# Detection of Arrhythmias Using Heart Rate Signals from Smartwatches
## 4 Results and Discussion
### Model Validation Results with Patient Smartwatch Samples  
We used the validation data set detailed in the Section 3 to validate our proposal with data obtained from smartwatches. The decision tree model was used, as it achieved the best results in terms of sensitivity and accuracy. The results of our proposed model applied to the sample validation data set are provided in Table 6.  
Our model showed good sensitivity, with a value of 91.67%, almost the same as the results based on the training and testing data sets (91.89%); see Table 5. This result confirms that our model can effectively recognize arrhythmia from the smartwatch data of patients with arrhythmias; that is, in the true-positive case.  
However, the specificity only reached 50%. This result indicates that our model is not good at recognizing the normality of patients; that is, it may be wrong when recognizing an arrhythmia in a patient who does not have it (i.e., false positives).  
Finally, the overall accuracy was 70.83%, which is acceptable but low compared to that based on the training and testing data, due to errors in the recognition of false positives.  
These results were also compared with those of the state-of-the-art methods (see Table 5). Regarding sensitivity, our proposal tested on heart rate data from smartwatches demonstrated cutting-edge results with respect to the other proposals (91.67%). Meanwhile, the accuracy and specificity results were the lowest when compared with those of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Proposal (decision tree) & 70.83 & 91.67 & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model validation results on patient smartwatch samples.  
\begin{table}
\begin{tabular}{l l l c c c} \hline \hline
**Study** & **Year** & **Data** & **Patients** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Sensitivity** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [32] & 2021 & Smartwatch ECG & 250 & - & 25 & 99 \\ [PERSON] et al. [41] & 2022 & Smartwatch ECG & 260 & 92 & 91 & 94 \\ [PERSON] et al. [42] & 2023 & Smartwatch ECG & 110 & - & 84 & 100 \\ [PERSON] et al. [43] & 2022 & Smartwatch ECG & 200 & - & 87 & 86 \\ [PERSON] et al. [14] & 2020 & Smartwatch ECG & 30 & 95 & 92 & 96 \\ [PERSON] et al. [44] & 2022 & Smartwatch ECG & 734 & - & 88 & 98 \\ [PERSON] et al. [45] & 2020 & Smartwatch ECG & 35 & - & 94.4 & 81.9 \\  & & and KardiaBand & & & \\ Our proposal & 2024 & \
\begin{tabular}{c} Holter \\ Heart Rate \\ \end{tabular} & 252 & 93.24 & 91.89 & 94.59 \\ Our proposal & 2024 &
\begin{tabular}{c} Smartwatch \\ Heart Rate \\ \end{tabular} & 25 & 70.83 & 91.67 & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with state-of-the-art methods.  
other proposals. Overall, this method using smartwatch data does not offer reliable results when detecting normality, but does offer reliable results when detecting the presence of an arrhythmia.