CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG (2010.00104v2)

Abstract: Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. The proposed network utilizes an attention-based generator to learn local salient features, as well as dual discriminators to preserve the integrity of generated data in both time and frequency domains. Our experiments show that the ECG generated by CardioGAN provides more reliable heart rate measurements compared to the original input PPG, reducing the error from 9.74 beats per minute (measured from the PPG) to 2.89 (measured from the generated ECG).

• The paper introduces CardioGAN, a CycleGAN-based framework with attention mechanisms and dual time/frequency discriminators for synthesizing ECG from PPG signals.
• CardioGAN significantly improves heart rate estimation accuracy, reducing the mean absolute error from 9.74 BPM (original PPG) to 2.89 BPM (generated ECG).
• The framework has the potential to enable more accurate and affordable continuous cardiac monitoring using wearable devices by generating realistic ECG from commonly available PPG data.

Analysis of CardioGAN: Synthesis of ECG from PPG Using Generative Adversarial Networks

The paper "CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG" introduces CardioGAN—a novel framework designed to generate electrocardiogram (ECG) signals from photoplethysmogram (PPG) inputs. This work is grounded in addressing the limitation of continuous ECG monitoring in wearable devices, which predominantly rely on PPG sensors.

Methodology

CardioGAN leverages an adversarial approach employing a CycleGAN-based architecture to facilitate unpaired domain translation between PPG and ECG signals. The generator within this network utilizes attention mechanisms to enhance focus on salient features contained in the ECG data—particularly emphasizing the QRS complexes. The network architecture is augmented with dual discriminators functionally operating in both time and frequency domains. This dual-discriminator strategy ensures the fidelity of the generated ECG signals through comprehensive analysis across different data domains.

Results

Quantitative analysis within the paper reveals significant improvement in heart rate measurement accuracy when comparing the generated ECG to the input PPG data. Specifically, the mean absolute error (MAE) for heart rate estimation is reduced from 9.74 BPM (from the original PPG) to 2.89 BPM (from the generated ECG). This outcome underscores the practical benefits and enhanced precision afforded by employing the CardioGAN framework.

Implications

The implications of CardioGAN extend into both theoretical and practical domains of cardiac health monitoring. Theoretically, the method showcases the potential of cross-domain signal synthesis in the biosignal field, presenting pathways for future explorations in signal translation techniques within bioinformatics. Practically, CardioGAN's ability to synthesize realistic ECG signals from prevalent PPG data could revolutionize the efficacy and affordability of continuous cardiac monitoring using wearable devices, where ECG sensors are seldom deployed or require cumbersome user interactions.

Future Directions

Future research could explore applications of CardioGAN-generated ECG data beyond heart rate monitoring, such as the identification of cardiovascular diseases or arrhythmia detection. Another direction includes expanding the model to synthesize multi-lead ECG data, facilitating richer cardiac analyses traditionally constrained by single-channel recordings. Additionally, the scalability of cross-modality signal synthesis for other physiological domains may offer promising advancements in low-cost health monitoring technologies.

In conclusion, while CardioGAN demonstrates marked advancement in biosignal translation and has significant potential for real-world applications in wearable healthcare technology, further exploration and optimization are warranted to understand its full capabilities in diverse real-world scenarios.