Name
Title | ||
|---|---|---|
A Bayesian Neural Network Approach To Compare The Spectral Information From Nasal Pressure And Thermistor Airflow In The Automatic Sleep Apnea Severity Estimation |
Abstract | ||
|---|---|---|
In the sleep apnea-hypopnea syndrome (SAHS) context, airflow signal plays a key role for the simplification of the diagnostic process. It is measured during the standard diagnostic test by the acquisition of two simultaneous sensors: a nasal prong pressure (NPP) and a thermistor (TH). The current study focuses on the comparison of their spectral content to help in the automatic SAHS-severity estimation. The spectral analysis of 315 NPP and corresponding TH recordings is firstly proposed to characterize the conventional band of interest for SAHS (0.025-0.050 Hz.). A magnitude squared coherence analysis is also conducted to quantify possible differences in the frequency components of airflow from both sensors. Then, a feature selection stage is implemented to assess the relevance and redundancy of the information extracted from the spectrum of NPP and TH airflow. Finally, a multiclass Bayesian multi-layer perceptron (BY-MLP) was used to perform an automatic estimation of SAHS severity (no-SAHS, mild, moderate, and severe), by the use of the selected spectral features from: airflow NPP alone, airflow TH alone, and both sensors jointly. The highest diagnostic performance was reached by BY-MLP only trained with NPP spectral features, reaching Cohen's kappa = 0.498 in the overall four-class classification task. It also achieved 91.3%, 84.9%, and 83.3% of accuracy in the binary evaluation of the 3 apnea-hypopnea index cut-offs (5, 15, and 30 events/hour) that define the four SAHS degrees. Our results suggest that TH sensor might be not necessary for SAHS severity estimation if an automatic comprehensive characterization approach is adopted to simplify the diagnostic process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8037670 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Sleep apnea,Feature selection,Computer science,Electronic engineering,Redundancy (engineering),Airflow,Bayesian neural networks,Perceptron,Thermistor,Bayesian probability | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
3 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gonzalo C. Gutiérrez-Tobal | 1 | 31 | 10.29 |
| Julio de Frutos | 2 | 0 | 0.34 |
| Daniel Álvarez | 3 | 212 | 25.22 |
| Fernando Vaquerizo-Villar | 4 | 6 | 6.92 |
| Verónica Barroso-García | 5 | 6 | 6.59 |
| Andrea Crespo | 6 | 5 | 3.47 |
| Félix del Campo | 7 | 126 | 18.12 |
| Roberto Hornero | 8 | 603 | 67.74 |