###### Abstract
ECG has been mainly used for stroke detection, which helps to determine the cause of stroke. Since the investigation of ECG signals is noninvasive by placing the electrodes on the skin, this helps in the accurate detection of abnormalities. Therefore, a baseline ECG can be useful for detecting cardiac abnormalities in acute stroke patients [11]. In medicine, machine learning has great hope in predicting the disease and assisting doctors in diagnosis, using data collected by wearable sensors and smartphones. The use-case of the machine-learning model is both a diagnosis or prognosis in which the diagnostic models can be used for new subjects, and the model developed for prognosis can predict a given subject's future clinical state [12]. The machine-learning and deep-learning techniques such as logistic regression, random forest and deep neural networks are used for predicting the presence of stroke disease with various related attributes [13; 14]. The stroke risk classification techniques are developed by using logistic regression, Naive Bayesian, Bayesian network, decision tree, neural network, random forest, bagged decision tree, voting and boosting models [15]. Additionally, the machine-learning algorithms are capable of identifying the features that are highly related to stroke occurrence efficiently from the huge set of features [16]. Several studies focused on patient classification based on the overall behavior of the ECG to diagnose specific diseases [17]. The extraction of features from the ECG signal is a key step for ECG recognition, as it allows to greatly enhance and extract the characteristics of the signal, and those features can be fed into the conventional machine-learning models to perform the classification [18]. The integration of classification techniques in the clinical setup majorly requires the detection of ECG abnormalities in real time to be used in the hospital environment at the bedside or on wearable devices [17]. In general, ECG abnormalities are frequently seen in patients with a stroke. A study revealed that any ECG abnormality is a highly ranked factor, and it may be possible that all ECG abnormalities are more indicative of stroke than just atrial fibrillation [16]. Therefore, the analysis of ECG features and the detection of abnormalities from the ECG signal are significant tasks for the diagnosis of stroke disease. With the consideration of the importance of ECG changes in stroke [19], our study aimed to analyze the ECG features for the classification of elderly post-stroke patients and control subjects with a stroke diagnostic approach. Firstly, we extracted the features from the denoised ECG signals of the study participants. Secondly, we performed the analyses on the features such as heart rate variability (HRV) indices of time and spectral domains, fiducial features and statistical and impulsive metrics variables that have not been commonly used. Thirdly, we employed the filter-based feature selection approach for selecting the input feature subset for the classifiers. Finally, we intended to develop a classification model for diagnosing stroke disease based on machine-learning techniques such as k-nearest neighbor (KNN), support vector machine (SVM), Naive Bayes, random forest and logistic regression. Furthermore, our study aimed to investigate the autonomic dysfunction and the potential risk factors in relation to stroke.