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As seismic data availability increases, the necessity for automated processing techniques has become increasingly evident. Expanded geophysical datasets collected over the past several decades across Antarctica provide excellent resources to evaluate different event detection approaches. We have used the traditional Short-Term Average/Long-Term Average (STA/LTA) algorithm to catalogue seismic data recorded by 19 stations in East Antarctica between 2012 and 2015. However, the complexities of the East Antarctic dataset, including low magnitude earthquakes and other types of seismic events such as icequakes or firnquakes, warrant more advanced automated detection techniques. Therefore, we have also applied template matching as well as several deep learning algorithms, including Generalized Phase Detection (GPD), PhaseNet, BasicPhaseAE, and EQTransformer (EQT), to identify seismic phases within our dataset. Our goal is not only to increase the volume of detectable seismic events but also to gain insights into the effectiveness of these different automated approaches. Our assessment evaluates the completeness of the newly generated catalogs, the precision of identified event locations, and the quality of the picks. The performance of these different event detection techniques applied to continuous seismic data from a polar environment will be highlighted. We will also identify potential limitations and necessary adjustments for deep learning algorithm training, which is essential for their reliable application to specific datasets.