# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
###### Abstract  
According to the World Health Organization (WHO), cardiovascular illnesses, including arrhythmia, are the primary cause of mortality globally, responsible for over 31% of all fatalities each year. To reduce mortality, early and precise diagnosis is essential. Although the analysis of electrocardiograms (ECGs) is the primary means of detecting arrhythmias, it depends significantly on the expertise and subjectivity of the health professional reading and interpreting the ECG, and errors may occur in detection. Artificial intelligence provides tools, techniques, and models that can support health professionals in detecting arrhythmias. However, these tools are based only on ECG data, of which the process of obtaining is an invasive, high-cost method requiring specialized equipment and personnel. Smartwatches feature sensors that can record real-time signals indicating the heart's behavior, such as ECG signals and heart rate. Using this approach, we propose a machine learning- and deep learning-based approach for detecting arrhythmias using heart rate data obtained with smartwatches. Heart rate data were collected from 252 patients with and without arrhythmias who attended a clinic in Arequipa, Peru. Heart rates were also collected from 25 patients who wore smartwatches. Ten machine learning algorithms were implemented to generate the most effective arrhythmia recognition model, with the decision tree algorithm being the most suitable. The results were analyzed using accuracy, sensitivity, and specificity metrics. Using Holter data yielded values of 93.2%, 91.89%, and 94.59%, respectively. Using smartwatch data yielded values of 70.83%, 91.67%, and 50%, respectively. These results indicate that our model can effectively recognize arrhythmias from heart rate data. The high sensitivity score suggests that our model adequately recognizes true positives; that is, patients with arrhythmia. Likewise, its specificity suggests an adequate recognition of false positives. ar arrhythmia; detection; machine learning; deep learning; heart rate; smartwatches; electrocardiogram; cardiovascular diseases +
Footnote †: journal: Medical  
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call for the knowledge of qualified medical professionals. This is especially problematic in developing nations where there is a scarcity of medical personnel, making it challenging for patients to receive proper diagnostic procedures. Additionally, low-income people may not be able to afford the costly medical testing needed for diagnosis [3; 4; 5]. Although electrocardiograms (ECGs) play the most important role in detecting and classifying arrhythmias, ECG analysis poses several challenges. Traditional electrocardiographic analysis is often laborious and dependent on the doctor's experience, which can lead to errors in interpretation [6]. Additionally, irregular or transitory arrhythmias might not be recorded by ECG equipment if they do not happen during recording. Moreover, the placement of the required physical contact sites (electrodes) may have an impact on the recording accuracy. Finally, ECG signals are susceptible to noise and artifacts. Consequently, doctors should be provided with tools to support the detection of arrhythmias [7]. Over the past decade, research on arrhythmia detection using photoplethysmograph (PPG) signals has increased [8]. Artificial intelligence comprises two main types of approach for automatic detection and recognition: machine learning and deep learning. Some machine learning methods include the support vector machine (SVM) [9; 10; 11; 12], random forest (RF) [13; 14; 11], and decision tree (DT) [11; 15] algorithms, while deep learning approaches include models based on the artificial neural network (ANN) [16; 17; 18; 11] and Transformer [19; 20] architectures. The inherent capabilities of such methods have been leveraged to process information from ECG time-series for the improved detection and accurate classification of arrhythmias [7].