Date of Publication :20th February 2018

Abstract: Sleep apnea and snoring are two conceivably genuine sleep disorders. Sleep apnea is defined as pauses while breathing or infrequent breathing at the time of sleep and snoring is a sound delivered because of blocked air development amid breathing while in sleep mode. The current traditional technique used to diagnose these sleep disorders is polysomnography which is costly and requires human specialists and done in unique labs. Subsequently, there is a need of a more comfortable and less expensive technique to detect such types of disorders. As of late analysts concentrated on signal processing and pattern recognition as substitute modes to reveal them. The following research is focused on the detection of sleep apnea and snoring using ECG signals by applying Tunable Q-Factor wavelet transform (TQWT). The obtained results showed a high degree of accuracy, approximately 85%.

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