Improving the Performance of Multi-parameter Patient Monitor System Using Additional Features

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


Multi-parameter patient monitor (MPM) keep track of the condition of a patient in intensive care units (ICU) or general wards using the human vital parameters, heart rate, blood pressure, respiration rate and oxygen saturation (SpO2). A high accuracy for the overall classification, specificity and sensitivity is extremely important in providing quality health care to the patients. Support vector machine (SVM) is a powerful supervised algorithm that is effectively used in MPMs for classification. A careful study of the vital parameters in a healthy person reveals that there exists an intrinsic relationship between the four vital parameters, for example when heart rate is on the higher side, blood pressure is expected to be on the lower side and vice versa. Hence, it would be highly required to understand the correlation between the vital parameters and to integrate it into the MPM system. In this work, we present the results of the MPM using the SVM as back-end classifier. Further, we use correlation features (feature expansion) along with base parameters in an effort to improve the performance of MPM and note that the performance of the MPM enhanced significantly.


Multi-parameter patient monitor SVM Vital parameters Feature expansion Intersection kernel 


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Copyright information

© The Author(s) 2015

Authors and Affiliations

  • S. Premanand
    • 1
  • C. Santhosh Kumar
    • 1
  • A. Anand Kumar
    • 2
  1. 1.Machine Intelligence Research Laboratory, Department of Electronics and Communication EngineeringAmrita Vishwa VidyapeethamEttimadaiIndia
  2. 2.Department of NeurologyAmrita Institute of Medical SciencesCochinIndia

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