# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 4 Results and Discussion
### Comparison with State-of-the-Art Methods  
We took the state-of-the-art works mentioned in the Section 2 as references. Our results are based on the model generated using the decision tree algorithm. A sample comparison is shown in Table 5. It is necessary to clarify that this comparison is not rigid, as all of these studies presented results from models trained and tested on electrocardiogram data captured using smartwatches, while our proposal used Holter heart rate data.  
Our work stands out as the sole method that utilized sampled heart rate data, while all other proposals relied on electrocardiogram data. This unique approach, utilizing heart rate monitoring data obtained from smartwatches, not only offers automatic, comfortable, and less invasive monitoring for patients, but also lowers the algorithmic complexity and computational costs associated with arrhythmia detection. The lightweight nature of our model makes it ideal for deployment on mobile devices and smartwatches, further enhancing its appeal.  
Our proposal boasted a high sensitivity rate of 91.89%, a figure that was only surpassed by the methods of [PERSON] et al. (2015) and [PERSON] et al. (2014) (at 94.4% and 92%, respectively). It is important to note that both these proposals utilized ECG samples from smartwatches, while our approach was based on heart rate monitoring. The first proposal detects atrial fibrillation using its data combined with those obtained using other KardiaBand devices, while the second also detects atrial fibrillation and only considers samples from 35 patients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline MLP & 83.78 & 75.68 & 100 \\ SVM & 87.84 & 83.78 & 91.89 \\ Logistic Regression & 74.51 & 67.57 & 72.97 \\ Random Forest & 89.19 & 83.78 & 94.59 \\ Gradient Boosting & 89.19 & 83.78 & 94.59 \\ Decision Tree & 93.24 & 91.89 & 94.59 \\ \(k\)NN & 90.54 & 83.78 & 97.3 \\ XGB & 89.19 & 81.08 & 97.3 \\ LGBM & 89.19 & 81.08 & 97.3 \\ CatBoost & 87.84 & 81.08 & 94.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm training and testing results.  
Our sensitivity result suggests that our model is good at detecting patients with arrhythmia; that is, it can correctly detect true positives. Sensitivity is the best metric for validating disease diagnostic tools in clinical settings.  
Regarding specificity--that is, how well true negatives or normals are detected--our proposal reached 94.59%. Although some proposals achieved lower values, we must highlight that our data set was balanced; that is, there were as many records with arrhythmia as normal ones. This was not the case for the data sets used in the other studies, leading to bias in the specificity calculation.  
Finally, when evaluating the model's effectiveness, we found that its accuracy was 93.24%. This result suggests that our model is effective in recognizing arrhythmia from heart rate samples obtained from smartwatches.  
The results of the implementation of the models with each algorithm, as well as their results, have been reported previously [49].