In the era of precision medicine and individualized approaches, remote
monitoring and control of heart function have emerged as critical components of
patient evaluation and management. The integration of consumer-grade software and
hardware devices for health monitoring has gained popularity as technological
advancements become increasingly integrated into daily life. The cardiology
community must adapt to the demands of distant and decentralized care, as highlighted
during the COVID-19 pandemic. Wearable technology, such as vital sign monitors,
holds significant potential for monitoring heart disease and associated risk factors. This
book chapter explores the expanding applications of wearable technology in
cardiology, focusing on examples such as Holter-event recording and
electrocardiogram (ECG) patches. Textile-based sensors and wristbands are
implemented across various patient groups, emphasizing real-time deployment and the
evolving role of wearables in arrhythmia, cardiovascular disorders, and associated risk
factors. The importance of conducting clinical trials and using proper terminology for
clinical validation is also highlighted. To enhance the accuracy and efficiency of ECG
signal analysis, this chapter proposes a novel approach that combines AI-based
unsupervised Long Short-Term Memory (LSTM) with a recursive-based Ensemble
Neural Network (ENN). The LSTM component effectively denoises raw ECG signals
and enables faster convergence. The ENN, with its built-in deep layers, provides an
improved classification of cardiovascular diseases (CVD) present in the input ECG
data. The recursive approach employed by the ENN efficiently utilizes the available
parameters, even in the presence of noisy labels. The proposed method demonstrates
enhanced prediction and classification of CVD, with high precision, recall, and F1
score. The objective is to derive a checkpoint between clinical and research potentials,
identify gaps, and address potential risks associated with CVD detection using ECG
measurements. By leveraging wearable technology and advanced AI techniques,
clinicians and researchers can benefit from improved diagnostic accuracy, remote patient monitoring, and personalized care. The insights gained from this chapter will
contribute to the ongoing advancements in remote heart monitoring and facilitate the
adoption of innovative approaches in cardiovascular disease management.
Keywords: Artificial intelligence, Big data-mining, Cardiovascular disease, Electrocardiogram, Internet of Things (IoT), Long Short-Term Memory, Medical signal processing, Recurrent Neural Network, Remote health monitoring, Wearable technology.
