## 1 Introduction  
Sudden cardiac arrest (SCA) is the sudden unexpected loss of heart function less than 1 h from the onset of symptoms [1, 2]. SCA arises when a triggering factor that is either acquired or genetically-determined affects an anatomical or physiological substrate, manifesting into an abnormal heart rhythm such as ventricular tachycardia, which ultimately degenerates into the fatal ventricular fibrillation (VF) rhythm [3]. SCA can occur at any age in a patient with or without a detectable heart disease [4]. Approximately 15,000 Australians per year experience SCA and only 6-13% of patients survive more than one year after the event [5]. SCA is one of the major causes of cardiovascular mortality and is a major public health issue both nationally and globally, with the annual cost of SCA amounting to approximately $33 billion USD [6]. This economic and health burden of SCA poses to society can be reduced with the improvement of patient outcomes through better detection systems [7]. However, the main challenge associated with SCA detection lies in the fact that SCA can manifest in the complete absence of symptoms. Various methods have been investigated for the detection of SCA, each demonstrating strengths in various patient populations. Cardiac imaging modalities such as cardiac magnetic resonance imaging, echocardiography and cardiac positron emission tomography were found to be useful in patients with a suspected structural heart disease [8]. However, there are many cases in which SCA may arise as a result of a non-cardiac cause. Electrophysiology study had also been investigated as a potential detection method, however, there is limited data on its prognostic value for SCA and it is limited due to its poor negative predictive value and its decrease in sensitivity with polymorphic ventricular tachycardia rhythms [9]. The current most commonly explored detection method for SCA is electrocardiography (ECG). Early studies had already established the clinical importance of ECG detection, as 95% of SCA patients were found to have abnormal ECG readings [10]. These early studies have highlighted the usefulness of ECG monitoring for SCA detection as it can aid in understanding the changes preceding the VF rhythm and have identified potential markers that may trigger SCA. The ECG markers that have been currently explored include: QTc interval, QRS duration, R-R interval, ST segment elevation, T-wave amplitude, and T-peak-to-T-end [11; 12; 13; 14]. Though its efficacy as a detection method has been well established, the field is still developing and future large cohort studies on specific patient populations still need to be conducted. Overall, although a majority of both invasive and non-invasive tests have been employed and evaluated, there is currently no optimal detection method nor criteria specific for SCA [15]. Currently, computer algorithms have been developed and utilized in the clinical setting for the quick detection of critical events such as ventricular fibrillation, in the case of SCA. These pattern recognition methods aim to either assign an individual heartbeat to a specific class or to detect the underlying pathology using information obtained from measurable features of the ECG, such as the R-R interval duration. The assumption is that features of individuals in the same class have similar values and, thus, occupy a specific region in the multidimensional feature space separated from other classes [16]. Early studies on pattern recognition algorithms utilized linear classification methods, such as linear discriminant analysis, which assumed that biological signals were linear in nature. Although these methods provided reasonable results, the underlying non-linear features of these signals were ignored. As a majority of biological signals are non-linear in nature, this led to the need for further classification and, thus, research interest had shifted to non-linear classification methods, such as support vector machines [17].