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.

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