Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Abstract

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Charting Tomorrow’s Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

“…The use of innovative technologies such as wearable devices has emerged as a promising area of research, with significant advancements being made in both outpatient and inpatient settings. The field of wearable devices for medical purposes is undergoing rapid advancements, encompassing real-time monitoring of vital signs in intensive care units, and the development of mobile electroencephalogram (EEG) and electrocardiogram (EKG) monitors for outpatient care, among others [17,18,[86][87][88].…”

Validation of a New and Straightforward Algorithm to Evaluate Signal Quality during ECG Monitoring with Wearable Devices Used in a Clinical Setting

“…Over the past decade, the healthcare field has undergone rapid transformation, primarily driven by the development and introduction of new technologies embedded in wearable devices [ 1 , 2 , 3 ]. These devices can collect real-time data, such as the electrocardiogram (ECG), enabling prolonged non-invasive monitoring, potentially helpful in monitoring clinical parameters and the early detection of acute events [ 4 , 5 ]. However, a significant challenge lies in ensuring optimal signal quality [ 6 , 7 ], often compromised by several factors, including movement, sweating, and displacement of sensors, caused by suboptimal electrode adherence and stability [ 8 ].…”

Real-Time Myocardial Infarction Detection Approaches with a Microcontroller-Based Edge-AI Device

“…A large body of literature focuses on the methods for ECG signal analysis to detect and predict MI [8][9][10]. Early methodologies were primarily centered around the analysis of morphological features within the ECG signal.…”