The challenge of detecting sleep disorders from consumer wearable sensors is attracting more and more researchers in the field. Sleep apnea has been the target of many sleep studies because this disorder has many health, physical, and mental consequences. Because obstruction in the airway is the direct cause of sleep apnea, overnight pulse oximetry provides valuable information to simplify the obstructive sleep apnea (OSA) screening. In this study, we aimed to estimate the apnea-hypopnea index (AHI) from consumer-grade lowgranularity oximetry data. We used 5804 sleep records from the Sleep Heart Health Study (SHHS) dataset for training and testing six different regression models. The best model achieved an R-square of 0.64±0.019 and ICC of 0.77±0.015. The estimated AHI was further converted to 4 levels of severity (ie, normal, mild, moderate, and severe). The macro F1-score, precision and recall were 0.576±0.044, 65.16±4.58 and 56.28±3.42, respectively. Central tendency measure, sample entropy and zero crossing of the oximetry data are the most important features for AHI estimation. Differences between male and female groups indicate a promising direction to improve the models' performance.