Electrocardiographic (ECG) signals have been used for clinical purposes for a long time. Notwithstanding, they may also be used as the input for a biometric identification system. Several studies, as well as some prototypes, are already based on this principle. One of the methods already used for biometric identification relies on a measure of similarity based on the Kolmogorov Complexity, called the Normalized Relative Compression (NRC)—this approach evaluates the similarity between two ECG segments without the need to delineate the signal wave. This methodology is the basis of the present work. We have collected a dataset of ECG signals from twenty participants on two different sessions, making use of three different kits simultaneously—one of them using dry electrodes, placed on their fingers; the other two using wet sensors placed on their wrists and chests. The aim of this work was to study the influence of the ECG protocol collection, regarding the biometric identification system’s performance. Several variables in the data acquisition are not controllable, so some of them will be inspected to understand their influence in the system. Movement, data collection point, time interval between train and test datasets and ECG segment duration are examples of variables that may affect the system, and they are studied in this paper. Through this study, it was concluded that this biometric identification system needs at least 10 s of data to guarantee that the system learns the essential information. It was also observed that “off-the-person” data acquisition led to a better performance over time, when compared to “on-the-person” places.

In this work, we present an approach for the automatic classification of political topics in the context of TV-broadcasted political debates. The full recordings of five Republican Party political debates were subjected to a pipeline involving automatic speech recognition and speaker diarization. The output chunks were then automatically classified based on a set of predefined political topics, according to (1) natural language processing (NLP, using mDeBERTa) and (2) large language models (LLMs, using GPT-4o, llama3-8b and llama 3-70b). The performance of the models was compared against manual classification. The results demonstrated that GPT-4o had the highest accuracy (69%) followed by llama3-70b (67%), llama3-8b (61%) and mDeBERTa (43%). Models’ accuracy further improved when considering secondary manual classifications from the human coders (GPT-4o: 75%, llama3-70: 74%, llama3-8b: 69%, mDeBERTa: 43%). This research demonstrates the viability of automated text classification, based on LLMs to summarize political debates.