  Cardiovascular disease remains a significant problem in modern society. Among
non-invasive techniques, the electrocardiogram (ECG) is one of the most
reliable methods for detecting abnormalities in cardiac activities. However,
ECG interpretation requires expert knowledge and it is time-consuming.
Developing a novel method to detect the disease early could prevent death and
complication. The paper presents novel various approaches for classifying
cardiac diseases from ECG recordings. The first approach suggests the Poincare
representation of ECG signal and deep-learning-based image classifiers
(ResNet50 and DenseNet121 were learned over Poincare diagrams), which showed
decent performance in predicting AF (atrial fibrillation) but not other types
of arrhythmia. XGBoost, a gradient-boosting model, showed an acceptable
performance in long-term data but had a long inference time due to
highly-consuming calculation within the pre-processing phase. Finally, the 1D
convolutional model, specifically the 1D ResNet, showed the best results in
both studied CinC 2017 and CinC 2020 datasets, reaching the F1 score of 85% and
71%, respectively, and that was superior to the first-ranking solution of each
challenge. The paper also investigated efficiency metrics such as power
consumption and equivalent CO2 emissions, with one-dimensional models like 1D
CNN and 1D ResNet being the most energy efficient. Model interpretation
analysis showed that the DenseNet detected AF using heart rate variability
while the 1DResNet assessed AF pattern in raw ECG signals.
