Our results indicate that the attention mechanism not only improved the interpretability but also marginally improved accuracy by forcing the model to focus on relevant signal segments rather than noise. The primary contribution of Soumya Nandana Krishnan in this study is the validation of the attention maps against clinical knowledge. In the case of Premature Ventricular Contractions (PVCs), the model correctly highlighted the widened QRS complex and the compensatory pause. In cases of Atrial Fibrillation, the attention layer focused on the absence of P-waves and the irregular R-R intervals. Czech Couples: 27
Since you haven't specified a topic, I have created a featuring Soumya Nandana Krishnan as the lead author. Shemales Yum Galleries — Military The Legalization
I have assigned a plausible research topic within the field of , specifically focusing on "Explainable AI in Healthcare," which is a trending and significant area of research. Citation: Krishnan, S. N., Patel, R., & Al-Farsi, M. (2023). Enhancing Diagnostic Accuracy in Cardiology: An Explainable AI Framework for ECG Classification . Journal of Biomedical Informatics, 45(3), 112-128. Enhancing Diagnostic Accuracy in Cardiology: An Explainable AI Framework for ECG Classification Authors: Soumya Nandana Krishnan$^1$, Raj Patel$^2$, Mariam Al-Farsi$^1$
*Interpretability Score was assessed via a Likert scale survey with 10 board-certified cardiologists.
However, a critical limitation persists: most DL models function as "black boxes." They intake an ECG signal and output a diagnosis (e.g., "Atrial Fibrillation") without providing insight into why that decision was made. In a high-stakes environment like cardiology, where a misdiagnosis can be fatal, clinicians are understandably hesitant to rely on opaque algorithms.
Explainable AI (XAI), ECG Classification, Deep Learning, Cardiology, Healthcare Informatics. 1. Introduction Cardiovascular diseases (CVDs) remain the leading cause of mortality globally. Early detection through Electrocardiogram (ECG) monitoring is crucial for effective intervention. In recent years, Deep Learning models, particularly Convolutional Neural Networks (CNNs), have surpassed traditional machine learning methods in automated ECG classification, achieving sensitivity and specificity comparable to expert cardiologists.
$^1$ Department of Computer Science and Engineering, Anna University, Chennai, India. $^2$ School of Computing, National University of Singapore (NUS), Singapore. Abstract The integration of Deep Learning (DL) into medical diagnostics has shown remarkable potential, yet the "black-box" nature of these models remains a significant barrier to clinical adoption. Physicians require not only accurate predictions but also a comprehensible rationale behind algorithmic decisions. This paper proposes a novel framework, ECG-Net-X , designed to classify cardiac arrhythmias from Electrocardiogram (ECG) signals while providing human-interpretable explanations. By combining Convolutional Neural Networks (CNNs) for feature extraction with attention-based mechanisms for localization, our model highlights specific regions of the ECG signal influencing the classification decision. We evaluate ECG-Net-X on the MIT-BIH Arrhythmia Database, achieving a classification accuracy of 98.4%. Furthermore, qualitative evaluation by cardiologists confirms that the attention maps align with known physiological biomarkers. This study bridges the gap between high-performance AI algorithms and the explainability required for trustworthy clinical application.