Improved Action Potential Detection for Imaging Techniques by exploiting Fuzzy C-Means Clustering

In recent years, observing neuronal activity by inferring action potentials (APs) in mammals has attracted much attention. A common way to observe thousands of neurons simultaneously is by using calcium imaging techniques. However, estimating the APs based on the fluorescence signal obtained by the calcium imaging technique is a challenging task due to noise, slow imaging rates and especially the nonlinearity of the calcium binding kinetics. Though the electrical recording technique can measure the APs very precisely, it is rather time consuming in practice. In this paper, an approach is proposed that reconstruct the APs based on the noisy fluorescence signal. For this purpose, at first the forward-backward filtering is applied on the fluorescence signal to reduce the level of noise and to avoid the nonlinear shift in time with respect to the true fluorescence signal. Then, for each local maximum in the filtered fluorescence signal, three characteristic values, namely, the integral, the amplitude and the gradient are extracted to localize the neuronal activity. By exploiting the fuzzy c-means clustering method, the time instants and the number of APs can be estimated. The proposed approach is validated by using the well-established spikefinder challenge data. The comparison shows that the proposed approach outperforms other existing AP estimation approaches.