@misc{10481/92981,
year = {2024},
month = {10},
url = {https://hdl.handle.net/10481/92981},
abstract = {A non-invasive, wireless, smartphone-based electronic measurement system for sleep stage identification is
presented in this work. Ballistocardiograph signals are collected by two piezo-capacitive thin film strips located
on the mattress base. Suitable analog conditioning circuits and digital pre-processing techniques are applied to
obtain the heart and breathing rates (HR, BR), and an activity index (ACT) related to the body movements during
the sleep. An initial calibration stage is proposed where analog signal amplification is fitted to each subject, from
which activity index is derived. Features considered for machine learning classifications were the mentioned data
and the time variabilities of HR and BR represented by the features R(k) and B(k), respectively. Support Vector
Machine (SVM) and K-Nearest-Neighbour (KNN) classifiers are employed in both flat and hierarchical classifi-
cation scenarios for Wake – Non Rapid Eye Movement – Rapid Eye Movement (WAKE/NREM/REM) sleep stage
identification. Twelve healthy subjects were recorded with the developed system using a polysomnograph (PSG)
as reference data. When compared with PSG, the presented system achieved an average accuracy of 69 % using
only three features: R(k), B(k), and ACT, highlighting an 88.2 % recall for NREM stage identification. These
findings suggest that accounting only for time variability features and activity, satisfactory results can be pro-
vided as a complementary alternative for sleep stage identification, with a smartphone-based electronic system
designed as an affordable, versatile, and simple tool for household applications.},
organization = {Junta de Andalucía (Spain) PYC20-RE-040 UGR},
organization = {MCIN/AEI/10.13039/501100011033/  PID2022–138727OB-I00},
organization = {European Regional Development Funds “ERDF A way of making Europe”},
organization = {University of Granada},
publisher = {Elsevier},
keywords = {Piezo-capacitive sensors},
keywords = {PVDF},
keywords = {Sleep monitoring},
keywords = {Smartphone},
keywords = {Machine learning},
title = {Smartphone-based and non-invasive sleep stage identification system with piezo-capacitive sensors},
doi = {10.1016/j.sna.2024.115659},
author = {Pérez Ávila, Antonio Javier and Ruiz Herrera, Noelia and Martínez Olmos, Antonio and Carvajal Rodríguez, Miguel Ángel and Capitán Vallvey, Luis Fermín and López Ruiz, Nuria and Palma López, Alberto José},
}