## 1 Introduction  
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia characterized by irregular and frequently accelerated heartbeats. It increases the risk of cardiovascular mortality, severe cardiovascular events, heart failure, ischemic heart disease, sudden cardiac death, and stroke [1, 2]. Patients often suffer symptoms with AF such as exhaustion, palpitations, shortness of breath, and chest discomfort. In some instances, individuals also experience these with asymptomatic or silent AF, meaning that an individual may not exhibit any visible symptoms related to AF. Thus, it is crucial to mention that silent AF or undiagnosed AF can cause life-threatening thromboembolic consequences due to its intermittent nature [3, 4]. The combination of rhythm irregularities in the AF makes the diagnosis and detection difficult and thus requires efficient and effective methods and tools for its early detection [2, 4, 5]. Various heart health monitoring instruments are available on the market for the detection of AF, and one such tool is AliveCor [2]. However, it is important to mention that this equipment is distinguished by a significant expense, making it unattainable for most individuals. Moreover, the achievement of reliable outcomes with AliveCor relies on the precise and prompt identification of AF symptoms. Other methods for diagnosing AF include the use of single-lead electrocardiogram (ECG) monitors, Holter monitoring, mobile telemetry monitoring, and implanted loop recorders [2]. These heart health monitoring methods may be classified as either expensive or may necessitate the use of specialized methods that must be used in conjunction with substantial signal processing algorithms to provide high-quality and reliable results. The 12-lead electrocardiogram (ECG) is often regarded as the most dependable technique for identifying a fibrillation waveform [6, 7]. When diagnosing atrial fibrillation (AF) using the electrocardiogram (ECG), physicians look for abnormalities in the pattern of ECG waveforms. This includes irregularities in the RR interval and the absence of P waves,which are replaced by irregular fibrillation waves (f waves) that vary in shape and size. There is a huge body of research that studies atrial fibrillation symptoms for early detection. It has been shown in one of the studies that hospitalized patients with atrial fibrillation (AF) were older and had more health issues, such as diabetes and heart disease, compared to outpatients [8]. Additionally, another study showed that patients with type 2 diabetes and hyperuricemia are 9.66 times more likely to have AF, as analyzed using a generalized linear model of the Poisson family [9]. AF is a growing health concern influenced by aging and cardiovascular risk factors, progressing from paroxysmal (episodes lasting <7 days) to persistent (\(\geq\)7 days) or permanent forms, with yearly progression rates varying widely [10]. [PERSON] et al. 's study found Apixaban promising for AF patients up to 140 kg, but careful monitoring is needed for those at extreme weights [11]. Another study revealed that 3.9% of lung cancer patients in outpatient oncology clinics had recently diagnosed AF, with high rates of major bleeding and cardiovascular events significantly impacting mortality in early-stage cancer patients [12]. Diastolic dysfunction, often overlooked in coronary heart disease, can lead to AF through increased atrial afterload, stretching, and pressure, causing left atrial enlargement and triggering the condition [13]. Finally, coronary embolism, a rare cause of acute STEMI, can be triggered by conditions like AF [14]. These studies and many others show that the detection of AF is indeed important as it may cause many diseases and can be fatal too if not treated on time. The distinctive electrocardiogram (ECG) pattern assists clinicians in distinguishing atrial fibrillation (AF) from abnormal sinus rhythm and other types of cardiac arrhythmias [2; 15; 16].