Leveraging Liquid Time-Constant Neural Networks for ECG Classification: A Focus on Pre-Processing Techniques

Neural networks have become pivotal in timeseries classification due to their ability to capture complex temporal relationships. This paper presents an evaluation of Liquid Time-Constant Neural Networks (LTCs), a novel approach inspired by recurrent neural networks (RNNs) that introduces a unique mechanism to adap-
tively manage temporal dynamics through time-constant parameters. Specifically, we explore the applicability and effectiveness of LTC in the context of classifying myocardial infarctions in electrocardiogram data by benchmarking the performance of LTCs against RNN and LSTM models utilzing the PTB-XL dataset. Moreover, our study focuses on analyzing the impact of various pre-processing methods, including baseline wander removal, Fourier transformation, Butterworth filtering, and a novel x-scaling method, on the efficacy of these models. The findings provide insights into the strengths and limitations of LTCs, enhancing the understanding of their applicability in multivariate time series classification tasks.