# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
###### Abstract
The detection and classification of arrhythmias have advanced significantly due to the ease of access to systems with higher processing capacity and the development of machine learning techniques [21]. However, most works aim to improve the accuracy of models based on known data sets, without assessing their real-world applicability [22; 23; 24; 25; 26; 27; 28]. Fortunately, technological advances have led to the widespread availability of wearable smart devices, such as smartwatches, which are capable of detecting cardiac arrhythmias and various health conditions [29; 30]. Smartwatches can monitor biometric data, including oxygen saturation, sleep patterns, blood pressure, and heart rate [31]. Smartwatches can be useful in detecting ECG abnormalities such as bradyarrhythmias, tachyarrhythmias, or variations suggestive of ischemia, in addition to biometric data detection [32]. As of right now, smartwatches can help medical practitioners to identify and treat a wide range of disorders related to electrocardiograms (ECGs) [29]. However, irregularities and mistakes in the data can lead to insufficient detection in the case of the extracted ECGs. Taking the above information into account, we propose an alternative to traditional arrhythmia detection techniques using Holter monitors. Our proposal is based on the use of smartwatches, which are non-invasive, comfortable, and low-cost devices. Their use will allow anyone--with or without arrhythmia--to obtain information about a possible arrhythmia, subsequently receive a diagnosis from a cardiologist, and undergo appropriate treatment. This is important as early diagnoses could save lives. Our arrhythmia detection model is based on artificial intelligence. Although many good detection proposals have used artificial intelligence, their techniques are typically based on ECG data derived from Holter monitors. In contrast, the proposed model uses heart rate data. To the best of our knowledge, no work has carried out the detection of arrhythmias based on the heart rate. Therefore, we do not use classic data sets, such as MIT BIH arrhythmia [33]. Instead, we propose two data sets derived from patients with and without arrhythmia presenting certain characteristics based on their heart rate, including one data set for training and testing the model and another data set for validation. The data used in the model include subjects' age, sex, and the minimum, maximum, and average heart rates over a 24-hour period of using the device. These data provide a great advantage as they are tabular and do not require the use of a heavy model based on a specialized architecture such as Transformer. Therefore, the model uses lightweight machine learning algorithms that can be deployed on a smartphone or smartwatch. Our model, which was designed to be deployed on wearable devices, offers a practical and accessible solution for early detection of arrhythmia. This tool eliminates the need for electrocardiograms, making it a convenient and non-invasive option for patients. The model was trained using heart rate recordings extracted from Holter devices worn by 252 patients at a clinic in Arequipa, Peru. Finally, it was validated with 25 records extracted from smartwatches worn by patients with and without arrhythmia.