A new approach based on the transformation of the Cepstral domain is developed on this work. This approach reaches an automatic diagnosis for the syndrome of obstructive sleep apnea that includes a specific block for the removal of electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by a transformation of the Cepstral domain sequence using hidden Markov model (HMM). The final decision is done with two different approaches: one based on HMM as a classifier and a second one based on support vector machines classification and a parameterization based on the transformation of HMM by a kernel. The later approach reached results up to 99.23 %, using all test samples from Physionet Apnea-ECG Database.
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This work was supported by funds from “Cátedra Telefónica 2009/10—ULPGC,” in Spain, under the reference ARUCAS.
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Travieso, C.M., Alonso, J.B., del Pozo-Baños, M. et al. Automatic Apnea Identification by Transformation of the Cepstral Domain. Cogn Comput 5, 558–565 (2013). https://doi.org/10.1007/s12559-012-9184-x