|
|||||
|
|
Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging |
|
| Authors: | Bo Pang Suraj Doshi Bhaswati Roy Milena Lai Luke Ehlert Ravi S. Aysola Daniel W. Kang Ariana Anderson Shantanu H. Joshi Daniel Tward Fabien Scalzo Susana Vacas Rajesh Kumar |
| Affiliation: | 1. Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, California, USA Department of Statistics, University of California Los Angeles, Los Angeles, California, USA;2. Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, California, USA;3. Department of Medicine, University of California Los Angeles, Los Angeles, California, USA;4. Department of Statistics, University of California Los Angeles, Los Angeles, California, USA Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California, USA;5. Department of Neurology, University of California Los Angeles, Los Angeles, California, USA Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA;6. Department of Neurology, University of California Los Angeles, Los Angeles, California, USA Department of Computational Medicine, University of California Los Angeles, Los Angeles, California, USA;7. Department of Neurology, University of California Los Angeles, Los Angeles, California, USA |
| Abstract: | Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m2; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m2; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC and accuracy over SVM, and either model can be used as a faster OSA screening tool for subjects having brain DTI data. |
| Keywords: | brain mean diffusivity random forest sleep disordered breathing support vector machine |