A Unified Attentive Cycle-Generative Adversarial Framework for Deriving Electrocardiogram From Seismocardiogram Signal

In this letter, for the first time, we propose a unified framework based on attentive cycle-generative adversarial network for the synthesis of electrocardiogram (ECG) signals from the seismocardiogram (SCG) signals. The proposed attentive cycle generative adversarial network exploits dual generators and dual discriminators to learn the pattern for the synthesis of ECG from SCG and vice versa. The proposed framework is evaluated on publicly available combined measurement of ECG, breathing and seismocardiogram (CEBS) database. Subjective visual analysis and objective performance metrics demonstrate that the proposed framework can accurately derive the ECG signal from SCG signal. Since, the SCG can be recorded using a wearable and non-adhesive modality, it can provide comfort to the patients by avoiding adhesive ECG electrodes. Further, the derived ECG can help in better cardiac rhythm and arrhythmia analysis.

Automated summary

In this letter, a unified framework based on attentive cycle-generative adversarial network is proposed for the synthesis of electrocardiogram (ECG) signals from seismocardiogram (SCG) signals. The proposed framework is evaluated on a publicly available database and has shown accurate results in deriving ECG signals from SCG signals. This research has significant practical implications, providing a more comfortable method for patients and assisting in better analysis of cardiac rhythm and arrhythmia.