A Hierarchical Framework for Apnea Detection and Respiration Pace Assessment Using Seismocardiogram Signals

Berke Kizir, Beren Semiz

2024

Abstract

Sleep constitutes one-third of human life and plays a critical role in physical repair, mental functioning, and memory consolidation. Although polysomnography (PSG) has been used to assess sleep performance; this test requires participants to visit a sleep clinic and have multiple sensors attached to their bodies. Hence, there is a need for alternative methods which can provide sleep monitoring outside clinical settings, but with clinical standards. In this work, a novel hierarchical framework was built to leverage the seismocardiogram (SCG) signals in apnea detection and respiration pace assessment using a simulated data collection protocol. In the first step of the framework, a binary Light Gradient-Boosting Machine (LGBM) model was trained to detect the breath-holding (apnea) episodes. If the prediction was not a breath-holding state, the data was fed into a multi-class LGBM model to distinguish between normal, slow and fast breathing episodes. Overall, the binary LGBM resulted in an accuracy, recall, precision and f1-score of 0.99, 0.95, 0.87 and 0.91, respectively; whereas for the multi-class case all metrics were 0.96. Additionally, the optimum window length to achieve real-time detection was determined as 5 seconds. The results show that the SCG signals hold substantial information regarding the changes in breathing patterns, thus could potentially be leveraged in the design of wearable systems as an alternative to the PSG test.

Paper Citation

in Harvard Style

Kizir B. and Semiz B. (2024). A Hierarchical Framework for Apnea Detection and Respiration Pace Assessment Using Seismocardiogram Signals. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-688-0, SciTePress, pages 793-798. DOI: 10.5220/0012446400003657

in Bibtex Style

@conference{biosignals24,
author={Berke Kizir and Beren Semiz},
title={A Hierarchical Framework for Apnea Detection and Respiration Pace Assessment Using Seismocardiogram Signals},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2024},
pages={793-798},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012446400003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - A Hierarchical Framework for Apnea Detection and Respiration Pace Assessment Using Seismocardiogram Signals
SN - 978-989-758-688-0
AU - Kizir B.
AU - Semiz B.
PY - 2024
SP - 793
EP - 798
DO - 10.5220/0012446400003657
PB - SciTePress