Key result
Graph-based approaches for anomaly detection and classification in the MIT-BIH database are reviewed to refine computer-assisted diagnosis of cardiac arrhythmias.
Why the study?
Cardiac arrhythmias present diagnostic challenges, and despite the utility of the MIT-BIH dataset for computer-assisted diagnosis, it grapples with class imbalance complicating effective analysis.
This review highlights the potential of graph-based computational approaches to improve the accuracy of computer-assisted diagnosis for cardiac arrhythmias using the MIT-BIH database.
Supports computational arrhythmia detection; leaves open clinical translation pending larger annotated datasets.
Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisions made by cardiologists. This review paper surveys diverse computational intelligence methodologies employed for arrhythmia analysis within the context of the widely utilized MIT-BIH dataset. The paucity of adequately annotated medical datasets significantly impedes advancements in various healthcare domains. Publicly accessible resources such as the MIT-BIH Arrhythmia Database serve as invaluable tools for evaluating and refining computer-assisted diagnosis (CAD) techniques specifically targeted toward arrhythmia detection. However, even this established dataset grapples with the challenge of class imbalance, further complicating its effective analysis. This review explores the current research landscape surrounding the application of graph-based approaches for both anomaly detection and classification within the MIT-BIH database. By analyzing diverse methodologies and their respective accuracies, this investigation aims to empower researchers and practitioners in the field of ECG signal analysis. The ultimate objective is to refine and optimize CAD algorithms, ultimately culminating in improved patient care outcomes.
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Sadiq Alinsaif (2024) conducted a review in Cardiac arrhythmias. Graph-based approaches was evaluated. Graph-based approaches for anomaly detection and classification in the MIT-BIH database are reviewed to refine computer-assisted diagnosis of cardiac arrhythmias.
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