Reconstruction of EEG and ECG from Single Channel Mixture using Branched Autoencoder based Separable Representations

The growing use of wearable devices requires accurate and compact representations of high dimensional physiological signals. This work presents a UNet inspired autoencoder to represent and reconstruct multiple neuro-physiological signals from single channel data. The architecture comprises single-encoder/dual-branched decoders to obtain self-attention enabled compact embeddings of mixed ExG (EEG /ECG) signals through decaying encoder-decoder skip connections, for improved representation capability. The embeddings are separable into individual ExG components enabling simultaneous reconstruction of high fidelity EEG and ECG sources. The pretrained encoder can be used for a complex downstream task with minimum fine-tuning. Using the proposed method on a large corpus of single-channel mixed ExG generated from overnight Polysomnography (PSG) recordings, we show subject- and class- independent EEG/ECG reconstructions validated by multiple domain-specific metrics, and evaluate the classification performance of the encoded EEG embeddings into five sleep stages as a downstream task.