This study focuses on the analysis of airflow (AF) recordings to help in sleep apnea-hypopnea syndrome (SAHS) diagnosis. The objective is to estimate the apnea-hypopnea index (AHI) by means of spectral features from AF data. Multiple linear regression (MLR) was used for this purpose. A training group is used to obtain two MLR models: the first one consisting of features obtained from the full PSDs (MLR(full)) and the second one consisting of features from a new frequency band of interest (MLR(band)). Then a test group is used to validate the final model. The correlation of spectral features and MLR models with AHI was compared by means of Pearson's coefficient (ρ). MLR(band) reached the highest ρ (0.809). Four different AHI decision thresholds were used to evaluate MLR(band) ability to distinguish the severity of SAHS. The accuracy achieved was higher as the threshold increased (69.7%, 75.3%, 80.9%, 87.6%) These results suggest that the automated estimation of AHI through spectral features can provide useful knowledge about SAHS severity.