\section{Related Work} \label{sec:rleated_work}
Recent approaches for the task of AF classification in ECG recordings can be clustered into two groups. First classical machine learning approaches that typically extract features, first and classify them in a section learnable step \cite{Antink2017, Smisek2017}. Hoog Antink \etal \cite{Antink2017} proposed an approach that first extracts ECG timing features, robust interval features, and waveform features \cite{Antink2017}. All extracted features are fed into a learned random forest for classification \cite{Antink2017}. Other approaches perform similar feature extractions but utilize different learnable classification methods, such as support vector machines \cite{Smisek2017}. These approaches, however, require a lot of domain knowledge to extract relevant features. Additionally, hand-crafted feature extraction approaches are often complicated and error-prone to implement.\\
\indent Recently deep learning-based approaches have been applied to AF classification in ECG recordings \cite{Zihlmann2017, Mousavi2019, Mashrur2019, Khriji2020, Nonaka2020}. These approaches typically produce a spectrogram of the ECG recording and feed it into a deep neural network for classification \cite{Zihlmann2017, Mashrur2019}. Deep learning AF classification approaches tend to outperform more classical machine learning approaches \cite{Mousavi2019, Khriji2020}. Using deep neural networks, however, require a reasonable amount of data, or extensive data augmentation \cite{Perez2017}, and has higher computational requirements \cite{Lecun2015, Goodfellow2016}. Since a deep neural network is able to learn the extraction of relevant features, heavy preprocessing is typically not required \cite{Zihlmann2017, Lecun2015, Goodfellow2016}.
% Recent approaches for the task of AF classification in ECG recordings can be clustered in two groups. First, classical machine learning approaches \cite{Antink2017, Smisek2017}, and second, deep learning approaches \cite{Zihlmann2017, Mousavi2019, Mashrur2019, Khriji2020, Nonaka2020}. In general, deep learning approaches achieve better classification accuracy, however, sacrifice explainability \cite{Goodfellow2016, Zihlmann2017, Samek2018, Mousavi2019}.\\
% \indent Classical machine learning approaches typically extract features, first and classify them in a section learnable step. Hoog Antink \etal \cite{Antink2017} proposed an approach that first extracts ECG timing features, robust interval features, and waveform features \cite{Antink2017}. All extracted features are fed into a learned random forest for classification \cite{Antink2017}. Other approaches perform similar feature extractions but utilize different learnable classification methods, such as support vector machines \cite{Smisek2017}. These approaches, however, require a lot of domain knowledge to extract relevant features. Additionally, hand-crafted feature extraction approaches are often complicated and error prune to implement.\\
% \indent Recently deep learning-based approaches have been applied to AF classification in ECG recordings \cite{Zihlmann2017, Mousavi2019, Mashrur2019, Khriji2020, Nonaka2020}. These approaches typically produce a spectrogram of the ECG recording and feed it into a deep neural network for classification \cite{Zihlmann2017, Mashrur2019}. Deep learning AF classification approaches tend to outperform more classical machine learning approaches \cite{Mousavi2019, Khriji2020}. Using deep neural networks, however, require a reasonable amount of data, or extensive data augmentation \cite{Perez2017}, and has higher computational requirements \cite{Lecun2015, Goodfellow2016}. Since a deep neural network is able to learn the extraction of relevant features, heavy preprocessing is typically not required \cite{Zihlmann2017, Lecun2015, Goodfellow2016}.