NDLI: A novel multi-parametric algorithm for faint prediction integrating indices of cardiac inotropy and vascular tone

Content Provider	IEEE Xplore Digital Library
Author	Couceiro, R. Carvalho, P. Paiva, R.P. Muehlsteff, J. Henriques, J. Eickholt, C. Brinkmeyer, C. Kelm, M. Meyer, C.
Copyright Year	2014
Description	Author affiliation: Div. of Cardiology, Pneumology, & Angiology, Heinrich-Heine Univ. Hosp. Dusseldorf, Düsseldorf, Germany (Eickholt, C.; Brinkmeyer, C.; Kelm, M.; Meyer, C.) \|\| Philips Res. Labs. Eur., Eindhoven, Netherlands (Muehlsteff, J.) \|\| Dept. of Inf. Eng., Univ. of Coimbra, Coimbra, Portugal (Couceiro, R.; Carvalho, P.; Paiva, R.P.; Henriques, J.)
Abstract	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.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	2952
Ending Page	2956
File Size	3861390
Page Count	5
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944242
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Prediction algorithms Biomedical monitoring Algorithm design and analysis Blood pressure Electrocardiography Heart rate
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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