Polysomnography (PSG) studies are considered the "gold standard" for the diagnosis of Sleep Apnoea (SA). Identifying cessations of breathing from long-lasting PSG recordings manually is a labour-intensive and time-consuming task for sleep specialist, associated with inter-scorer variability. In this study a simplified, semi-automatic, three-channel method for detection of SA patients is proposed in order to increase analysis reliability and diagnostic accuracy in the clinic. The method is based on characteristic features, such as respiration stoppages pr. hour and the total number of oxygen desaturations > 3%, extracted from the thorax and abdomen respiration effort belts, and the oxyhemoglobin saturation (SaO2), fed to an Elastic Net classifier and validated according to American Academy of Sleep Medicine (AASM) using the patients' AHI value. The method was applied to 109 patient recordings and resulted in a very high SA classification with accuracy of 97.9%. The proposed method reduce the time spent on manual analysis of respiration stoppages and the inter- and intra-scorer variability, and may serve as an alternative screening method for SA.