Ensemble approach on deep and handcrafted features for neonatal bowel sound detection

Lachie Burne, Chiranjibi Sitaula, Archana Priyadarshi, Mark Tracy, Omid Kavehei, Murray Hinder, Anusha Withana, Alistair McEwan, Faezeh Marzbanrad

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis plan for early intervention. In this paper, a novel technique is proposed for automated peristalsis sound detection from neonatal abdominal sound recordings and compared to various other machine learning approaches. It adopts an ensemble approach that utilises handcrafted as well as one and two dimensional deep features obtained from Mel Frequency Cepstral Coefficients (MFCCs). The results are then refined with the help of a hierarchical Hidden Semi-Markov Models (HSMM) strategy. We evaluate our method on abdominal sounds collected from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results of leave-one-patient-out cross validation show that our method provides an accuracy of 95.1&#x0025; and an Area Under Curve (AUC) of 85.6&#x0025;, outperforming both the baselines and the recent works significantly. These encouraging results show that our proposed Ensemble-based Deep Learning model is helpful for neonatologists to facilitate tele-health applications. The source code of our proposed work can be found at: <uri>https://gitlab.com/lbur0010/ensemble-approach-to-neonatal-bowel-sound-classification/-/tree/main/</uri>.

Original languageEnglish
Pages (from-to)2603-2613
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume27
Issue number6
DOIs
Publication statusPublished - Jun 2023

Keywords

  • bi-linear feature fusion
  • bowel sounds
  • Data models
  • deep learning
  • Deep learning
  • ensemble learning
  • Feature extraction
  • machine learning
  • Mel frequency cepstral coefficient
  • MFCC
  • Modeling
  • Pediatrics
  • Recording

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