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Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

  • S.I. : Advances in Bio-Inspired Intelligent Systems
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Abstract

Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources.

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Acknowledgements

All the authors acknowledge the Portuguese Foundation for Science and Technology for their support through Projeto Estratégico LA 9—UID/EEA/50009/2013. S. S. Mostafa acknowledges ARDITI—Agência Regional para o Desenvolvimento e Tecnologia under the scope of the Project M1420-09-5369-FSE-000001—PhD Studentship.

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Authors and Affiliations

  1. Instituto Superior Técnico da Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Sheikh Shanawaz Mostafa

  2. MITI - Madeira Interactive Technologies Institute, Caminho da Penteada, 9020-105, Funchal, Portugal

    Sheikh Shanawaz Mostafa & Fernando Morgado-Dias

  3. Universidade da Madeira, Praca do Município 17, 9000-034, Funchal, Portugal

    Fernando Morgado-Dias

  4. Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Campus Universitario de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    Antonio G. Ravelo-García

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  1. Sheikh Shanawaz Mostafa
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  2. Fernando Morgado-Dias
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  3. Antonio G. Ravelo-García
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Correspondence to Sheikh Shanawaz Mostafa.

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Appendix

Appendix

See Tables 6, 7, 8, 9 and 10 and Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15.

Table 6 Feature sequence for PhysioNet and UCDDB database
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Table 7 mRMR feature rank for PhysioNet database (the feature number of this table belongs to the sequence number of Table 6)
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Table 8 mRMR features rank for UCDDB database (the feature number of this table belongs to the sequence number of Table 6)
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Table 9 SFS feature sequence for PhysioNet database (best classification accuracy is achieved by bold features, and the feature number of this table belongs to the sequence number of Table 6)
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Table 10 SFS feature sequence for UCDDB database (the best classification accuracy is achieved by bold features, and the feature number of this table belongs to the sequence number of Table 6)
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Fig. 4
figure 4

Accuracy of PhysioNet mRMR method

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Fig. 5
figure 5

Accuracy of UCDDB mRMR method

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Fig. 6
figure 6

Accuracy of PhysioNet SFS method

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Fig. 7
figure 7

Accuracy of UCDDB SFS method

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Fig. 8
figure 8

Sensitivity of PhysioNet mRMR method

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Fig. 9
figure 9

Sensitivity of UCDDB mRMR method

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Fig. 10
figure 10

Sensitivity of PhysioNet SFS method

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Fig. 11
figure 11

Sensitivity of UCDDB SFS method

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Fig. 12
figure 12

Specificity of PhysioNet mRMR method

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Fig. 13
figure 13

Specificity of UCDDB mRMR method

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Fig. 14
figure 14

Specificity of PhysioNet SFS method

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Fig. 15
figure 15

Specificity of UCDDB SFS method

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Mostafa, S.S., Morgado-Dias, F. & Ravelo-García, A.G. Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection. Neural Comput & Applic 32, 15711–15731 (2020). https://doi.org/10.1007/s00521-018-3455-8

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