Real-Time Prediction of Neurally Mediated Syncope
Standard
Real-Time Prediction of Neurally Mediated Syncope. / Couceiro, R; Carvalho, P; Paiva, R P; Muehlsteff, J; Henriques, J; Eickholt, C; Brinkmeyer, C; Kelm, M; Meyer, C.
in: IEEE J BIOMED HEALTH, Jahrgang 20, Nr. 2, 03.2016, S. 508-520.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Real-Time Prediction of Neurally Mediated Syncope
AU - Couceiro, R
AU - Carvalho, P
AU - Paiva, R P
AU - Muehlsteff, J
AU - Henriques, J
AU - Eickholt, C
AU - Brinkmeyer, C
AU - Kelm, M
AU - Meyer, C
PY - 2016/3
Y1 - 2016/3
N2 - Neurally mediated 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 in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s.
AB - Neurally mediated 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 in our aging society, as its incidence increases with age. In this paper, we present a solution for prediction of NMS, which is based on the analysis of the electrocardiogram (ECG) and photoplethysmogram (PPG) alone. Several parameters extracted from ECG and PPG, associated with reflectory mechanisms underlying NMS in previous publications, were combined in a single algorithm to detect impending syncope. The proposed algorithm was evaluated in a population of 43 subjects. The feature selection, distance metric selection, and optimal threshold were performed in a subset of 30 patients, while the remaining data from 13 patients were used to test the final solution. Additionally, a leave-one-out cross-validation scheme was also used to evaluate the performance of the proposed algorithm yielding the following results: sensitivity (SE)--95.2%; specificity (SP)--95.4%; positive predictive value (PPV)--90.9%; false-positive rate per hour (FPRh)-0.14 h(-1), and prediction time (aPTime)--116.4 s.
KW - Adult
KW - Aged
KW - Aged, 80 and over
KW - Algorithms
KW - Blood Pressure/physiology
KW - Electrocardiography/methods
KW - Female
KW - Humans
KW - Male
KW - Middle Aged
KW - Photoplethysmography/methods
KW - Signal Processing, Computer-Assisted
KW - Syncope, Vasovagal/diagnosis
U2 - 10.1109/JBHI.2015.2408994
DO - 10.1109/JBHI.2015.2408994
M3 - SCORING: Journal article
C2 - 25769176
VL - 20
SP - 508
EP - 520
JO - IEEE J BIOMED HEALTH
JF - IEEE J BIOMED HEALTH
SN - 2168-2194
IS - 2
ER -
