The diagnosis and monitoring of cardiovascular disease relies heavily on electrocardiogram (ECG) signal analysis. Basic interpretation may be achieved using classic time series analysis techniques, but these methods do not always capture the intricate temporal patterns and long-term correlations seen in ECG data. This study investigates the feasibility of using machine learning and deep learning techniques to enhance ECG-based time series analysis. By employing designs such as LSTM networks we aim to improve the accuracy and robustness of ECG interpretation.