1. Real-Time Monitoring and Analysis of Zebrafish Electrocardiogram with Anomaly Detection
- Author
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Ang Sherpa, Isaac Clark, Peter Hofsteen, Huy-Dung Han, Michael Lenning, Jingchun Yang, Geethapriya Thamilarasu, Hung Cao, Tzung K. Hsiai, Joseph Fortunato, Xiaolei Xu, Soyeon Yi, and Tai Le
- Subjects
0301 basic medicine ,Engineering ,Heart disease ,Model system ,Computational biology ,030204 cardiovascular system & hematology ,Signal-To-Noise Ratio ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,03 medical and health sciences ,Electrocardiography ,0302 clinical medicine ,phenotype screening ,medicine ,Animals ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,Zebrafish ,heart diseases ,biology ,business.industry ,ECG pattern recognition ,Electrical engineering ,Heart ,biology.organism_classification ,medicine.disease ,zebrafish ,real-time monitoring ,electrocardiogram (ECG) ,Atomic and Molecular Physics, and Optics ,machine learning ,3. Good health ,030104 developmental biology ,Aquatic environment ,Anomaly detection ,Ecg signal ,business ,Microelectrodes - 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.
- Published
- 2017
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