arXiv cs.AI· Jordan Tschida, Matthew Yohe, Edward Kane, Gavin Jager, Emma J. Reid, Tony G. Allen, Mark Story, Leanne Thompson, Joe Hoskins, Brandon Schreiber, Stan Seiferth, Scott Dolvin, David Cornett·· 1 天前AI 评分20
模态 vs. 形态：面向生物信号时间序列分类的框架
Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
AI 导读
研究者提出“形态—模态”框架，将波形结构（spikes、bursts、oscillations、slow drift、hierarchical rhythms）与预处理、特征提取和建模设计相连接，用于脑电、肌电、心电、光电容积脉搏波及眼动等生物信号的时间序列分类。
正文 · 原文
Authors:Jordan Tschida, Matthew Yohe, Edward Kane, Gavin Jager, Emma J. Reid, Tony G. Allen, Mark Story, Leanne Thompson, Joe Hoskins, Brandon Schreiber, Stan Seiferth, Scott Dolvin, David Cornett
Abstract:Techniques for classifying biological signals are largely developed within specific modalities rather than the underlying signal structure (i.e., morphology), leading to disjointed methodologies and limited guidance across diverse signals. This work introduces a novel morphology--modality framework that connects waveform structure to a principled design of preprocessing, feature extraction, and modeling techniques for biological time-series classification. We analyze evidence across biological signals including electroencephalography, electromyography, electrocardiography, photoplethysmography, and ocular modalities including electrooculography, pupillometry, and eye-tracking, and organize according to their primary morphologies: spikes, bursts, oscillations, slow drift, and hierarchical rhythms. Across these categories, we examine how morphology influences the analytical pipeline from preprocessing to modeling and evaluation. Localized architectures favor spikes and bursts, whereas frequency and temporal models capture oscillations, and hybrid approaches integrate local and global dependencies in hierarchical signals. The associations are not absolute as biological signals often contain multiple morphologies where relevance is task dependent. The proposed framework positions morphology as an inductive biases are useful for modeling. We further identify challenges in cross-subject generalization, morphology-preserving data augmentation, multimodal fusion, and evaluation practices. By linking modality-specific constraints with waveform structure, this review provides guidance for selecting and evaluating pipelines across biological signals. We argue that morphology provides a useful cross-modal lens for developing biological time-series classification systems that are more generalizable, interpretable, and physiologically grounded.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.18483 [cs.LG] |
| (or arXiv:2605.18483v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18483 arXiv-issued DOI via DataCite |
Submission history
From: Jordan Tschida [view email]
[v1]
Mon, 18 May 2026 14:36:05 UTC (824 KB)
[v2]
Tue, 10 Nov 2026 19:19:42 UTC (815 KB)
来源：arXiv cs.AI · arxiv.org