Neuronal cell signal analysis: spike detection algorithm development for microelectrode array recordings

Abstract

Neural signal acquisition and processing techniques are rising trends among wide scientific and commercial areas. Microelectrode array (MEA) technology makes it possible to access and record the electrical activity of neural cells.

In this work, human pluripotent stem cell (hPSC) -derived neuronal populations were grown on MEA plates. The activity of the cells was recorded and the research about modern signal processing methods for the neural spike detection was performed. A list of approaches was selected for detailed investigation and the most efficient one was chosen as the new technique for permanent use in the research group.

The performed laboratory activities involved cell culture plating, regular medium changes, spontaneous activity recordings and pharmacological manipulations. The data acquired from pharmacological experiments were used for the comparison between the old and new spike detection algorithms in terms of the numbers of the detected events.

The Stationary Wavelet Transform-based Teager Energy Operator (SWTTEO) shows prominent performance in the tests with synthetic data. The use of the proposed algorithm in conjunction with the common amplitude-based thresholding enables to lower the threshold and to detect more spikes without an excessive number of false positives. This mode is applicable for real cell data.

The detection method was considered superior and was further distributed for the processing of all neural data of the research group which include signals acquired from neuronal populations derived from human embryonic and induced pluripotent stem cells (hESCs and iPSCs) as well as rat cells.