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  3. A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

  • Microsyst Nanoeng. 2022 May 9;8:49. doi: 10.1038/s41378-022-00383-1.
Hao Wang  # 1,  Yue Wu  # 2,  Quchao Zou  # 3,  Wenjian Yang 2,  Zhongyuan Xu 2,  Hao Dong 2,  Zhijing Zhu 4 5,  Depeng Wang 6,  Tianxing Wang 7,  Ning Hu 1 3 8,  Diming Zhang 2
Affiliations

Affiliations

  • 1 State Key Laboratory of Optoelectronic Materials and Technologies, Guangdong Province Key Laboratory of Display Material and Technology, School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006 China.
  • 2 Research Center for Intelligent Sensing Systems, Zhejiang Lab, Hangzhou, 311121 China.
  • 3 ZJU-Hangzhou Global Scientific and Technological Innovation Center, Department of Chemistry, The Second Affiliated Hospital Zhejiang University School of Medicine, Department of Clinical Medical Engineering, Zhejiang University, Hangzhou, 310058 China.
  • 4 Key Laboratory of Novel Target and Drug Study for Neural Repair of Zhejiang Province, School of Medicine, School of Computer & Computing Science, Zhejiang University City College, Hangzhou, 310015 China.
  • 5 School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058 China.
  • 6 College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 China.
  • 7 E-LinkCare Meditech Co., Ltd., Hangzhou, 310011 China.
  • 8 State Key Laboratory of Transducer Technology, Chinese Academy of Sciences, Shanghai, 200050 China.
  • # Contributed equally.
PMID: 35547605 DOI: 10.1038/s41378-022-00383-1
Abstract

Cardiovascular Disease is the number one cause of death in humans. Therefore, cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug development. Recently, cell-based techniques developed for arrhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large datasets. We carried out the first report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm (MNDA) analysis for drug-induced arrhythmia recognition and classification. The biosensing system cultured cardiomyocytes as physiologically relevant models, used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes, and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating recording. The best performing MNDA parameter, approximate entropy, enabled the system to recognize the appearance of sertindole- and norepinephrine-induced arrhythmia in the recording. The MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of arrhythmia. This new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

Keywords

Electrical and electronic engineering; Micro-optics.

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