# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 5 Conclusions  
This study proposed a model for the detection of arrhythmias based on heart rate data obtained from smartwatches worn by patients with and without arrhythmias. To the best of our knowledge, this is the first proposal suggesting the recognition of arrhythmias based on heart rate data, as all previous state-of-the-art proposals have been constructed based on electrocardiogram data.  
We collected two complete data sets: a set of test and training data for the detection model, containing the heart rate samples of 252 patients obtained using Holter equipment; and a validation data set including heart rate samples obtained from 25 patients wearing smartwatches.  
Ten machine learning and deep learning algorithms were implemented to generate models for comparative testing, among which the decision tree model obtained the best results.  
The training results of our model based on the Holter heart rate data demonstrated a sensitivity of 91.89%, an accuracy of 93.24%, and a specificity of 94.59%; thus, our model can effectively detect arrhythmias. These high sensitivity and specificity results provide peace of mind that patients--both with and without arrhythmia--are accurately recognized, underscoring the potential impact of our model in the healthcare context.  
The proposed model was trained with accurate signals from Holters to test it with less accurate signals from smartwatches. Therefore, the test results of our model with heart rate data from smartwatches demonstrated a sensitivity of 91.67%, an accuracy of 70.83%, and a specificity of 50%. The model was not deployed on smartwatches. These results indicate that our model could be implemented in smartwatches to detect arrhythmias reliably; however, it is unreliable for detecting normality in patients.  
Conceptualization, H.A.H.B. and A.M.D.C.T.; methodology, H.A.H.B. and J.A.S.T.; software, H.A.H.B. and R.C.M.; validation, J.A.S.T., H.A.H.B. and A.M.D.C.T.; formal analysis, R.C.M.; investigation, H.A.H.B. and S.C.C.H.; resources, L.A.C.S.; data curation, J.A.S.T.; writing, H.A.H.B. and J.A.S.T.; writing--review and editing, H.A.H.B., S.C.C.H. and J.A.S.T.; visualization, R.C.M.; supervision, H.A.H.B.; project administration, A.M.D.C.T.; funding acquisition, A.M.D.C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by PROCIENCIA--CONCYTEC, Peru, as part of the 2022 applied research projects competition, Contract number: PE501080050-2022-PROCIENCIA.  
Approval code 042-2023, Research Ethics Committee of the Catholic University of Santa Maria Peru, 11 April 2023.  
This work received the approval of the patients involved through informed consent.  
The data sets and models used in this work can be accessed through the following link: [[https://github.com/hhuillcen/HRArrhythmiaDetection](https://github.com/hhuillcen/HRArrhythmiaDetection)]([https://github.com/hhuillcen/HRArrhythmiaDetection](https://github.com/hhuillcen/HRArrhythmiaDetection)).  
Author [PERSON] was employed by the company Agile Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.