Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
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).
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Reports INT8 autoencoder TinyML models for on-device arrhythmia detection from ECG, achieving 84% recall and 9 ms latency on ESP32 after filtering ambiguous cases.
ECG-Lens, a complex CNN, achieves 80% accuracy and 90% ROC-AUC on PTB-XL ECG classification, outperforming Decision Tree, Random Forest, Logistic Regression, simple CNN, and LSTM.
citing papers explorer
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Physiology-Aware CNN and Zero-Shot Multimodal LLMs for ECG Image Classification: A Comparative Study
Zero-shot LLMs achieve near-chance ROC-AUC (~0.5) on ECG image classification while CNN models reach 0.92-0.94 internally and 0.85-0.86 externally on PTB-XL.
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ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
Reports INT8 autoencoder TinyML models for on-device arrhythmia detection from ECG, achieving 84% recall and 9 ms latency on ESP32 after filtering ambiguous cases.
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ECG-Lens: Benchmarking ML & DL Models on PTB-XL Dataset
ECG-Lens, a complex CNN, achieves 80% accuracy and 90% ROC-AUC on PTB-XL ECG classification, outperforming Decision Tree, Random Forest, Logistic Regression, simple CNN, and LSTM.