###### Abstract  
Stroke is considered as a major cause of death and neurological disorders commonly associated with elderly people. Electrocardiogram (ECG) signals are used as a powerful tool in diagnosing stroke, and the analysis of ECG signals has become the focus of stroke research. ECG changes and autonomic dysfunction are reportedly seen in patients with stroke. This study aimed to analyze the ECG features and develop a classification model with highly ranked ECG features as input variables based on machine-learning techniques for diagnosing stroke disease. The study included 52 stroke patients (mean age 72.7 years, 63% male) and 80 control subjects (mean age 75.5 years, 39% male) for a total of 132 elderly subjects. Resting ECG signals in the lying down position are measured using the BIOPAC MP150 system. The ECG signals are denoised using the discrete wavelet transform (DWT) method, and the features such as heart rate variability (HRV), indices of time and spectral domains and statistical and impulsive metrics, in addition to fiducial features, are extracted and analyzed. Our results showed that the values of the HRV variables were lower in the stroke group, revealing autonomic dysfunction in stroke patients. A statistically significant difference was observed in low-frequency (LF)/high-frequency (HF), time interval measured after the S wave to the beginning of the T wave (ST) and time interval measured from the beginning of the Q wave to the end of the T wave (QT) (\(p<0.05\)) between the groups. Our study also highlighted some of the risk factors of stroke, such as age, male sex and dyslipidemia (\(p<0.05\)), that are statistically significant. The k-nearest neighbors (KNN) model showed the highest classification results (accuracy 96.6%, precision 94.3%, recall 99.1% and F1-score 96.6%) than the random forest, support vector machine (SVM), Naive Bayes and logistic regression models. Thus, our study reported some of the notable ECG changes in the study participants and also indicated that ECG could aid in diagnosing stroke disease. stroke; risk factors; ECG; heart rate variability; classification; machine learning models +
Footnote †: journal: Medical Physics, University of Science and Technology, Daejeon 34113, Korea; [EMAIL_ADDRESS]  
Footnote †: journal: Medical Physics, University of Science and Technology, Daejeon 34113, Korea; kalaivani@usta. of those, 50% occur in people aged \(\geq\)70 years and 25% in those who are aged \(>\)85 years [6]. As of 2013, the global mortality and disability rate caused by stroke were nearly 6.5 million and 113 million, respectively, whereas the mortality rate is higher in Asia than in Europe and the Americas [7]. Another study reported that the stroke mortality rate of Korea sharply increased, particularly after the age of 70 years [2]. Approximately, every year, 105,000 people experience a new or recurrent stroke, and more than 26,000 people die due to stroke, which indicates that, every five min, a stroke attacks someone, and in every 20 min, a stroke kills someone in Korea. This results in a substantial economic burden to Korea, with the total nationwide cost for stroke care nearly 3.3 billion US dollars in 2005 [1]. The duration of hospital stay and medical expenditure is higher in stroke patients than patients with other chronic diseases [8]. Therefore, an early diagnosis of stroke can enable us to save the lives of people, and the research on stroke patients is also very important for the effective utilization of medical resources. Conventionally, stroke has been diagnosed by various methods, such as computed tomography (CT), magnetic resonance imaging (MRI), a blood test and electrocardiogram (ECG) signals. ECG has always been considered as a popular measurement scheme that is comparatively low-cost in screening and diagnosing various diseases [9]. It has a variety of applications in health monitoring and auxiliary diagnosis and plays a valuable complementary technique for stroke diagnostics [10].