Potential Benefits of Polar Transformation of Time–Frequency Electrocardiogram (ECG) Signals for Evaluation of Cardiac Arrhythmia

There is a lack of studies on the effectiveness of polar-transformed spectrograms in the visualization and prediction of cardiac arrhythmias from electrocardiogram (ECG) data. In this study, single-lead ECG waveforms were converted into two-dimensional rectangular time–frequency spectrograms and polar time–frequency spectrograms. Three pre-trained convolutional neural network (CNN) models (ResNet50, MobileNet, and DenseNet121) served as baseline networks for model development and testing. Prediction performance and visualization quality were evaluated across various image resolutions. The trade-offs between image resolution and model capacity were quantitatively analyzed. Polar-transformed spectrograms demonstrated superior delineation of R-R intervals at lower image resolutions (e.g., 96 × 96 pixels) compared to conventional spectrograms. For deep-learning-based classification of cardiac arrhythmias, polar-transformed spectrograms achieved comparable accuracy to conventional spectrograms across all evaluated resolutions. The results suggest that polar-transformed spectrograms are particularly advantageous for deep CNN predictions at lower resolutions, making them suitable for edge computing applications where the reduced use of computing resources, such as memory and power consumption, is desirable.