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Bridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy

Wuyang Zhang, Zhen Luo, Chuqiao Gu, Jianming Ma, Yebo Cao, Wangming Yuan, Yinzhi Jin

TL;DR

The paper tackles the compression-precision paradox in automated clinical EEG report generation, showing that extreme data compression required to fit EEG data into LLM context windows can erase clinically critical measurements, such as a $0.5$ Hz distinction between seizure types. It proposes a measurement-first hybrid architecture that separates exact measurement extraction via signal processing from narrative generation, using hierarchical multirate sampling, a cross-modal bridge, graph-aware attention, and constrained decoding to preserve clinical values with provenance. A formal result (Theorem 1) demonstrates that end-to-end neural learning cannot preserve precise measurements under high compression, motivating the measurement-first approach and FDA-compliant traceability. Empirical evaluation on TUH, TUSZ, and CHB-MIT shows substantial gains: about 60% fewer false alarms, ~50% faster detection, and measurement accuracy within clinical tolerances, all with sub-minute latency; the approach also generalizes to other high-frequency biosignals requiring precise quantitative reporting alongside narrative text.

Abstract

Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, requiring extreme compression (400:1+ ratios) that destroys fine-grained temporal precision. A 0.5 Hz error distinguishes absence epilepsy from Lennox-Gastaut syndrome. LLMs lack inherent time-series comprehension and rely on statistical associations from compressed representations. This dual limitation causes systems to hallucinate clinically incorrect measurement values. We separate measurement extraction from text generation. Our hybrid architecture computes exact clinical values via signal processing before compression, employs a cross-modal bridge for EEG-to-language translation, and uses parameter-efficient fine-tuning with constrained decoding around frozen slots. Multirate sampling maintains long-range context while preserving event-level precision. Evaluation on TUH and CHB-MIT datasets achieves 60% fewer false alarms, 50% faster detection, and sub-clinical measurement precision. This is the first system guaranteeing clinical measurement accuracy in automated EEG reports.

Bridging the Compression-Precision Paradox: A Hybrid Architecture for Clinical EEG Report Generation with Guaranteed Measurement Accuracy

TL;DR

The paper tackles the compression-precision paradox in automated clinical EEG report generation, showing that extreme data compression required to fit EEG data into LLM context windows can erase clinically critical measurements, such as a Hz distinction between seizure types. It proposes a measurement-first hybrid architecture that separates exact measurement extraction via signal processing from narrative generation, using hierarchical multirate sampling, a cross-modal bridge, graph-aware attention, and constrained decoding to preserve clinical values with provenance. A formal result (Theorem 1) demonstrates that end-to-end neural learning cannot preserve precise measurements under high compression, motivating the measurement-first approach and FDA-compliant traceability. Empirical evaluation on TUH, TUSZ, and CHB-MIT shows substantial gains: about 60% fewer false alarms, ~50% faster detection, and measurement accuracy within clinical tolerances, all with sub-minute latency; the approach also generalizes to other high-frequency biosignals requiring precise quantitative reporting alongside narrative text.

Abstract

Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, requiring extreme compression (400:1+ ratios) that destroys fine-grained temporal precision. A 0.5 Hz error distinguishes absence epilepsy from Lennox-Gastaut syndrome. LLMs lack inherent time-series comprehension and rely on statistical associations from compressed representations. This dual limitation causes systems to hallucinate clinically incorrect measurement values. We separate measurement extraction from text generation. Our hybrid architecture computes exact clinical values via signal processing before compression, employs a cross-modal bridge for EEG-to-language translation, and uses parameter-efficient fine-tuning with constrained decoding around frozen slots. Multirate sampling maintains long-range context while preserving event-level precision. Evaluation on TUH and CHB-MIT datasets achieves 60% fewer false alarms, 50% faster detection, and sub-clinical measurement precision. This is the first system guaranteeing clinical measurement accuracy in automated EEG reports.
Paper Structure (35 sections, 7 equations, 12 figures, 5 tables)

This paper contains 35 sections, 7 equations, 12 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. EEG and Time-Series Foundation Models
  4. Medical Report Generation
  5. Problem Formulation
  6. Task Definition
  7. The Compression-Precision Paradox
  8. Fundamental Limitations of End-to-End Learning
  9. Methodology
  10. Solution Overview
  11. Pipeline.
  12. Hyperparameters.
  13. Feature Extraction and Neural Architecture
  14. Hierarchical multirate sampling.
  15. Measurement-first guardrails.
  16. ...and 20 more sections

Figures (12)

  • Figure 1: Hybrid architecture: hierarchical sampling balances context and precision; signal processing extracts measurements before compression; cross-modal bridge translates to language space; constrained decoder generates reports around frozen slots.
  • Figure 5: Detection trade-off: FA/24h vs. latency. Lower-left is better.
  • Figure : (a) Ablation impacts
  • Figure : (a) Robustness to artifacts
  • Figure : (a) Value error distributions
  • ...and 7 more figures