mlsanityData to 7 Oct 2026

SIM-ECG: A Signal Importance Mask-driven ECGClassification System

arXiv 2110.14835 ↗First posted 28 Oct 2021cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Heart disease is the number one killer, and ECGs can assist in the early diagnosis and prevention of deadly outcomes. Accurate ECG interpretation is critical in detecting heart diseases; however, they are often misinterpreted due to a lack of training or insufficient time spent to detect minute anomalies. Subsequently, researchers turned to machine learning to assist in the analysis. However, existing systems are not as accurate as skilled ECG readers, and black-box approaches to providing diagnosis result in a lack of trust by medical personnel in a given diagnosis. To address these issues, we propose a signal importance mask feedback-based machine learning system that continuously accepts feedback, improves accuracy, and ex-plains the resulting diagnosis. This allows medical personnel to quickly glance at the output and either accept the results, validate the explanation and diagnosis, or quickly correct areas of misinterpretation, giving feedback to the system for improvement. We have tested our system on a publicly available dataset consisting of healthy and disease-indicating samples. We empirically show that our algorithm is better in terms of standard performance measures such as F-score and MacroAUC compared to normal training baseline (without feedback); we also show that our model generates better interpretability maps.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 28 Oct 2021.

Builds on

17 of 36 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  3. 2017Right for the Right Reasons: Training Differentiable Models by Constraining their Explanationscited by 293
  4. 2019Automatic diagnosis of the 12-lead ECG using a deep neural networkcited by 164
  5. 2020Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcited by 158
  6. 2019Interpretations are useful: penalizing explanations to align neural networks with prior knowledgecited by 144
  7. 2014Part-based R-CNNs for Fine-grained Category Detectioncited by 107
  8. 2018ECG Heartbeat Classification: A Deep Transferable Representationcited by 76
  9. 2016Rationale-Augmented Convolutional Neural Networks for Text Classificationcited by 39
  10. 2019Embedding Human Knowledge into Deep Neural Network via Attention Mapcited by 33
  11. 2020HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networkscited by 14
  12. 2020Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogramcited by 11
5 more
  1. 2018Classification of 12-Lead ECG Signals with Bi-directional LSTM Networkcited by 8
  2. 2021Improving the trustworthiness of image classification models by utilizing bounding-box annotationscited by 4
  3. 2017Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networkscited by 3
  4. 2013Computer Aided ECG Analysis - State of the Art and Upcoming Challengescited by 1
  5. 2018Using Multitask Learning to Improve 12-Lead Electrocardiogram Classificationcited by 1

Built on by

Not cited yet.