A Multilayer LSTM Auto-Encoder for Fetal ECG Anomaly Detection...18th International Conference on Wearable Micro and Nano Technologies for Personalized Health (virtual), November 8-10, 2021.
The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method...
| Publicado en: | Studies in Health Technology & Informatics Vol. 285; pp. 147 - 153 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas proceedings research tables/charts tracings Journal Article |
| Publicado: |
Sage Publications Inc.
2021
|
| 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=153525186&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153525186 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2021 vid: 285 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 153525186 153525186 153525186 10.3233/SHTI210588 153525186 ppf: 147 ppct: 6 formats: tig: atl: A Multilayer LSTM Auto-Encoder for Fetal ECG Anomaly Detection...18th International Conference on Wearable Micro and Nano Technologies for Personalized Health (virtual), November 8-10, 2021. aug: au: SKARGA-BANDUROVA, Inna BILOBORODOVA, Tetiana SKARHABANDUROV, Illia BOLTOV, Yehor DERKACH, Maryna affil: School of Engineering, Computing and Mathematics, Oxford Brookes University sug: subj: Fetal Monitoring Methods Heart Defects, Congenital Diagnosis Electrocardiography Methods Signal Processing, Computer Assisted Neural Networks (Computer) Congresses and Conferences Human Reproducibility of Results Deep Learning Software Design ab: The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method provides a filtering procedure able to reproduce ECG variability based on the semi-supervised paradigm. Experiments show that the proposed method can learn better features than the traditional approach without any prior knowledge and subject to proper signal identification can facilitate the analysis of fetal ECG signals in daily life. pubtype: Academic Journal doctype: equations & formulas proceedings research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|