Abstract
With availability of large volume of collected data from healthcare centers and significant improvement in computation power, evidence based learning is helping in building robust disease diagnostic models.
In this work MIMIC-II database, consisting of physiologic waveforms and clinical Information about ICU patients, is used for patient classification, taking Coronary Artery Disease (CAD) as a use case.
A learning algorithm (wavelet transform + SVM) is trained and evaluated for CAD patient segregation with 89% accuracy on ICD-9 labeled MIMIC-II Photoplethysmogram (PPG) signals. Due to the noisy nature of machine collected MIMIC-II ICU data, the same SVM model was validated on a local hospital dataset containing doctor labeled PPG signals resulting a 5% accuracy gain.
This work is the first attempt of CAD patient classification on MIMIC-II, using heart rates from easily obtainable PPG signal suitable in mobile/wearable setting.
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Dey, S., Biswas, S., Pal, A., Mukherjee, A., Garain, U., Mandana, K. (2017). CAD Patient Classification Using MIMIC-II. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds) eHealth 360°. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-319-49655-9_44
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DOI: https://doi.org/10.1007/978-3-319-49655-9_44
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