Advances in cardiac signal processing

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High-performance computing has significantly benefited various disciplines, including health care, by providing practical solutions to complex problems. In this context, signal processing and data mining tools have been developed to enhance computational capabilities, assisting clinicians with diagnosis and treatment. The electrocardiogram (ECG) serves as a critical signal that conveys information about heart health, with the shape and size of the P-QRS-T wave and the time intervals between peaks revealing insights into cardiac conditions. However, human observers often struggle to monitor these subtle details effectively. Additionally, biosignals are inherently subjective, with symptoms appearing randomly over time. Cardiac abnormalities typically manifest in the ECG waveform and heart rate, but due to the random nature of biosignals, these manifestations may not be consistent, occurring at irregular intervals throughout the day. Consequently, analyzing ECG patterns and heart rate variability requires extended monitoring periods, such as 24 hours, resulting in a massive volume of data that is both tedious and time-consuming to study. This complexity increases the risk of analysts missing or misreading critical information.