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In order to realize the on-line real-time processing of portable signal binary classification analysis system, the embedded transplantation of CiSSA-DSP feature extraction algorithm is implemented on DSP platform. The CiSSA-CSP feature extraction algorithm has excellent performance of time-frequency-spatial feature extraction, and is suitable for extracting non-stationary signals in real-time binary classification system. Compared with PC, embedded implementation has the characteristics of miniaturization, portability, low power consumption and low delay, while the computing resources and memory of embedded platform processor is limited, and the algorithm must be optimized to ensure the binary classification accuracy and low delay. By optimizing the flow of CiSSA-CSP algorithm, using compiler optimization, keywords and library functions to improve the compilation efficiency, CiSSA-CSP feature extraction algorithm is transplanted to the TMS320C6678 embedded DSP platform, and its effectiveness for real-time classification system is verified by public dataset. Compared with the Matlab on PC, the classification accuracy of the binary classification system implemented by DSP platform is reduced by less than 0.5% and the computing time is less than 0.15s.