## 3 Results  
Figure 3 compares rectangular and polar spectrograms illustrating the effect of image resolution on image quality for normal sinus rhythm. As image resolution decreases, spacing between adjacent R-R intervals becomes blurrier in both rectangular and polar spectrograms. Polar-transformed spectrograms (Figure 3b,d,f) exhibit wider spacing between adjacent R-R intervals compared to rectangular spectrograms (Figure 3a,c,e). In Table 2, SSIM and PSNR values were compared between rectangular and polar-transformed spectrograms. We considered all rectangular and polar spectrogram images for evaluation. As can be seen from Table 2, polar-transformed images exhibited higher SSIM and PSNR values compared to rectangular images across all image resolutions (\(p\)-value \(<\) 0.001). Although lower image resolutions resulted in decreased SSIM and PSNR values for both polar and rectangular spectrograms, polar-transformed spectrograms maintained superior image quality relative to rectangular spectrograms. Figure 4 compares rectangular and polar spectrograms illustrating the impact of image resolution on image quality for atrial fibrillation. At high resolutions (224 \(\times\) 224), both rectangular and polar spectrograms clearly distinguish adjacent R-R intervals (Figure 4a,b, arrows). However, at lower resolutions (96 \(\times\) 96), the polar spectrogram (Figure 4f, arrow) maintains distinguishable spacing between adjacent R-R intervals, while the rectangular spectrogram (Figure 4e, arrow) does not appear distinguishable. Table 3 summarizes changes in weight/bias parameters and feature map dimensions across various input image sizes for each baseline network. Given the baseline network, the number of parameters remained constant regardless of input image size, while feature map dimensions decreased by 3/7 when image dimensions changed from 224 to 96. Training deep neural networks with 96 \(\times\) 96 images was approximately 2-3 times faster compared to training with 224 \(\times\) 224 images. The consistent number of parameters in dense layers across varying image sizes was due to the use of Global Average Pooling (GAP) layers just after the extraction of features at the penultimate layer of the baseline network. Figure 3: Effect of resolution on image quality—normal sinus rhythm. (**a**) Rectangular spectrogram at 224 \(\times\) 224 resolution. (**b**) Polar spectrogram at 224 \(\times\) 224 resolution. (**c**) Rectangular spectrogram at 128 \(\times\) 128 resolution. (**d**) Polar spectrogram at 128 \(\times\) 128 resolution. (**e**) Rectangular spectrogram at 96 \(\times\) 96 resolution. (**f**) Polar spectrogram at 96 \(\times\) 96 resolution. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Metric** & **Rect** & **Polar** & **\(p\)-Value** \\ \hline \multirow{2}{*}{\(128\times 128\)} & SSIM & 0.748 \(\pm\) 0.053 & 0.880 \(\pm\) 0.012 & \textless{}0.001 \\  & PSNR & 17.20 \(\pm\) 1.37 (dB) & 21.05 \(\pm\) 1.01 (dB) & \textless{}0.001 \\ \hline \multirow{2}{*}{\(96\times 96\)} & SSIM & 0.576 \(\pm\) 0.081 & 0.790 \(\pm\) 0.019 & \textless{}0.001 \\  & PSNR & 14.94 \(\pm\) 1.43 (dB) & 18.59 \(\pm\) 1.01 (dB) & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons of spectrogram image quality. Table 4 summarizes the prediction performance of various deep CNN models on test data. Polar spectrogram results showed performance comparable to rectangular spectrogram results. For example, at the 96 \(\times\) 96 image resolution using ResNet50, polar spectrograms achieved a higher macro \(F1\)-score (0.7681) compared to rectangular spectrograms (0.7206). When DenseNet121, which produced the highest macro \(F1\)-scores at \(224\times 224\) resolution, was used at 96 \(\times\) 96 resolution, polar spectrograms consistently showed superior performance across all metrics compared to rectangular spectrograms. Figure 4: Effect of resolution on image quality—atrial fibrillation. (**a**) Rectangular spectrogram at 224 \(\times\) 224 resolution.