A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
Hgctnet: Handcrafted feature-guided cnn and transformer network for wearable cuffless blood pressure measurement,
2 Pith papers cite this work. Polarity classification is still indexing.
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XAI analysis of time-series decomposition reveals insufficient context in CPS ML training, and enlarging data windows improves model performance.
citing papers explorer
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
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Explainable AI to Improve Machine Learning Reliability for Industrial Cyber-Physical Systems
XAI analysis of time-series decomposition reveals insufficient context in CPS ML training, and enlarging data windows improves model performance.