| dc.description.abstract | The most common signals found in nerve signals are isolated spikes. However, biologists have detected signals other than spikes in the thalamus. These are known as bursts. This paper will mainly be examining the characteristics of bursts. Moreover, the data found will be quantified in order to automatically detect and categorize bursts.
First, we will introduce how nerve cells generate action potentials and the background information of nerve signals. Next, according to the bursts selected by a biologist through experience, we will generalize the characteristics and establish three screening conditions. These three screening conditions are as follows: associated with time, the gap condition, associated with amplitude, the decay condition, and associated with waveforms, the shape condition. With these conditions, we will then apply them on to a set of raw data to detect bursts. Before detecting the signals, we will first process the raw data. This includes down sampling and filtering. After processing the raw data, we will automatically detect the bursts using the three screening conditions mentioned above. In addition, using the Principal Component Analysis (PCA), we will then classify the bursts.
Testing with the bursts that the biologist has selected based on his experience, the results collected through the filtering is confirmed. Moreover, the bursts detected from the raw data using the filtering criteria also pass the test. Therefore, we can see that the filtering criteria can reflect the characteristics of bursts and effectively detect them.
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