Efficient syncope prediction from resting state clinical data using wavelet bispectrum and multilayer perceptron neural network.
Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify NMS. In this work, an effort to predict the NMS before performing the HUTT is attempted. To achieve this, the heart rate variability (HRV) at rest and du...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 6; pp. 1311 - 1325 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150893729&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150893729 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jun2021 vid: 59 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150893729 150194522 150893729 NLM33959855 10.1007/s11517-021-02353-7 NLM33959855 150893729 ppf: 1311 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Efficient syncope prediction from resting state clinical data using wavelet bispectrum and multilayer perceptron neural network. aug: au: Myrovali, Evangelia Fragakis, Nikolaos Vassilikos, Vassilios Hadjileontiadis, Leontios J. affil: Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR 54645, Thessaloniki, Greece sug: subj: Syncope, Vasovagal Tilt-Table Test Syncope Heart Rate Scales ab: Neurally mediated syncope (NMS) is the most common type of syncope, and head up tilt test (HUTT) is, so far, the most appropriate tool to identify NMS. In this work, an effort to predict the NMS before performing the HUTT is attempted. To achieve this, the heart rate variability (HRV) at rest and during the first minutes of tilting position during HUTT was analyzed using both time and frequency domains. Various features from HRV regularity and complexity, along with wavelet higher-order spectrum (WHOS) analysis in low-frequency (LF) and high-frequency (HF) bands were examined. The experimental results from 26 patients with history of NMS have shown that at rest, a time domain entropy measure and WHOS-based features in LF band exhibit significant differences between positive and negative HUTT as well as among 10 healthy subjects and NMS patients. The best performance of multilayer perceptron neural network (MPNN) was achieved by using an input vector consisted of WHOS-based HRV features in the LF zone and systolic blood pressure from the resting period, yielding an accuracy of 89.7%, assessed by 5-fold cross-validation. The promising results presented here pave the way for an early prediction of the HUTT outcome from resting state, contributing to the identification of patients at higher risk NMS. The HRV analysis along with systolic blood pressure at rest predict NMS using a multilayer perceptron neural network. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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