A Novel Multi-Parametric Algorithm for Faint Prediction Integrating Indices of Cardiac Inotropy and Vascular Tone



Neurally medicated syncope (NMS) patients
suffer from sudden loss of consciousness, which is associated
with a high rate of falls and hospitalization. NMS negatively
impacts a subject’s quality of life and is a growing cost issue for
the healthcare systems in particular since mainly elderly are at
risk of NMS in our aging societies.
In the present paper we present an algorithm for prediction
of NMS, which is based on the analysis of the
electrocardiogram (ECG) and photoplethysmogram (PPG)
signals. Several parameters extracted from ECG and PPG,
which have been associated in previous works with reflectory
mechanisms underlying NMS, were combined in a single
algorithm to detect impending syncope. The proposed
algorithm was validated in 43 subjects using a 3-way data split
scheme and achieved the following performance: sensitivity
(SE) - 100%; specificity (SP) - 92%; positive predictive value
(PPV) - 85%; false positive rate per hour (FPRh) - 0.146h-1
and; average prediction time (aPTime) - 217.58s.


Clinical Informatics


36th Int. Conf. of the IEEE Engineering in Medicine and Biology Society – EMBC’2014, August 2014

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