# RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
## 1 Introduction  
Artificial intelligence (AI) harnesses computer systems to mimic human intelligence in problem solving and decision making. It encompasses various techniques, including machine learning (ML) and deep learning (DL) [1]. Machine learning uses traditional methods like support vector machines, logistic regression, and decision-tree algorithms that use relatively smaller datasets [2; 3]. Deep learning, on the other hand, is a subset of ML that uses artificial neural networks inspired by and imitating the functionality of the human brain [4; 5; 6]. These networks, like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), process information from vast amounts of data through layers of interconnected nodes, achieving high learning capabilities. AI's impact is transforming numerous industries, including facial recognition [7; 8], object detection [9; 10], and autonomous vehicles [11]. Now, AI developers are building powerful models for healthcare applications as well. Major companies are heavily invested in this field, tackling diverse medical challenges [12; 13]. One exciting area is AI-powered arrhythmia detection [14; 15]. Arrhythmia disrupts the heart's rhythm, causing abnormalities in electrocardiogram (ECG) readings. Early detection and treatment are crucial to preventing potentially fatal consequences, as cardiovascular disease (CVD) remains a leading cause of mortality worldwide. A staggering 931,578 deaths in the United States alone were attributed to CVD in 2021, according to the American Heart Association's 2024 report [16]. These alarming figures highlight the widespread impact of the disease, with approximately 127.9 million American adults living with CVD conditions such as heart disease, stroke, and hypertension. Traditionally, ECG tests rely on 12 electrodes placed on the body, which can be inconvenient for patients. Additionally, diagnosing arrhythmias from these traces requires skilled doctors due to the complexity of identifying subtle abnormalities. This poses a risk of misdiagnosis, potentially leading to serious complications. Fortunately, alternative solutions are emerging. Single-lead ECG sensors offer a more comfortable and accessible approach. The captured signal can be analyzed by trained AI models to diagnose various heart conditions. The integration of AI and single-lead ECG technology shows immense promise for improving arrhythmia detection and potentially saving lives. Several studies have explored various approaches using the MIT-BIH and BIDMC datasets to predict arrhythmias and other heart conditions [17; 18; 19; 20; 21]. Notably, convolutional neural networks (CNNs) have emerged as a preferred classification method [22; 23; 24]. These studies demonstrate the effectiveness of CNNs in classifying irregular heartbeats and various ailments from one-dimensional (time series) and two-dimensional (image) data. A key advantage of CNNs is their ability to automatically learn features from the data, eliminating the need for pre-processing or manual feature extraction. This makes them versatile tools across diverse applications, including emotion detection [25; 26], medical data analysis [27; 28], and education [29]. Researchers have proposed combining CNNs with long short-term memory (LSTM) networks (e.g., [14; 15]). This approach leverages LSTMs' ability to retain long-term dependencies within the data, potentially improving classification accuracy. Additionally, the same studies employed the fast Fourier transform (FFT) to extract relevant features from the ECG signals. Traditional ECG diagnostic devices face several challenges, including limited accessibility due to their bulk and stationary nature, high costs for their purchase and maintenance, lack of portability, complexity requiring trained personnel for their operation and interpretation, and difficulties integrating the data with electronic health records (EHRs). In contrast, mobile ECG devices offer significant advantages.