Wavelet filtering before spike detection preserves waveform shape and enhances single-unit discrimination

Abstract

The isolation of single units in extracellular recordings involves filtering. Removing lower frequencies allows a constant threshold to be applied in order to identify and extract action potential events. However, standard methods such as Butterworth bandpass filtering perform this frequency excision at a cost of grossly distorting waveform shapes. Here, we apply wavelet decomposition and reconstruction as a filter for electrophysiology data and demonstrate its ability to better preserve spike shape. For the majority of cells, this approach also improves spike signal-to-noise ratio (SNR) and increases cluster discrimination. Additionally, the described technique is fast enough to be applied real-time.

Introduction

The removal of unwanted frequencies and artifacts is essential in the analysis of electrophysiological data, especially where the data of interest are the time stamps of neuron action potentials, also called “spikes.” In addition to high amplitude spikes, typical electrode signals include local field potentials (LFPs), instrument noise, and spikes from neurons too distant from the recording site to be effectively discriminated. An ideal filtering technique would preserve only discriminable spikes without distorting their waveforms, since differences in waveform shape are useful in clustering and also provide an important source of information about neuronal phenotypes (e.g. Csicsvari et al., 1998, Barthó et al., 2004, Berke et al., 2004). Filters commonly used in electrophysiology, such as the Butterworth filter, can be fast to compute and possess a maximally flat frequency response (Butterworth, 1930). However, they possess the undesirable side-effect of distorting the time-domain, e.g. the shape of action potentials.

After the data are filtered, they are usually thresholded to locate spike events, and then certain features of the extracted spikes are used in a manual or semi-automated clustering procedure (Lewicki, 1998). The features to be used in clustering can include spike amplitude, valley width, principal components or wavelet decomposition coefficients. Wavelets have recently gained notice as a powerful tool for signal analysis in the neurosciences and have been applied in a myriad of ways, including spike detection (Hulata et al., 2002, Nenadic and Burdick, 2005), cell classification (Cesar and Costa, 1998, Letelier and Weber, 2000, Quiroga and Garcia, 2003, Quiroga et al., 2004) and EEG/LFP analysis (Clarençon et al., 1996, Adeli et al., 2003, Markazi et al., 2006, Berke et al., 2008). Here, we apply wavelet filtering to “raw” (wide-band) electrode signals as a preprocessing stage before spike detection and sorting. This approach accurately maintains waveform shape while removing low frequency field potentials and noise artifacts. We demonstrate benefits for the later stages of spike discrimination, compared to the standard Butterworth bandpass filter.