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Currently, clinicians identify quiet sleep cycles in neonates by visually inspecting amplitude-integrated EEG (aEEG) recordings. However, no existing tool provides an objective and automated estimation of the duration of these cycles. This project presents a low-complexity algorithm designed to automatically detect the onset and offset of quiet sleep using impedance and aEEG signals. The proposed method is suitable for integration into standard clinical monitors or hospital-grade systems and will be validated against expert visual annotations. By enabling consistent and objective measurement of quiet sleep, this work supports its use as a potential biological marker of neurological maturation in neonates, advancing the field of neonatal neurophysiological monitoring.