Slantlet Transform-Based ECG Classification System with Efficient Design and Implementation - A. Alwan, Majid; M. Jasim, Abdulhamed; Khalid Abdullah, Anas; M. Abdul-Jabbar, Jassim (Iraqi Journal for Electrical and Electronic Engineering, 2026)
Since cardiac conditions are among the most fatal illnesses in the medical community, ECG classification systems arecrucial for understanding and diagnosing patients’ health conditions. Numerous techniques for ECG feature extractionand classification algorithms are developed by researchers. This paper presents a method for accurately classifying ECGillnesses based on the 3-scale Slantlet transform (SLT) and artificial neural network (ANN). The ability of the SLT filtersto decompose the ECG signal at various resolutions led to excellent classification. As a new realization, all coefficients ofthe modified designed SLT filters are expressed by the sum-of-power-of-two (SOPOT) approach to reduce the complexity.It is noteworthy that the average and maximum deviation error values between the responses of original and modifiedfilters are very small. Hardwarely, the new realization leads to a less complex implementation for the designed SLTfilters on FPGA kit using the Xilinx System Generator for DSP with very small errors between output resposes of theoriginal and modified filters. FPGA results show that the system is designed using a best-selected wordlength method.The proposed classification system is capable of distinguishing the ECG normal case and other four different diseaseswith a high overall accuracy of 98.50 %.