  Automatic Sleep Staging study is presently done with the help of
Electroencephalogram (EEG) signals. Recently, Deep Learning (DL) based
approaches have enabled significant progress in this area, allowing for
near-human accuracy in automated sleep staging. However, EEG based sleep
staging requires an extensive as well as an expensive clinical setup. Moreover,
the requirement of an expert for setup and the added inconvenience to the
subject under study renders it unfavourable in a point of care context.
Electrocardiogram (ECG), an unobtrusive alternative to EEG, is more suitable,
but its performance, unsurprisingly, remains sub-par compared to EEG-based
sleep staging. Naturally, it would be helpful to transfer knowledge from EEG to
ECG, ultimately enhancing the model's performance on ECG based inputs.
Knowledge Distillation (KD) is a renowned concept in DL that looks to transfer
knowledge from a better but potentially more cumbersome teacher model to a
compact student model. Building on this concept, we propose a cross-modal KD
framework to improve ECG-based sleep staging performance with assistance from
features learned through models trained on EEG. Additionally, we also conducted
multiple experiments on the individual components of the proposed model to get
better insight into the distillation approach. Data of 200 subjects from the
Montreal Archive of Sleep Studies (MASS) was utilized for our study. The
proposed model showed a 14.3\% and 13.4\% increase in weighted-F1-score in
4-class and 3-class sleep staging, respectively. This demonstrates the
viability of KD for performance improvement of single-channel ECG based sleep
staging in 4-class(W-L-D-R) and 3-class(W-N-R) classification.
