# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
###### Abstract  
Internet of Things (IoT) devices play a crucial role in the real-time acquisition of photoplethysmography (PPG) signals, facilitating seamless data transmission to cloud-based platforms for analysis. Atrial fibrillation (AF), affecting approximately 1-2% of the global population, requires accurate detection methods due to its prevalence and health impact. This study employs IoT devices to capture PPG signals and implements comprehensive preprocessing steps, including windowing, filtering, and artifact removal, to extract relevant features for classification. We explored a broad range of machine learning (ML) and deep learning (DL) approaches. Our results demonstrate superior performance, achieving an accuracy of 97.7%, surpassing state-of-the-art methods, including those with FDA clearance. Key strengths of our proposal include the use of shortened 15-second traces and validation using publicly available datasets. This research advances the design of cost-effective IoT devices for AF detection by leveraging diverse ML and DL techniques to enhance classification accuracy and robustness. IoT devices; artificial intelligence; atrial fibrillation +
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Recent research has explored converting PPG signals into images for AF detection or classification, demonstrating promising results. For instance, [PERSON] et al. [12], [PERSON] et al. [13], and [PERSON] et al. [14] utilize ML or DL approaches such as Random Forest (RF) or CNN to classify the converted images into AF or normal sinus rhythm (NSR). [PERSON] et al. and [PERSON] et al. specifically convert PPG signals into Poincare images for classification, while [PERSON] et al. convert the PPG waveform into a 2D RGB image. Similarly, ECG-based AF detection methods, such as those proposed by [PERSON] et al. [15] and [PERSON] et al. [16], employ hybrid approaches combining CNN and Long Short-Term Memory (LSTM) networks or multi-feature extraction methods based on CNN for AF classification. However, these ECG-based solutions generally require more complex and expensive sensors, multiple electrodes, and face challenges in IoT integration. In contrast, PPG-based systems benefit from being lower-cost, non-invasive, and easier to integrate into wearable devices and IoT platforms. The integration of AI into remote healthcare enhances AF management by optimizing ML-based AF classification systems within telemedicine platforms. While remote monitoring, timely intervention, and personalized healthcare management have been successfully implemented in the past, AI improves the accuracy and efficiency of these processes. Current consumer technology devices, such as smartwatches (e.g., Apple Watch, Fitbit) and fitness trackers, offer minimally obtrusive solutions for PPG signal acquisition. These devices incorporate advanced optical sensors and utilize sophisticated algorithms to provide real-time cardiovascular monitoring, including heart rate variability and arrhythmia detection like AF. Data collected from these devices can be seamlessly transmitted to cloud-based platforms for AI-driven analysis, offering healthcare professionals timely, actionable insights. The collected data from these IoT devices can be seamlessly transmitted to cloud-based platforms for AI-driven analysis, allowing healthcare professionals to access vital information at anytime, enhancing their ability to diagnose and respond to cardiac conditions promptly [17; 18]. This approach not only supports continuous heart rate monitoring but also promotes proactive cardiovascular health management, ultimately improving patient outcomes through continuous, non-invasive monitoring and timely medical interventions [2; 19; 20]. The increasing availability of wearable technologies, including FDA-approved devices, is expanding the scope of remote AI-driven healthcare, improving the timeliness of medical interventions and promoting preventive care strategies.