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Empirical mode decomposition (EMD) is a data driven method for nonstationary data analysis which has proven to be extremely useful in diverse applications in biomedical engineering. In its original formulation, the EMD method works collectively on a batch of data and hence is not suitable for online applications that demand continuous data processing capability in real-time. To cater for such applications, few complete field-programmable gate array (FPGA) based designs for EMD computations have emerged in recent years, yet they have found limited practical applications. The main reason could be that existing complete FPGA based architectures invariably work on integer data sets thus ignoring the sensitivity EMD to truncation errors. Moreover, these architectures mostly implement simplistic linear interpolation technique for sifting process within EMD, thereby compromising its accuracy. To that end, we develop a complete FPGA-based architecture for EMD which works on fixed point data formats thereby alleviating the main source of error in existing EMD based architectures. We also implement well established and accurate cubic spline interpolation technique in addition to giving provision to linear interpolation within the sifting process in EMD, which further improves its accuracy. Our proposed pipeline design ensures that despite implementing computationally expensive EMD related tasks, the proposed architecture exhibits lowest execution time among all existing EMD architectures. We provide examples of computation of EMD decomposition through proposed architecture for both synthetic and real world biomedical signals to demonstrate the prowess of our work.

FPGA Based Real-Time Implementation of Online EMD With Fixed Point Architecture