Automated obstructive sleep apnoea detection using symmetrically weighted local binary patterns. Issue 4 (1st February 2017) - Sunil Kumar, T.; Kanhangad, Vivek
Abstract : This Letter presents a computer‐aided methodology for automated obstructive sleep apnoea (OSA) detection using the proposed symmetrically weighted local binary pattern (SLBP)‐based features. The SLBP, which is a variant of one‐dimensional local binary pattern (LBP), generates a binary pattern by making comparisons in the left and right neighbourhood of a sample. However, as opposed to LBP, the generated binary information is encoded into decimal value by using a symmetric weighting scheme. The proposed encoding scheme helps to reduce the length of the feature vector significantly. Experimental evaluations on the Physionet sleep apnoea single‐lead electrocardiography signals suggest that the proposed SLBP features are effective in detecting OSA with an accuracy of 89.80%. Our results also show that the proposed SLBP achieves a good trade‐off between the classification and computational performance among different variants of LBP. Further, the proposed approach outperforms recently proposed methodologies for OSA detection.