Journal of Robotics, Networking and Artificial Life
Vol. 7(1); June (2020), pp. 48–51
DOI: https://doi.org/10.2991/jrnal.k.200512.010; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Spike Sorting Tool for Analysis of Cardiac Extracellular
Signals Recorded by Thin-Film-Transistor Sensor Arrays
Anne-Claire Eiler1,*, Junichi Sugita2, Satoshi Ihida3, Hiroshi Toshiyoshi4, Katsuhito Fujiu1,
Timothée Levi5,6, Agnès Tixier-Mita7
Department of Advanced Interdisciplinary Studies, Graduate School of Engineering, The University of Tokyo, 4-6-1 Komaba,Meguro-ku, Tokyo 153-8505, Japan2
Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
3Development Group, Display Device Company, Sharp Corporation, Tenri, Nara, Japan
4
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan5
IMS lab, University of Bordeaux, Talence, 351 cours de la Liberation, 33400 France
6
LIMMS/CNRS-IIS (UMI-2820), Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
7Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan1ARTICLE INFOABSTRACTArticle History

The dynamical property of the heart bioelectrical system is closely associated with cardiac diseases. For this reason, there is a
growing interest in the development of system analysis for studying the cardiac signaling network. In this article, extracellularpotentials of cardiac muscle cells were measured on an array of microelectrodes using Thin-Film-Transistor (TFT) technology,and electrophysiological data was analyzed using a spike sorting technique. This study shows the possibility of extracting usefulbioelectrical information from the extracellular signals recorded by TFT arrays.

Received 01 November 2019
Accepted 29 February 2020
Keywords
Thin-film-transistor arrays
spike sorting
extracellular potentials
cardiomyocytes© 2020 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Cardiomyocytes are primary muscle cells derived from heart tissue
that can generate and conduct bioelectrical signals for cardiac contraction and blood flow. Thus, a problem occurring in the cellularbioelectrical network can range from minor to fatal inconvenience[1]. The cells can retain their physiological functions and thus provide a useful in vitro model to look at the beating rate, the durationand the shape of the field potential. In vitro research of the generalbehavior of cardiomyocytes can help to understand arrhythmia,
long Q-T syndrome, and cardiotoxicity. As a result, in vitro study ofcardiomyocytes represents a valuable tool for drug discovery anddisease modeling.
However, signal processing and data analysis of extracellular
recordings remain intensive, and new tools are needed to makeelectrophysiological data handling and analysis easier.
In light of this problem, this paper proposes a spike sorting
algorithm for the analysis of cardiac extracellular potentials
measured on an array of microelectrodes using a new Thin-FilmTransistor (TFT) sensor array developed by Sharp Corporation.
Measurement of neuronal potentials was already demonstrated
with TFT arrays [2]. The data flow generated by large arrays must
be compressed to envision compact data acquisition systems.
Corresponding author. Email: anne@iis.u-tokyo.ac.jp; www.u-tokyo.ac.jp*Hence, the electrical signals have been analyzed using a MATLAB
program developed for biosignal processing. The recorded signals
were filtered for the detection of spikes, which were then grouped
into clusters according to their similar features. Through this analysis, the experiments demonstrated the possibility of obtaining
accurate spike sorting of extracellular recordings on TFT arrays.
Future implementation of the program in Field Programmable
Gate Array (FPGA) will allow real-time analysis for automatic cellstimulation with a closed-loop system.

2. MEASUREMENT METHOD
2.1. Thin-Film-Transistor Arrays
Thin-film-transistor arrays were used for in vitro recording ofextracellular signals from cardiomyocytes. TFT technology is wellknown for Liquid Crystal Display in appliances including televisionsets, computer monitors, or mobile phones. Here, TFT technology
is used for biological applications [3].
The standard type of TFT array comes in a pattern of 150 × 150
transparent microelectrodes and is mounted on a printed circuitboard. Microelectrodes are composed of indium tin oxide with asize of 100 × 100 µm2. The array of microelectrodes is controlled
by an array of TFTs, which are used for switching ON/OFF themicroelectrodes. The TFTs are controlled by means of gate andA.-C. Eiler et al. / Journal of Robotics, Networking and Artificial Life 7(1) 48–51

source/drain lines. The columns of the array control the gates of theTFTs, while the rows control the sources. When a 12 V DC voltage
is applied to one gate line, all the microelectrodes connected to thatline are activated. One or more source lines are then connected to ameasurement system for sensing.
Extracellular signals of cardiomyocytes were measured using a
Multi-Channel Measurement System and optical observations
were performed simultaneously with an inverted microscope.
Figure 1 describes the working principle of TFT arrays and theexperimental setup.

49

used for electrophysiological data analysis. This data processing
technique consists in identifying the spiking activities of cells that
contribute to the signal recorded by each microelectrode [4]. The
identified basic functions are: (1) bandwidth reduction for selective
band amplification and noise reduction; (2) discrimination threshold computation; (3) detection and alignment of biological spikesignals; (4) extraction of spike shape features; and finally (5) spikeclustering. Those functions are depicted in Figure 2.

3. RESULTS

2.2. Culture of Cardiomyocytes

3.1. Bioelectrical Signal Recording

Cardiomyocytes were dissociated into single-cell suspension fromneonatal mice hearts by combining mechanical dissociation withenzymatic degradation of the extracellular matrix, which maintains
the structural integrity of tissues.

In this study, extracellular potentials of cardiomyocytes wererecorded on an array of 28 × 28 microelectrodes. The measured
noise level was approximately ±50 µV. Extracellular recordings ofcardiomyocyte cultures was confirmed by optical visualization ofcell contraction using an inverted microscope (Figure 3). Here, aline of four microelectrodes was selected and data was extracted forthe ensuing processing of the bioelectrical signals.

The neonatal hearts were enzymatically digested using the neonatal heart dissociation kit for mouse from Miltenyi Biotec (GmbH,Bergisch Gladbach, Germany) and gentleMACS™ Dissociator wasused for the mechanical dissociation steps. After dissociation, thesample was filtered to remove any remaining larger particles from thesingle-cell suspension. Finally, red blood cells were lysed and cardiomyocytes were resuspended with appropriate cell culture medium.
Cardiomyocytes were finally cultured for 3 days on the TFT arraydevices without surface treatment.

3.2. Bioelectrical Signal Processing
Bioelectrical signal processing of extracellular signals acquiredby TFT arrays was performed. This technique provided valuable
information about the characteristics of spontaneous bioelectricalsignals in cardiomyocyte networks. In this paper, electrophysiological data of a decrease in temperature was analyzed.

2.3. Bioelectrical Analysis
Embedded signal processing is an essential step in the developmentof recording instrumentation. Here, a spike sorting algorithm wasa

3.2.1. Filtered data
Filtering of raw data was performed for noise reduction by removing undesired signals according to their frequency. To detect cardiomyocyte electrical activity, signals were band-pass filtered withbFigure 2 | Workflow of the functions performed by the spike sorting
algorithm.

Figure 1 | (a) Working principle of the transparent TFT substrate, with aclose-up view of the array of 100 μm2 microelectrodes. (b) Experimental
setup.

Figure 3 | Culture of cardiomyocytes on TFT array device, 3 days after cellseeding.

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50

cut-off frequencies at 200 and 3000 Hz. The sampling rate of therecorded data was 10 kHz. Figure 4 shows an example of raw andfiltered data.

3.2.2. Spike detection and alignment
A predefined threshold was used for distinguishing extracellularpotentials of cardiomyocytes from background noise. For eachmicroelectrode, spikes were then detected and aligned with respect
to the maximum of their absolute value, as shown in Figure 5.
Figure 7 | Three clusters of spikes detected on one microelectrode.

3.2.3. Spike intervals
Spike intervals were then classified. Figure 6 shows the histograms
that display the number of spikes according to their time intervals
on one electrode at two temperatures.

4. DISCUSSION

3.2.4. Clustering
Finally, Principal Component Analysis (PCA) was used for parameters extraction, such as the amplitude or spike width, and data
dimension reduction. In this analysis, each spike became a point
ain a three-dimension space. As a result, spikes were divided into
clusters in such a way that the spikes in the same cluster are moresimilar to each other than to those in other clusters (Figure 7).

b

Figure 4 | (a) Raw data and (b) filtered data within 10 s of measurement.

abAnalysis of electrophysiological data with a spike sorting program
allowed the extraction of useful information about spontaneous bioelectrical signals in cardiomyocyte networks recorded by TFT sensor
arrays. The shape of the extracellular potentials is similar to what
we can obtain with other Micro Electrode Arrays (MEA). Decreaseof the beating rate with temperature (from +37°C to room temperature) was observed optically during the data acquisition witha system from Multi Channel Systems (MCS) (GmbH, Reutlingen,Germany). This observation was confirmed by the program with adecrease rate of around −6.5 beats per minute/°C. A possible explanation would be that the temperature drop depresses the speed ofion exchange, as it increases the permeability of the membrane toions. A modification of the signal peak-to-peak voltage amplitude
was also observed as a result of decrease in temperature, with a shift
from ±300 to ±200 µV. The program could also successfully distinguish spikes and divide them into different clusters. A spike rasterplot available in the program (not shown here) also revealed thespike synchronicity of extracellular signals recorded on each microelectrode. This observation confirmed the synchronicity of the bioelectrical conduction among the network of cardiomyocytes, which
was also observed experimentally under microscope.

5. CONCLUSION
Figure 5 | (a) Filtered data (blue) and threshold lines (red) for spike
detection, and (b) spike alignment.

abFigure 6 | (a) Histogram of spike intervals at +37°C and (b) room
temperature (RT). In this study, spike intervals went from 200 ms (5.0 Hz)
to 270 ms (3.7 Hz).

In this paper, a spike sorting algorithm for electrophysiological dataprocessing was successfully used for analysis of spontaneous electrical signals generated by a network of cardiomyocytes measured
by TFT sensor arrays. As this program was written for future operation with a FPGA, a closed-loop for automatic cell stimulation withreal-time analysis could be implemented. Combining this techniquewith deep-learning algorithms [5] could conjointly allow in vitro
identification of abnormal functional behaviors of cardiac biosignaling networks and provide a powerful tool for drug screening.

CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

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REFERENCES[1] D. Farwell, M.H. Gollob, Electrical heart disease: genetic andmolecular basis of cardiac arrhythmias in normal structuralhearts, Can. J. Cardiol. 23 (2007), 16A–22A.
[2] F.A. Shaik, Y. Ikeuchi, G. Cathcart, S. Ihida, H. Toshiyoshi,
A. Tixier-Mita, Extracellular neural stimulation and recording with a Thin-Film-Transistor (TFT) array device, 2017 19thInternational Conference on Solid-State Sensors, Actuators andMicrosystems (TRANSDUCERS), IEEE, Kaohsiung, Taiwan,
2017, pp. 206–209.

51[3] A. Tixier-Mita, S. Ihida, B.D. Ségard, G.A. Cathcart, T. Takahashi,
H. Fujita, et al., Review on thin-film transistor technology, itsapplications, and possible new applications to biological cells,
Jpn. J. Appl. Phys. 55 (2016), 04EA08.
[4] J.F. Beche, S. Bonnet, T. Levi, R. Escola, A. Noca, G. Charvet,
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[5] A. Mincholé, B. Rodriguez, Artificial intelligence for the electrocardiogram, Nat. Med. 25 (2019), 22–23.

AUTHORS INTRODUCTION
Ms. Anne-Claire Eiler

Dr. Katsuhito Fujiu

She received a M.Eng. in Biosciences andM.S. degree in Material Sciences fromUniversity Paris XII, France, in 2014. Since
2018, she is pursuing the PhD degree in
Advanced Interdisciplinary Studies withthe RCAST at The University of Tokyo,
Japan. Her research interests include BioMicrosystems and Bioelectricity.
Dr. Junichi Sugita

He received the PhD degree from The
University of Tokyo, Japan, 2005. He is an
Associate Professor at the Department ofAdvanced Cardiology of The Universityof Tokyo. His current research interests
are including cardiac hemostasis by inter-­cellular and inter-organ communications.

Dr. Timothée Levi
He received the PhD degree in Internal
Medicine from The University of Tokyo,Japan, in 2015. He is a postdoctoral fellow
at Department of Cardiovascular Medicineof The University of Tokyo Hospital. Hiscurrent research interests include cardiac
arrhythmia and its treatment especially byusing brain-machine interface.

He received the PhD degree in Electronics
at The University of Bordeaux, France, in2007. He is an Associate Professor at IMSlaboratory of The University of Bordeaux,since 2009, and a Project Associate
Professor at The University of Tokyo since2017. His research interest deals with neuromorphic engineering for bio-hybrid experiments.

Mr. Satoshi Ihida

Dr. Agnès Tixier-Mita

He received his B.E. degree from theDepartment of Metallurgy, The Universityof Tokyo, Japan, in 1990. Since 2003, hehas been working on liquid crystal inSharp Corporation, Japan, and joined
the Institute of Industrial Science, The
University of Tokyo, as a Private Sector
Collaborative Researcher from Sharp Corporation in 2012. His
research interests include thin film transistors and their application in MEMS.

She received the PhD degree at theUniversity of Lille I, France, in 1998.
She has been affiliated to the RCAST
and the IIS at The University of Tokyo,
as an Associate Professor since 2010.
Her research interests include integratedsensor-­array devices for biological applications based on CMOS and TFT technologies.

Prof. Hiroshi Toshiyoshi
He received the PhD degree in ElectricalEngineering from The University of Tokyo,
Japan, in 1996. He joined the Institute ofIndustrial Science (IIS), The University ofTokyo in 1996 and has been a Professor
with the IIS since 2009. His research interests include optical and RF-MEMS.