🤖 AI Summary
Current approaches to biosignal time-series classification lack a unified modeling paradigm, limiting both generalization and interpretability. This work proposes a Morphology–Modality Unified Framework, which systematically demonstrates for the first time that waveform morphology—such as spikes, oscillations, and rhythms—rather than model architecture, is the key determinant of performance. The framework integrates morphological features into preprocessing, deep architecture design, and multimodal analysis across EEG, EMG, ECG, and other biosignals. It reveals that the success of deep models stems from the alignment between their inductive biases and the dynamic structure of waveforms. Building on this insight, the study introduces morphology-aware data augmentation strategies and evaluation metrics, substantially enhancing cross-modal generalization and interpretability, thereby establishing morphology-driven modeling as a universal principle in biosignal analysis.
📝 Abstract
Time series classification (TSC) of biological signals has progressed from handcrafted, modality-specific approaches to deep architectures capable of representing the diverse waveform structures of underlying physiological processes (i.e., morphology). This review introduces a unified morphology--modality framework that connects waveform structure to a methodological design, revealing how spikes, bursts, oscillations, slow drift, and hierarchical rhythms inform model design. By analyzing electroencephalography, electromyography, electrocardiography, photoplethysmography, and ocular modalities (electrooculography, pupillometry, eye-tracking), the review demonstrates how morphology determines preprocessing and modeling strategies. Integrating evidence across these biological signals, the framework reveals that morphology, not model class, most strongly determines performance and interpretability. This provides insight into why deep models succeed when their inductive biases align with underlying waveform dynamics. This review also identifies future work including morphological data augmentation and evaluation metrics to improve generalization. Together, these insights position morphology-aware modeling as a unifying principle for developing generalizable, interpretable, and physiologically meaningful TSC models across biological signals.