# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 1 Introduction  
According to the American Heart Association [1] and the World Health Organization [2], the mortality ratio due to heart diseases is growing rapidly in the world. It is estimated that around 17.7 million people were affected due to cardiovascular disease (CVD) in 2015. The documentation report from China in 2011 [3] disclosed the information of CVD patients, which showed the statistics that around 230 million people were suffering from CVD and that 3 million death cases were due to the CVD. Electrocardiogram (ECG) signals are a major tool which have been widely used for the analysis of heart disorders in many applications [4; 5; 6]. The collection of these ECG signals are based on contemporary devices which are being used to communicate with the body surface using sensors or nodes [7]. The ECG signals refers to the physical activity of the heart; either the heart has disease or an abnormal rhythm. Irregular rhythm or abnormal rhythm is also known as arrhythmia, which is the leading source of any heart disease. It is not stated that all arrhythmiasare dangerous, however it requires therapy to avoid the severe heart diseases [8]. In recent years, many computer-assisted techniques have been proposed to classify heartbeats for arrhythmia prediction [8; 9; 10]. The two main aspects were considered in previous studies (1) feature extraction techniques were used to extract the morphological changes from the ECG signals and, (2) learning algorithms for the classification of heart beats. Based on the feature extraction aspect, the studies used time domain analysis [8; 11; 12], frequency domain analysis [13; 14; 15], and feature analysis based on wavelet transforms [16; 17; 18; 19]. The most common selection of learning algorithms includes Support Vector Machines [11; 12; 14; 20], K-nearest Neighbors [21; 22], Neural Networks [19; 23; 24; 25; 26], decision trees [15; 27], and deep learning [28; 29; 30]. With the increasing trend of contemporary ECG monitoring devices, the demands of efficient computer-assisted diagnostic methods are also increasing. These types of devices demand fully automated methods for real-time classification which requires less computational cost and can easily be adaptable for hardware. Nowadays, the concept of the Medical Internet of Things is improving the e-health systems. All of these systems require less computational cost and time-saving algorithms for classification. However, despite the extensive efforts that have been made in the previous techniques for heartbeat classification with the real-time scenarios, they often deal with a limited number of leads which are often contaminated and some of the studies are lead specific which do not perform efficiently in terms of cost and time [12]. Furthermore, inter-patient variability is the major problem in ECG signals because heart beats with the same class behave differently in patients. In the literature, most of the studies were majorly focused on simple heartbeat classification based on the single lead ECG signal. Therefore, we observed that the analysis of ECG signals are not sufficient to classify the proper heartbeats; to bridge this gap, the author proposed the patient adaptable approach template matching [31] which allows a learning algorithm to classify the heartbeat lead-specific and this method also enhances the efficiency of the algorithm to classify heartbeats from non-standard leads. In this work, this paper proposed the Reservoir computing based echo state-network for ventricular heartbeat classification. The design of the Reservoir computing is based on a large recurrent neural network (RNN), and the main advantage of this proposed ring network is to handle real-world problems efficiently with high processing speed. This study used ESN (Echo State Networks) with non-random cyclic technique to classify the ventricular heartbeats from the physiological signals.