There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation of possible pulse and pressure variation. In total there were analyzed more than 15000 heartbeats and 21 parameters were extracted from each of them that define the input vector for the ANN. The comparison between estimated and reference values shows better accuracy than the linear regression method and satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.

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This study investigates the usability of the smartphone camera for the evaluation of arterial blood oxygenation (SpO 2 %). The advantage of this solution derives from the pervasiveness of the smartphone that makes available the evaluation of the SpO 2 % everywhere. Differently from the pulse oximeter, which uses welldefined wavelength light, the smartphone uses Light Emitting Diodes as a light source to evaluate the SpO 2 %. The change of the light intensity in the Red and Green colour channels in the video frames of the patient fingertip are properly processed. Two PPG signals are obtained at the wavelengths 600nm and 940nm, respectively. These two PPGs are used to evaluate the SpO 2 % without calibration coefficients and independently of the smartphone hardware and skin characteristics. Experimental tests are performed to compare the proposed procedure with respect to a commercial pulse oximeter and gas chromatograph. The experimental tests assess the effectiveness of the proposal.

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