Onset and Peak Pattern Recognition on Photoplethysmographic Signals Using Neural Networks

  • Alvaro D. Orjuela-Cañón
  • Denis Delisle-Rodríguez
  • Alberto López-Delis
  • Ramón Fernandez de la Vara-Prieto
  • Manuel B. Cuadra-Sanz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for onset and peak detection on the arterial pulse wave. In the present work a Multilayer Perceptron (MLP) neural network is used for classifying fiducial points on photoplethysmographic (PPG) signals. System was trained with a dataset of temporal segments from signals located based on information about onset and peak points. Different segments sizes and units in the neural network were used for the classification, and optimal values were searched. Results of the classification reach 98.1% in worse of cases. This proposal takes advantages from MLP neural networks for pattern classification. Additionally, the use of ECG signal was avoided in the presented methodology, making the system robust, less expensive and portable in front of this problem.

Keywords

Arterial Pulse Wave Artificial Neural Networks Multilayer Perceptron Onset Classification Peak Classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alvaro D. Orjuela-Cañón
    • 1
  • Denis Delisle-Rodríguez
    • 2
  • Alberto López-Delis
    • 2
  • Ramón Fernandez de la Vara-Prieto
    • 2
  • Manuel B. Cuadra-Sanz
    • 3
  1. 1.GIBIO - Electronic and Biomedical FacultyUniversidad Antonio NariñoBogotá D.C.Colombia
  2. 2.Center of Medical BiophysicsUniversidad de OrienteSantiago de CubaCuba
  3. 3.CIDEI (Research and Technologic Development Center for the Electro-Electronics and Informatics Industry)Bogotá D.C.Colombia

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