Unraveling Arrhythmias with Graph-Based Analysis: A Survey of the MIT-BIH Database

Abstract

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.

COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep Features

“…Following the five-fold nested CV protocol outlined previously, I reiterate the entire procedure five times. Subsequently, across these five iterations, the average of the following classification metrics [16] is reported:…”

“…Conversely, DL models, specifically convolutional neural networks (CNNs), offer the unique capability of end-to-end training [14,15]. However, their data-intensive nature necessitates substantial labeled data samples for training from scratch [16]. To circumvent this limitation, pre-trained CNN models can be fine-tuned and deployed as feature extractors, effectively capturing the salient information within medical images like CT scans [17].…”