The field of electrocardiogram (ECG) analysis and physiological signal representation is rapidly evolving, with a focus on developing innovative models and frameworks to improve diagnostic accuracy and clinical decision-making. Recent research has emphasized the importance of multimodal understanding, where ECG signals are analyzed in conjunction with other vital sign waveforms, such as photoplethysmogram (PPG) and arterial blood pressure (ABP), to provide a more comprehensive perspective on cardiac function.

One of the key trends in this area is the development of unified models that can handle multiple tasks and modalities, eliminating the need for distinct models for each task. This approach has been shown to improve performance, reduce complexity, and enhance usability in clinical settings.

Another significant area of research is the application of deep learning techniques to physiological signal analysis, including wavelet-based approaches and transformer architectures. These methods have demonstrated superior performance in capturing multi-scale time-frequency features and addressing challenges such as motion artifacts, baseline drift, and low signal-to-noise ratio.

Noteworthy papers in this area include:

• Heartcare Suite, which presents a multimodal framework for fine-grained ECG understanding and achieves state-of-the-art performance across multiple clinically meaningful tasks.
• MD-ViSCo, a unified framework for multi-directional vital sign waveform conversion that can generate any target waveform from any single input waveform with a single model, surpassing state-of-the-art baselines in performance.