Abstract—Traditional methodologies use electrocardiographic (ECG) signals to develop automatic methods for onset and peak detection on the arterial pulse wave. An alternative method using pattern recognition is implemented to detect onset and peak fiducial points, using Self Organizing Maps (SOM). In the present work SOM neural networks were trained with a dataset of signals with information about localization of onset and peak points. Later on, the trained network was used to make the detection on a validation dataset. This was developed using a shifting temporal windowing, which is presented to the network to decide whether the window corresponds to an onset or peak in the pulse wave. Results of the classification reach 97.93% over the validation dataset. Sensitivity and positive predictivity measures were used to assess the proposed method, reaching 100% for sensitivity and 99.84% for the positive predictivity detecting peaks in the signals. This proposal takes advantages from SOM neural networks for pattern classification and detection. Additionally, ECG signal is not necessary in the presented methodology.

Index Terms—Electrocardiography, fiducial points photoplethysmography, self-organizing maps.

A. D. Orjuela-Cañón and H Posada-Quintero are with GIBIO - Electronic and Biomedical Faculty, Universidad Antonio Nariño, Bogotá D.C. – Colombia (e-mail: alvorjuela@uan.edu.co, hposada@uan.edu.co).
D. Delisle, R. Fernandez and D. A. Lopez are currently with Medical Biophysics Center in the Universidad de Oriente, Santiago de Cuba, Cuba (e-mail: denis.delisle@cbiomed.cu, ramon.fernandez@cbiomed.cu, alberto.lopez@cbiomed.cu).
M. Cuadra is with Medical Biophysics Center, Universidad de Oriente, Santiago de Cuba, Cuba (e-mail: manuel.cuadra@cbiomed.cu).

 

Cite:A. Orjuela-Cañón, H. Posada-Quintero, D. Delisle-Rodríguez, M. Cuadra-Sanz, R. Fernández de la Vara-Prieto, and A. López-Delis, "Onset and Peak Detection over Pulse Wave Using Supervised SOM Network," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 2, pp. 133-137, 2013.