Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection

Michael Lenning, Joseph Fortunato, Tai Le, Isaac Clark, Ang Sherpa, Soyeon Yi, Peter Hofsteen, Geethapriya Thamilarasu, Jingchun Yang, Xiaolei H Xu, Huy Dung Han, Tzung K. Hsiai, Hung Cao

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Heart disease is the leading cause of mortality in the U.S. with approximately 610,000 people dying every year. Effective therapies for many cardiac diseases are lacking, largely due to an incomplete understanding of their genetic basis and underlying molecular mechanisms. Zebrafish (Danio rerio) are an excellent model system for studying heart disease as they enable a forward genetic approach to tackle this unmet medical need. In recent years, our team has been employing electrocardiogram (ECG) as an efficient tool to study the zebrafish heart along with conventional approaches, such as immunohistochemistry, DNA and protein analyses. We have overcome various challenges in the small size and aquatic environment of zebrafish in order to obtain ECG signals with favorable signal-to-noise ratio (SNR), and high spatial and temporal resolution. In this paper, we highlight our recent efforts in zebrafish ECG acquisition with a cost-effective simplified microelectrode array (MEA) membrane providing multi-channel recording, a novel multi-chamber apparatus for simultaneous screening, and a LabVIEW program to facilitate recording and processing. We also demonstrate the use of machine learning-based programs to recognize specific ECG patterns, yielding promising results with our current limited amount of zebrafish data. Our solutions hold promise to carry out numerous studies of heart diseases, drug screening, stem cell-based therapy validation, and regenerative medicine.

Original languageEnglish (US)
Article number61
JournalSensors (Switzerland)
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2018

Fingerprint

electrocardiography
Zebrafish
heart diseases
Electrocardiography
anomalies
Monitoring
Heart Diseases
therapy
Screening
screening
recording
machine learning
stem cells
mortality
Microelectrodes
Stem cells
temporal resolution
medicine
Learning systems
Signal to noise ratio

Keywords

  • ECG pattern recognition
  • Electrocardiogram (ECG)
  • Heart diseases
  • Machine learning
  • Phenotype screening
  • Real-time monitoring
  • Zebrafish

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

Cite this

Lenning, M., Fortunato, J., Le, T., Clark, I., Sherpa, A., Yi, S., ... Cao, H. (2018). Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection. Sensors (Switzerland), 18(1), [61]. https://doi.org/10.3390/s18010061

Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection. / Lenning, Michael; Fortunato, Joseph; Le, Tai; Clark, Isaac; Sherpa, Ang; Yi, Soyeon; Hofsteen, Peter; Thamilarasu, Geethapriya; Yang, Jingchun; Xu, Xiaolei H; Han, Huy Dung; Hsiai, Tzung K.; Cao, Hung.

In: Sensors (Switzerland), Vol. 18, No. 1, 61, 01.01.2018.

Research output: Contribution to journalArticle

Lenning, M, Fortunato, J, Le, T, Clark, I, Sherpa, A, Yi, S, Hofsteen, P, Thamilarasu, G, Yang, J, Xu, XH, Han, HD, Hsiai, TK & Cao, H 2018, 'Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection', Sensors (Switzerland), vol. 18, no. 1, 61. https://doi.org/10.3390/s18010061
Lenning, Michael ; Fortunato, Joseph ; Le, Tai ; Clark, Isaac ; Sherpa, Ang ; Yi, Soyeon ; Hofsteen, Peter ; Thamilarasu, Geethapriya ; Yang, Jingchun ; Xu, Xiaolei H ; Han, Huy Dung ; Hsiai, Tzung K. ; Cao, Hung. / Real-time monitoring and analysis of zebrafish electrocardiogram with anomaly detection. In: Sensors (Switzerland). 2018 ; Vol. 18, No. 1.
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