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- W2538428457 abstract "We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.T. Physionet Apnea-ECG database. Performance was assessed with leave-one-record-out cross-validation. The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. These results are comparable with best results reported on this database." @default.
- W2538428457 created "2016-10-28" @default.
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- W2538428457 date "2016-08-01" @default.
- W2538428457 modified "2023-09-27" @default.
- W2538428457 title "Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters" @default.
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- W2538428457 doi "https://doi.org/10.1109/embc.2016.7591410" @default.
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