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·· 3 小时前AI 评分35
模态与形态：生物信号时间序列分类的形态学框架研究
Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
AI 导读
一项研究提出形态—模态框架，将脑电、肌电、心电、光电容积脉搏波及眼电、瞳孔测量、眼动等生物信号的波形结构归纳为尖峰、脉冲簇、振荡、慢漂移和层级节律五类形态，据此指导预处理、特征提取与建模的流程设计。分析显示局部化架构适合尖峰和脉冲簇信号，频率与时序模型适合振荡信号，混合方法适合层级信号。研究还指出跨被试泛化、保形态数据增强、多模态融合与评估实践方面的挑战。
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