## 1 Introduction  
Mobile and wearable electrocardiograms (ECGs) have emerged as widespread, low-cost devices for continuous heart rhythm monitoring in individuals potentially at risk of cardiac abnormalities [1]. In particular, atrial fibrillation is a serious condition that can lead to thromboembolism in intracranial arteries, resulting in acute ischemic stroke and subsequent disability or death if not promptly treated [2]. Since atrial fibrillation is a sudden and brief event occurring in daily lives, it can be easily missed in short-duration ECG recordings [3; 4]. Conversely, long-duration ECG recordings generate enormous quantities of data, making manual diagnosis time-consuming and laborious. Hence, it is necessary to automate the ECG diagnosis. Deep learning has been widely adopted for the automatic detection of cardiac abnormalities and other conditions, as it is data-driven and does not require any intervention of experts' feature engineering [5; 6; 7; 8]. One-dimensional convolutional neural network (1D CNN) architectures are naturally suited to ECG data presented as 1D time series [9; 10]. In addition, two-dimensional (2D) CNN architectures have been utilized with a 2D transformed version of ECG time series signal as input [11]. The use of 2D CNN models is advantageous because many popular pretrained CNN models are available as a result of the ImageNet large-scale visual recognition challenges in the field of computer vision [12; 13]. Transfer learning from these pretrained deep CNN models based on large-scale databases like ImageNet [14] can be directly applicable to 2D transformed ECG data [15; 16]. Previously, 2D time-frequency spectrograms generated by short-time Fourier transform [17; 18] or scalograms created via discrete wavelet transform [19; 20; 21; 22; 23; 24] have been suggested as inputs for deep CNN-based classification of cardiac abnormalities. Recently, novel representations such as polar-transformed 2D spectrograms have been proposed [25; 26]. The iris spectrogram representation has been demonstrated in deep-learning-based predictions of beat-wise arrhythmia types [27; 28]. Additionally, reverse polar-transformed spectrograms have been proposed for rhythm-wise arrhythmia classification using deep CNN models [25]. Given the growing interest in on-device health monitoring with artificial intelligence, recent research studies have focused on developing efficient, small-scale deep CNN architectures for fast and resource-efficient ECG predictions. For example, [PERSON] and [PERSON] proposed a hybrid lightweight 1D CNN-LSTM architecture for ECG beat-wise classification [29]. [PERSON] and [PERSON] proposed a lightweight model for classification of abnormal heart rhythms using a single-lead ECG on low-powered edge devices [30]. Similarly, [PERSON] and [PERSON] demonstrated the efficacy of lightweight deep CNN architectures optimized for mobile devices [22]. Our research is motivated by the need to explore the potential advantages of polar-transformed ECG representations in edge computing scenarios, where the economic use of computational resources is critical for the visualization and deep-learning-based prediction of cardiac arrhythmia. Hence, the primary innovation of our study is to simulate the impact of reduced image resolutions in polar spectrograms, which could lead to decreased memory usage and faster inference, on the spectrogram visualization quality and deep learning prediction accuracy. The manuscript is organized as follows. Section 2 describes the differences between conventional rectangular spectrograms and polar spectrograms, and explains deep learning model development and validation processes. Section 3 presents the results of the visualization and deep-learning-based arrhythmia classification comparing rectangular and polar spectrograms. Section 4 discusses our findings, and Section 5 concludes and outlines future research directions. Our main contributions are summarized as follows. 1.