# Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1707.01836 · HTML: https://mlsanity.com/paper/1707.01836/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2017.

## Built on by (most cited first, 30 of 53)

- [Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling](https://mlsanity.com/paper/2104.04569/) (2021)
- [ECGBERT: Understanding Hidden Language of ECGs with Self-Supervised Representation Learning](https://mlsanity.com/paper/2306.06340/) (2023)
- [ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling](https://mlsanity.com/paper/2412.14373/) (2024)
- [ECG-Adv-GAN: Detecting ECG Adversarial Examples with Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2107.07677/) (2021)
- [Simulator and Experience Enhanced Diffusion Model for Comprehensive ECG Generation](https://mlsanity.com/paper/2511.09895/) (2025)
- [XAI Methods for Neural Time Series Classification: A Brief Review](https://mlsanity.com/paper/2108.08009/) (2021)
- [Explainable Deep Neural Network for Multimodal ECG Signals: Intermediate vs Late Fusion](https://mlsanity.com/paper/2508.11666/) (2025)
- [A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification](https://mlsanity.com/paper/2110.09759/) (2021)
- [SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2311.07750/) (2023)
- [MSW-Transformer: Multi-Scale Shifted Windows Transformer Networks for 12-Lead ECG Classification](https://mlsanity.com/paper/2306.12098/) (2023)
- [ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks](https://mlsanity.com/paper/2108.10226/) (2021)
- [Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning](https://mlsanity.com/paper/2210.06297/) (2022)
- [Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework](https://mlsanity.com/paper/2505.18847/) (2025)
- [HALF: Holistic Auto Machine Learning for FPGAs](https://mlsanity.com/paper/2106.14771/) (2021)
- [Application of Adversarial Examples to Physical ECG Signals](https://mlsanity.com/paper/2108.08972/) (2021)
- [Optimizing Neural Network Scale for ECG Classification](https://mlsanity.com/paper/2308.12492/) (2023)
- [Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction](https://mlsanity.com/paper/2404.15347/) (2024)
- [ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models](https://mlsanity.com/paper/2105.03176/) (2021)
- [ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods](https://mlsanity.com/paper/2212.13890/) (2022)
- [Statistical Tests for Replacing Human Decision Makers with Algorithms](https://mlsanity.com/paper/2306.11689/) (2023)
- [HypUC: Hyperfine Uncertainty Calibration with Gradient-boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms](https://mlsanity.com/paper/2311.13821/) (2023)
- [Arrhythmia Classification from 12-Lead ECG Signals Using Convolutional and Transformer-Based Deep Learning Models](https://mlsanity.com/paper/2502.17887/) (2025)
- [A Comprehensive Benchmark for Electrocardiogram Time-Series](https://mlsanity.com/paper/2507.14206/) (2025)
- [CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from ECG](https://mlsanity.com/paper/2509.25804/) (2025)
- [ECG-MoE: Mixture-of-Expert Electrocardiogram Foundation Model](https://mlsanity.com/paper/2603.04589/) (2026)
- [ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset](https://mlsanity.com/paper/2604.15822/) (2026)
- [Neurological Status Classification Using Convolutional Neural Network](https://mlsanity.com/paper/2104.02058/) (2021)
- [Dynamic-Deep: Tune ECG Task Performance and Optimize Compression in IoT Architectures](https://mlsanity.com/paper/2106.00606/) (2021)
- [Automated Atrial Fibrillation Classification Based on Denoising Stacked Autoencoder and Optimized Deep Network](https://mlsanity.com/paper/2202.05177/) (2022)
- [A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance](https://mlsanity.com/paper/2208.00323/) (2022)

## Abstract (arXiv)

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).