In a previous work, a method was presented to detect sleep apnea using signals commonly found in wearable devices: peripheral oxygen saturation (SpO2) and pulse photoplethysmography (PPG). However, these signals were obtained from a conventional fingertip pulse oximeter. This paper describes a pilot study applying those methods with signals acquired by a wrist-worn wearable device in a non-supervised home environment (n = 12). A classifier was applied to differentiate normal from abnormal breathing segments. Later, the Cyclic Variation of Heart Rate Index (CVHRI) was calculated within the abnormal breathing segments. The classifier achieved an accuracy of 65.7% on the wearable data and CVHRI maintained a strong correlation with the AHI (r = 0.85, p < 0.001), suggesting its potential for patient stratification remains viable.