Article
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Non‐Invasive Heart Failure Evaluation with Wearable Signals and Machine Learning Algorithms
Version 1
: Received: 19 February 2024 / Approved: 19 February 2024 / Online: 19 February 2024 (12:55:23 CET)
A peer-reviewed article of this Preprint also exists.
Victor, O.A.; Chen, Y.; Ding, X. Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms. Sensors 2024, 24, 2248. Victor, O.A.; Chen, Y.; Ding, X. Non-Invasive Heart Failure Evaluation Using Machine Learning Algorithms. Sensors 2024, 24, 2248.
Abstract
Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.
Keywords
Heart Failure, Photoplethysmogram, Echocardiogram, Machine Learning
Subject
Engineering, Bioengineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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