ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling

• URL: http://arxiv.org/abs/2412.14373v1
• Date: Wed, 18 Dec 2024 22:13:21 GMT
• Title: ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling
• Authors: William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Ding Zhao,
• Abstract summary: ECG-Byte is a tokenizer pipeline for autoregressive language modeling of ECGs. It compresses and encodes ECG signals into tokens, enabling end-to-end Large Language Models training. It achieves competitive performance in NLG tasks in only half the time and 48% of the data required by two-stage approaches.
• Score: 20.484166589932702
• License:
• Abstract: Large Language Models (LLMs) have shown remarkable adaptability across domains beyond text, specifically electrocardiograms (ECGs). More specifically, there is a growing body of work exploring the task of generating text from a multi-channeled ECG and corresponding textual prompt. Current approaches typically involve pretraining an ECG-specific encoder with a self-supervised learning (SSL) objective and using the features output by the pretrained encoder to finetune a LLM for natural language generation (NLG). However, these methods are limited by 1) inefficiency from two-stage training and 2) interpretability challenges with encoder-generated features. To address these limitations, we introduce ECG-Byte, an adapted byte pair encoding (BPE) tokenizer pipeline for autoregressive language modeling of ECGs. This approach compresses and encodes ECG signals into tokens, enabling end-to-end LLM training by combining ECG and text tokens directly, while being much more interpretable since the ECG tokens can be directly mapped back to the original signal. Using ECG-Byte, we achieve competitive performance in NLG tasks in only half the time and ~48% of the data required by two-stage approaches.

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