Translational Biomedicine

  • ISSN: 2172-0479
  • Journal h-index: 18
  • Journal CiteScore: 5.91
  • Journal Impact Factor: 4.11
  • Average acceptance to publication time (5-7 days)
  • Average article processing time (30-45 days) Less than 5 volumes 30 days
    8 - 9 volumes 40 days
    10 and more volumes 45 days
Awards Nomination 20+ Million Readerbase
Indexed In
  • Open J Gate
  • Genamics JournalSeek
  • JournalTOCs
  • ResearchBible
  • The Global Impact Factor (GIF)
  • China National Knowledge Infrastructure (CNKI)
  • CiteFactor
  • Scimago
  • Electronic Journals Library
  • Directory of Research Journal Indexing (DRJI)
  • OCLC- WorldCat
  • Proquest Summons
  • Publons
  • MIAR
  • University Grants Commission
  • Geneva Foundation for Medical Education and Research
  • Google Scholar
  • Secret Search Engine Labs
  • ResearchGate
Share This Page


Non-Invasive Alarm Generation for Sudden Cardiac Arrest: A Pilot Study with Visibility Graph Technique

Nilanjana P, Anirban B, Susmita B and Dipak G

Objective: The objective of this work is to formulate biomarkers based on visibility graph technique for early detection of sudden cardiac arrests.

Background: Sudden cardiac arrest is a serious medical condition claiming numerous lives across the globe. The fact that in most cases the victim succumbs to death within an hour of the onset of symptoms itself indicates the dreadful nature of the disease. This very fact has prompted us to analyze an ECG waveform using a chaos based non-linear technique viz. visibility graph technique.

Method: The visibility graph algorithm has been used to convert the ECG time series data into visibility graphs and then calculate power of scale-freeness in visibility graph (PSVG) values at an interval of one minute across the time series. Subsequently, comparative analysis of PSVG values has been carried out between diseased and normal subjects.

Results: The comparative study of PSVG values of diseased and normal subjects clearly shows that for diseased subjects mean PSVG is always less and this difference is statistically significant. Decrease in PSVG values is an indicator of dysfunction of the heart and the extent of deviation is an indicator of the degree of dysfunction.

Conclusion: The analysis leads to formulation of biomarkers which may be used to detect abnormalities of the human heart sufficiently ahead of the time before it turns out to be fatal.