Change Detection in Electrocardiogram (ECG) Signals After Postoperative Pain Management

Postoperative pain is a subjective experience, typically assessed through self-report scales, such as the numerical rating scale. When self-reporting is not possible, healthcare providers often rely on clinical observations. However, due to its subjective nature, pain assessment remains challenging, leading to increased interest in objective methods to support assessment in clinical settings, including the use of physiological signals such as the Electrocardiogram (ECG). This study aimed to develop a method for detecting changes in physiological signals over time based on symbolic theory and compression analysis. The proposed method compares ECG segments by first transforming ECG intervals into a symbolic representation using the Symbolic Aggregate approXimation (SAX) algorithm adapted to the ECG probability distribution of the subject. Afterward, the similarity between SAX segments is assessed using the Normalized Compression Distance (NCD), a data compression metric related to the approximation of the Kolmogorov complexity. To identify changes in the ECG signal over time, the method employed two strategies: determining the similarity between each SAX segment either with the subject's baseline SAX segment or with the preceding SAX segment. This method was applied to ECG signals collected in the recovery room after surgery, focusing specifically on intervals related to postoperative pain assessment and subsequent analgesic treatment. Although no clear association was found between variations in similarity and pain treatment, the method successfully detected changes in the ECG over time relative to the patient's baseline.