Automatic Apnea Identification by Transformation of the Cepstral Domain

作者：Travieso Carlos M^*; Alonso Jesus B; del Pozo Banos Marcos; Ticay Rivas Jaime R; Lopez de Ipina Karmele

来源：Cognitive Computation, 2013, 5(4): 558-565.

DOI：10.1007/s12559-012-9184-x

• Automatic apnea detection
• Artifacts removal
• Hidden Markov model
• Kernel building
• Machine learning
• Pattern recognition
• Nonlinear processing

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.

• 出版日期2013-12