\ \end{tabular} &
\begin{tabular}{c} **CNN** \\ **(CVD) \%** \\ \end{tabular} \\ \hline
1 & Empty-Respiration & Empty & Respiration & – & 240 & 210 & Validation & 80.06 & 89.52 & 94.17 & 84.49 \\  & & & & & & Test & 79.17 & 87.50 & 95.83 & 91.67 \\ \hline
2 & Empty-Movement & Empty & Movement & – & 240 & 210 & Validation & 72.98 & 97.12 & 100.00 & 100.00 \\
3 & Empty-Respiration-Movement & Empty & Respiration & Movement & 360 & 210 & Validation & 87.50 & 74.44 & **\(\bullet\)** & **\(\bullet\)** \\  & & & & & & Test & 88.89 & 86.11 & **\(\bullet\)** & **\(\bullet\)** \\ \hline
4 & Empty-Respiration & Empty & Respiration & – & 240 & 332.5 & Validation & 94.98 & 93.35 & 97.91 & 96.25 \\  & & & & & & Test & 91.67 & 100.00 & 95.83 & 95.83 \\ \hline
5 & Empty-Movement & Empty & Movement & – & 240 & 332.5 & Validation & 82.51 & 97.53 & 100.00 & 99.59 \\  & & & & & & Test & 100.00 & 91.67 & 100.00 & 100.00 \\ \hline
6 & Empty-Respiration-Movement & Empty & Respiration & Movement & 360 & 332.5 & Validation & 91.39 & 75.28 & **\(\bullet\)** & **\(\bullet\)** \\  & & & & & & Test & 91.67 & 75.00 & **\(\bullet\)** & **\(\bullet\)** \\ \hline
7 & Empty-Respiration & Empty & Respiration & – & 240 & 405 & Validation & 93.76 & 85.77 & 97.94 & 95.01 \\  & & & & & & Test & 95.83 & 91.67 & 100.00 & 91.67 \\ \hline
8 & Empty-Movement & Empty & Movement & – & 240 & 405 & Validation & 100.00 & 100.00 & 100.00 \\  & & & & & & Test & 100.00 & 100.00 & 100.00 & 100.00 \\ \hline
9 & Empty-Respiration-Movement & Empty & Respiration & Movement & 360 & 405 & Validation & 90.56 & 68.33 & **\(\bullet\)** & **\(\bullet\)** \\  & & & & & & Test & 97.22 & 77.78 & **\(\bullet\)** & **\(\bullet\)** \\ \hline \end{tabular}  
* The performance metrics could not be established due to the high demand for workstation memory.  
\end{table} 
Table 7: Classification results of DL models.