Short ECG recordings are common in research datasets, but many clinical and physiological phenomena, such as autonomic modulation, circadian HRV changes, and transient arrhythmias, require long continuous recordings. Existing generative models either synthesize short beats/segments or fail to preserve physiologically meaningful beat-to- beat variability over long durations.We propose an architecture PhysioDyn with (a) a physiology-constrained GAN that separates beat morphology from inter-beat interval (IBI) dynamics and (b) IDM models inter-beat intervals (RR series) as a continuous-time latent process (GRU-Neural ODEs) and (c) Composer compose beat morphologies spaced by IBIs and (d) a multi-scale discriminator suite. This enables the synthesis of longer-range ECG that preserve clinically and statistically significant HRV properties.
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While ECG signals are harder to forge than fingerprints or facial features, privacy, spoofing, and identification in noisy environments remain concerns. We present an attention-based (self and cross attention) multimodal that leverages the MobileNetV4 and BiGRU as encoders. We simulate noisy or wearable acquisition conditions by segmenting ECG signals into random non-overlapping windows of (1-4) seconds. Multiple noise sources, including Gaussian noise, muscle artifacts, powerline interference, base- line wander, electrode noise, and motion artifacts, were randomly injected at varying signal-to-noise ratio (SNR) levels ranging from -6 dB to 24 dB for each subject and signal segment. Using ECG-ID with a synthetic(GAN) dataset under adversarial training, our method achieves an accuracy of 96.93% with an EER of 3.70%.
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