Real‐time signal processing for high‐density microelectrode array systems

Imfeld, K.; Maccione, Alessandro; Gandolfo, Mauro; Martinoia, Sergio; Farine, Pierre-André; Koudelka‐Hep, M.; Berdondini, Luca

doi:10.1002/acs.1077

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…Their efficient on‐line handling is guaranteed by the specific properties of the integrated circuit in terms of signal identification and computed final data output at a defined significance level. Although similar devices have been presented previously (Imfeld et al 2009; Jones et al 2011), they only worked off‐line and on considerably larger signals such as those recorded here. During the submission of our manuscript a similar electrophysiological approach was reported by Pfeiffer et al recording the effect of glucose on islets positioned on only one microelectrode of a multi‐array and using the fraction of plateau phase of the sustained bursting phase as the signal read‐out (Pfeiffer et al 2011).…”

Section: Discussionmentioning

confidence: 93%

Non‐invasive long‐term and real‐time analysis of endocrine cells on micro‐electrode arrays

Raoux

Bornat

Quotb

et al. 2012

The Journal of Physiology

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Key points• Pancreatic islet cells are required for glucose homeostasis and their dysfunction leads to diabetes mellitus.• The electrical activity of these cells is regulated by nutrients as well as hormones and this provides the first integrative read-out reflecting their function.• Non-invasive recording of their electrical activity and its analysis in real time would provide the possibility of long-term and repetitive investigations or testing of multiple drugs.• Combining electrophysiology and microelectronics, we now demonstrate such an approach with multiple (up to 60) electrodes and including on-line signal analysis on the two major cell types present in islets, α-and β-cells.• This method should be applicable to other endocrine cells and may serve in the long-run as the basis for a novel biosensor.Abstract Non-invasive high-throughput and long-term monitoring of endocrine cells is important for drug research, phenotyping, tissue engineering and pre-transplantation quality control. Here we report a novel approach to obtain simultaneous long-term electrical recordings of different islet cell types using multi-electrode arrays. We implemented wavelet transforms to resolve the low signal/noise ratio inherent to these measurements and extracted on-line a signature specific of cell activity. The architecture employed allows multiplexing a large number of electrodes for high-throughput screening. This method should be of considerable advantage in endocrine research and may be extended to other excitable cells previously not accessible to the technique. Abbreviations ASIC, Application-specific integrated circuit; FPGA, field-programmable gate array; MEA, multi-electrode array; SWT, stationary wavelet transform.

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Section: Discussionmentioning

confidence: 93%

Non‐invasive long‐term and real‐time analysis of endocrine cells on micro‐electrode arrays

Raoux

Bornat

Quotb

et al. 2012

The Journal of Physiology

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“…The coefficients obtained are the result of subsequent high- and low-pass filtering of the spike waveform at j different scales (i.e., filter bandwidths). The spike waveforms can then be described with a vector v :

where a − j is the output of the low-pass filter (approximation coefficients) at the j th scale and d − j , d −( j −1) ,…, d −1 are the outputs of the high-pass filter at each scale (detail coefficients) [ 40 ]. In this work, Haar wavelet has been chosen as the default wavelet option, thanks to its computational efficiency and similarity to biphasic spike shapes [ 5 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Regalia

Coelli

Biffi

et al. 2016

Computational Intelligence and Neuroscience

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Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

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“…It can be highly efficient when combined with continuous noise estimation. It can be realized in real-time during acquisition even on large arrays (Maccione et al, 2009 ) and implemented in hardware, for instance using wavelet based compression and feature extraction (Imfeld et al, 2009 ). Once detected, putative spikes are then clustered according to spike shape parameters to separate multiple neurons recorded by the same channel and to exclude false positives (Lewicki, 1998 ; Einevoll et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

Muthmann

Amin

Sernagor

et al. 2015

Front. Neuroinform.

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An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

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Real‐time signal processing for high‐density microelectrode array systems

Cited by 10 publications

References 41 publications

Non‐invasive long‐term and real‐time analysis of endocrine cells on micro‐electrode arrays

Non‐invasive long‐term and real‐time analysis of endocrine cells on micro‐electrode arrays

A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

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