Abstract:
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) si...Show MoreMetadata
Abstract:
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.
Published in: IEEE Signal Processing Letters ( Volume: 29)