# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 4 Conclusions and Discussion
The application of machine learning models, including SVM, RF, and MLP, offers robust classification capabilities. Additionally, the exploration of deep learning approaches highlights the potential for leveraging pretrained models to enhance AF classification. While further investigation is required to optimize the deep learning process for PPG data, this approach holds promise for improving classification accuracy. In relation to the state of the art, several studies have evaluated the effectiveness of electrocardiography (ECG) and photoplethysmography (PPG) techniques for atrial fibrillation (AF) detection. ECG-based studies typically involve longer trace durations, often spanning several minutes to hours, which allows for a comprehensive analysis of cardiac rhythm [30]. These studies have reported high accuracy rates, ranging from 85% to 99% in detecting AF [31; 32; 33]. In contrast, PPG-based studies generally utilize shorter trace durations, typically limited to 30 s or a few minutes. Despite the brevity of these recordings, PPG studies have demonstrated promising results, with accuracy rates between 80% and 99% for AF detection, as detailed in [2; 12; 13; 34; 35; 36; 37; 38]. Our results demonstrate a competitive performance of 97.7% when benchmarked against state-of-the-art methods, including those with FDA clearance approval [39]. Additionally, our system exhibits three key strengths. First, we utilize shortened traces of 15 s, as opposed to the more commonly employed longer durations of 30 s or several minutes. Second, our findings are validated using a publicly available dataset. Third, we investigate a comprehensive range of machine learning (ML) and deep learning (DL) approaches. While the study benefits from using a publicly available dataset, it is worth noting that such datasets may not fully capture the diversity of real-world patients, potentially limiting the generalizability of the results. Additionally, while IoT devices offer significant advantages for real-time monitoring, practical applications may encounter challenges with data privacy and device reliability. In summary, this paper aims to advance the design of modern and cost-effective IoT devices for atrial fibrillation (AF) detection. A.P.-R.: Conceptualization, Methodology, Experimentation, Validation, Writing, C.C. : Conceptualization, Methodology, Experimentation, Validation, Supervision, Funding, Writing, P.P.-L.: Methodology, Experimentation, Validation, Supervision, Funding, Writing. All authors contributed equally to this work and have read and agreed to the published version of the manuscript. This work was supported by grants TED2021-131681B-I00 (CIOMET) and PID2022-140126 OB-I00 (CYCAD) from the Spanish Ministry of Science, Innovation and Universities, as well as by the INCIBE under the project VITAL-IoT in the context of the funds from the Recovery, Transformation, and Resilience Plan, financed by the European Union (Next Generation). Not applicable. We utilized the MIMIC PERform AF Dataset [21]. The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.