Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms

Yunsik Son, Seung Bo Lee, Hakseung Kim, Eun Suk Song, Hyub Huh, Marek Czosnyka, Dong Ju Kim

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Artifacts in physiological signals acquired during intensive care have the potential to be recognized as critical pathological events and lead to misdiagnosis or mismanagement. Manual artifact removal necessitates significant labor-time intensity and is subject to inter- and intra-observer variability. Various methods have been proposed to automate the task; however, the methods are yet to be validated, possibly due to the diversity of artifact types. Deep belief networks (DBNs) have been shown to be capable of learning generative and discriminative feature extraction models, hence suitable for classifying signals with multiple features. This study proposed a DBN-based model for artifact elimination in pulse waveform signals, which incorporates pulse segmentation, pressure normalization and decision models using DBN, and applied the model to artifact removal in monitoring arterial blood pressure (ABP). When compared with a widely used ABP artifact removal algorithm (signal abnormality index; SAI), the DBN model exhibited significantly higher classification performance (net prediction of optimal DBN = 95.9%, SAI = 84.7%). In particular, DBN exhibited greater sensitivity than SAI for identifying various types of artifacts (motion = 93.6%, biological = 95.4%, cuff inflation = 89.1%, transducer flushing = 97%). The proposed model could significantly enhance the quality of signal analysis, hence may be beneficial for use in continuous patient monitoring in clinical practice.

Original languageEnglish
Pages (from-to)145-158
Number of pages14
JournalInformation Sciences
Volume456
DOIs
Publication statusPublished - 2018 Aug 1

Keywords

  • Arterial pressure
  • Artifacts
  • Computer-assisted
  • Monitoring
  • Neural networks (computer)
  • Physiologic
  • Signal processing

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Automated artifact elimination of physiological signals using a deep belief network: An application for continuously measured arterial blood pressure waveforms'. Together they form a unique fingerprint.

  • Cite this