## 1 Introduction  
The cardiovascular system consists of the heart and blood vessels. Cardiovascular disease (CVD) is a general term used to describe a wide array of issues that can arise in the cardiovascular system, including strokes, peripheral arterial disease, and coronary heart disease, which can lead to myocardial infarction and heart failure [1, 2]. Accordingto the World Health Organization, CVD is the leading cause of death worldwide, causing an estimated 17.9 million deaths in 2019, or 32% of all global deaths [3]. The average annual cost associated with CVD is estimated to be USD 378 billion in the US and EUR 210 billion in the European Union [4; 5]. Cardiac arrhythmia is a type of CVD characterized by an abnormal heart rate or rhythm that is not physiologically justified [6]. Cardiac arrhythmia can involve a slow heartbeat (bradycardia) or a fast heartbeat (tachycardia). Atrial fibrillation (AF) is the most common type of cardiac arrhythmia, which involves irregular and often abnormally rapid heartbeats caused by the dysfunctional activity of electrical impulses in the atria [3; 7]. It can lead to cardiovascular complications, such as blood clots, stroke, and heart failure, and is responsible for one-third of ischemic strokes [8]. The mortality risk in patients with AF is 4 times higher than in the general population and 1.5 to 1.9 times higher when adjusted for preexisting cardiovascular conditions related to AF [7; 9]. The timely detection of AF is vital in order to initiate adequate treatment and prevent the risk of strokes, which are often the first manifestation of the disease [8]. The most common test used to diagnose cardiac arrhythmia is an electrocardiogram (ECG), which measures the heart's electrical activity using skin-level electrodes with built-in sensors [10]. ECGs detect irregular heartbeats, blocked or narrowed arteries, and other cardiovascular abnormalities. With the emergence of deep learning as an effective method for medical prediction and diagnosis, several models have been proposed for ECG signal classification using neural networks [11; 12; 13; 14]. However, most ECG devices currently available are expensive and are typically used in limited hospital or ambulatory settings. Newer home-monitoring devices have recently been integrated with ECGs, but they remain considerably more expensive than the alternative of photoplethysmography (PPG). Additionally, the materials used for high-quality equipment may cause skin irritation and discomfort during extended use, limiting the devices' long-term use [15]. PPG is a vascular optical measurement technique that detects variations in the blood volume of skin tissue and can be used for heart rate monitoring and cardiovascular abnormality detection [16]. PPG sensors are non-invasive, unobtrusive, and inexpensive, making PPG an appropriate alternative to ECG, although only as a supplementary approach for screening and not for definitive diagnosis. PPG sensors are available in various devices, including smartphones, smartwatches, and fitness trackers, making them more widely available to the general population [16]. Some limitations of PPG include sensitivity to motion artifacts caused by hand movements and vulnerability to environmental noise [17; 18]. The data obtained from ECG and PPG signals can be temporal or morphological [19]. Temporal features offer data based on time, such as the duration between heartbeats, while morphological features describe the appearance of the curve. Despite certain morphological differences, ECG- and PPG-based signals have been shown to have a high degree of correlation [20; 21]. These two signals do indeed reflect different physical essences; however, the correlation is in terms of peak-to-peak variability. Heart rate variability (HRV) measures the variation in time between consecutive cardiac cycles obtained from ECG signals. PPG signals offer similar information through pulse rate variability (PRV), which represents the variation in time between pulse signals [22].