Skip to main content

PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform.

Publication ,  Journal Article
Sutton, JR; Mahajan, R; Akbilgic, O; Kamaleswaran, R
Published in: IEEE journal of biomedical and health informatics
January 2019

Real-time analysis of streaming physiological data to identify earlier abnormal conditions is an important aspect of precision medicine. However, open-source systems supporting this workflow are lacking. In this paper, we present PhysOnline, a pipeline built on the open-source Apache Spark platform to ingest streaming physiological data for online feature extraction and machine learning. We consider scalability factors for horizontal deployment to support growing analysis requirements. We further integrate real-time feature extraction, including pattern recognition methods as well as descriptive statistical components to identify temporal characteristics of waveform signals. These generated features are then used for machine learning and for real-time classification of abnormal conditions. As a case study, we present the online classification of electrocardiography recordings for screening Paroxysmal Atrial Fibrillation (PAF) and demonstrate that our pipeline can predict persons developing PAF at least 45 min. before an episode of that condition. This pipeline can be applied in domains where pattern matching, temporal abstractions, and morphological characteristics can be used for real-time classification of streaming time-series data.1.

Duke Scholars

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

January 2019

Volume

23

Issue

1

Start / End Page

59 / 65

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Precision Medicine
  • Medical Informatics Applications
  • Machine Learning
  • Humans
  • Electrocardiography
  • Atrial Fibrillation
  • Algorithms
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Sutton, J. R., Mahajan, R., Akbilgic, O., & Kamaleswaran, R. (2019). PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE Journal of Biomedical and Health Informatics, 23(1), 59–65. https://doi.org/10.1109/jbhi.2018.2832610
Sutton, Jacob R., Ruhi Mahajan, Oguz Akbilgic, and Rishikesan Kamaleswaran. “PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform.IEEE Journal of Biomedical and Health Informatics 23, no. 1 (January 2019): 59–65. https://doi.org/10.1109/jbhi.2018.2832610.
Sutton JR, Mahajan R, Akbilgic O, Kamaleswaran R. PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE journal of biomedical and health informatics. 2019 Jan;23(1):59–65.
Sutton, Jacob R., et al. “PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform.IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 1, Jan. 2019, pp. 59–65. Epmc, doi:10.1109/jbhi.2018.2832610.
Sutton JR, Mahajan R, Akbilgic O, Kamaleswaran R. PhysOnline: An Open Source Machine Learning Pipeline for Real-Time Analysis of Streaming Physiological Waveform. IEEE journal of biomedical and health informatics. 2019 Jan;23(1):59–65.

Published In

IEEE journal of biomedical and health informatics

DOI

EISSN

2168-2208

ISSN

2168-2194

Publication Date

January 2019

Volume

23

Issue

1

Start / End Page

59 / 65

Related Subject Headings

  • Signal Processing, Computer-Assisted
  • Precision Medicine
  • Medical Informatics Applications
  • Machine Learning
  • Humans
  • Electrocardiography
  • Atrial Fibrillation
  • Algorithms