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

arXiv: https://arxiv.org/abs/2110.14835 · HTML: https://mlsanity.com/paper/2110.14835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations](https://mlsanity.com/paper/1703.03717/) (2017)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [Interpretations are useful: penalizing explanations to align neural networks with prior knowledge](https://mlsanity.com/paper/1909.13584/) (2019)
- [Part-based R-CNNs for Fine-grained Category Detection](https://mlsanity.com/paper/1407.3867/) (2014)
- [ECG Heartbeat Classification: A Deep Transferable Representation](https://mlsanity.com/paper/1805.00794/) (2018)
- [Rationale-Augmented Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1605.04469/) (2016)
- [Embedding Human Knowledge into Deep Neural Network via Attention Map](https://mlsanity.com/paper/1905.03540/) (2019)
- [HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks](https://mlsanity.com/paper/2002.05262/) (2020)
- [Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram](https://mlsanity.com/paper/2010.10328/) (2020)
- [Classification of 12-Lead ECG Signals with Bi-directional LSTM Network](https://mlsanity.com/paper/1811.02090/) (2018)
- [Improving the trustworthiness of image classification models by utilizing bounding-box annotations](https://mlsanity.com/paper/2108.10131/) (2021)
- [Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks](https://mlsanity.com/paper/1710.01115/) (2017)
- [Computer Aided ECG Analysis - State of the Art and Upcoming Challenges](https://mlsanity.com/paper/1306.5096/) (2013)
- [Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification](https://mlsanity.com/paper/1812.00497/) (2018)

## 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.