To the study team’s knowledge, this is the first open-source four-class sleep staging model developed from a multi-night Apple Watch sleep
study. SLAMSS-IFS, an advanced version of our previous SLAMSS model, for four-class sleep staging using IHR and accelerometry signals from
these wearable devices. Key innovations in the model, including an intra-epoch learning LSTM, frequency information incorporation, and
skip connections, contribute to substantial performance improvements over other SLAMSS variants and other state-of-the-art models. Our
results show that SLAMSS-IFS outperforms competing models in overall accuracy, sensitivity, specificity, precision, weighted F1 score, weighted MCC, and most clinical sleep metrics.
SLAMSS-IFS: The SLAMSS-IFS model builds on the original SLAMSS model with three additional components: an intraepoch learning sequence-to-sequence long short-term memory (LSTM (“I”), a frequency variable (“F”), and a skip connection (“S”). The intra-epoch learning LSTM processes temporal dependencies within individual epochs. The frequency variable captures irregularities in heart rate data. Skip connections enables the decoder to access epoch-wise low-dimensional representations