Obstructive Sleep Apnea (OSA) is a disturbance of breathing during sleep with loud snoring and cessation of breath in short periods. OSA research has become a widely researched topic in recent years. OSA is characterized by repeated episodes of collapse and upper airway obstruction during sleep. OSA detection carried out using several biological signals such as EEG, ECG, or EMG. In this article, several studies of OSA detection using ECG signals are presented. From the calculation of the RR interval on the ECG signal, it can be seen the condition of a person's respiration, known as ECG-derived respiration (EDR). In this review, we discuss the feature extraction method and classifier used in OSA detection using ECG signals. In addition, we briefly describe the potential and its development in the future.