# BEAT-Net: Injecting Biomimetic Spatio-Temporal Priors for Interpretable ECG Diagnosis

arXiv: https://arxiv.org/abs/2601.07316 · HTML: https://mlsanity.com/paper/2601.07316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 52 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Bag of Tricks for Image Classification with Convolutional Neural Networks](https://mlsanity.com/paper/1812.01187/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients](https://mlsanity.com/paper/2005.13249/) (2020)
- [Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL](https://mlsanity.com/paper/2004.13701/) (2020)
- [ECG-FM: An Open Electrocardiogram Foundation Model](https://mlsanity.com/paper/2408.05178/) (2024)
- [Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram](https://mlsanity.com/paper/2402.09450/) (2024)
- [Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model](https://mlsanity.com/paper/2502.10707/) (2025)
- [Intra-Inter Subject Self-supervised Learning for Multivariate Cardiac Signals](https://mlsanity.com/paper/2109.08908/) (2021)
- [MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals](https://mlsanity.com/paper/1905.11333/) (2019)
- [ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization](https://mlsanity.com/paper/2307.01946/) (2023)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling](https://mlsanity.com/paper/2412.14373/) (2024)
- [ECG Language Processing (ELP): a New Technique to Analyze ECG Signals](https://mlsanity.com/paper/2006.08841/) (2020)
- [IncepSE: Leveraging InceptionTime's performance with Squeeze and Excitation mechanism in ECG analysis](https://mlsanity.com/paper/2312.09445/) (2023)
- [Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors](https://mlsanity.com/paper/2107.13767/) (2021)

## Abstract (arXiv)

Automated electrocardiogram diagnosis using deep learning remains limited by signal-agnostic representations that treat multi-lead recordings as undifferentiated time-series or images, forcing models to rediscover physiological structure implicitly. This leads to data inefficiency, poor generalization, and opaque decision boundaries misaligned with clinical reasoning. We present BEAT-Net, a supervised biomimetic framework that integrates QRS-centered biological tokenization with a hierarchical architecture mirroring the cardiologist's workflow. A QRS tokenizer converts continuous signals into semantically complete heartbeat sequences, which are processed through four specialized stages: morphological feature extraction via a Word Encoder, lead-invariant normalization through a Spatial Operator, temporal context injection by a Temporal Operator, and global reasoning using a Transformer-based Sentence Encoder. Evaluated across three large-scale benchmarks including PTB-XL, CPSC2018, and CSN, BEAT-Net achieves diagnostic accuracy of 0.924 AUC, comparable to dominant CNN baselines at 0.925 AUC, while reducing parameters by 95 percent from 2.06 million to 0.7 million. Critically, BEAT-Net surpasses the 39.5-million-parameter foundation model HeartLang on morphological Form classification, reaching 0.901 AUC compared to HeartLang's 0.832 AUC, while attaining full CNN-level performance using only 35 percent of training data and exhibiting superior cross-dataset generalization. Learned attention patterns spontaneously align with established clinical heuristics, demonstrating that explicit physiological structure provides a more efficient and interpretable alternative to massive pre-training for clinical deployment.