## 4 Discussion  
We demonstrated the comparison of polar-transformed spectrograms with conventional rectangular spectrograms in terms of visual appearance and deep-leaning-based prediction performance. Polar-transformed spectrograms offer significant advantages over conventional rectangular spectrograms in clearly resolving R-R intervals at lower image resolutions. Furthermore, deep-learning-based arrhythmia prediction performance using polar-transformed spectrograms was comparable to rectangular spectrograms.  
A limitation of current approach with polar-transformed spectrograms is the use of the fixed temporal duration (30 s). However, this is a proof-of-concept study, focusing on the advantages of polar-transformed spectrograms. The polar representation itself  
Figure 6: Confusion matrices when applying the DenseNet121 models to test data. Deep learning prediction results on polar-transformed spectrogram images when (**a**) 224 \(\times\) 224, (**c**) 128 \(\times\) 128, and (**e**) 96 \(\times\) 96 images were used as input. Deep learning prediction results on rectangular spectrogram images when (**b**) 224 \(\times\) 224, (**d**) 128 \(\times\) 128, and (**f**) 96 \(\times\) 96 images were used as input. A: atrial fibrillation; N: normal sinus rhythm; O: other rhythm; \(\sim\): noise.  
cannot reveal the temporal duration. As a result, unless the temporal duration of ECG signals is fixed, it is challenging to distinguish between conditions like bradycardia or tachycardia and normal sinus rhythm. It is worth investigating the effectiveness of flexible temporal duration in polar-transformed images for the identification of cardiac arrhythmia. For example, one can embed temporal duration into one of the four corners in the polar-transformed image or use multi-modal inputs by incorporating the temporal duration as an attribute in the neural network.  
As the number of samples (i.e., temporal duration of the ECG signal) increases, it becomes difficult to visualize the R-R intervals even in polar-transformed spectrograms, although polar spectrograms are superior to rectangular spectrograms in resolving the R-R intervals given the same image size. Hence, optimal selection of temporal duration for polar image generation may be necessary by taking into account a trade-off between image resolution and temporal duration in ECG signals, when adopting polar representations in the continuous monitoring of cardiac rhythm abnormality. It may be desirable to generate a video that contains frames of polar-transformed spectrograms.  
On-device computing with deep learning has drawn a significant degree of attention in the research community since it alleviates the burden of cloud computing by locally processing and analyzing the sensors' data [42]. However, on-device computing is resource-constrained, and deep learning on edge devices requires lightweight architectures with lower precision (i.e., fewer bits per weight, post-training quantization from float32 to uint8 type) and fewer network connections (i.e., pruning) [42]. Recent studies investigated the comparison of various model architectures with regard to prediction accuracy and memory usage [43]. Quantitative evaluation of memory usage, inference speed, and power consumption on actual edge devices using polar-transformed spectrograms represent important directions for future research.