A multi-source electrocardio signal correction method and system based on adaptive fusion

Abstract

The present application relates to the technical field of data fusion, in particular to a multi-source electrocardio signal correction method and system based on adaptive fusion, comprising the following steps: constructing a multi-channel input tensor, extracting local features by using a weight calculation network, adaptively weighting and fusing and reducing dimensions of the multi-channel signals, establishing a nonlinear mapping relationship by using a deep reconstruction network, reconstructing a standard waveform, and iteratively optimizing network parameters based on reconstruction error. In the present application, local neighborhood features of multi-channel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting real-time contribution of signal sources is constructed, amplitude intensity is adaptively adjusted according to signal quality, unstable channel noise interference is effectively suppressed and high-quality signal components are enhanced, a nonlinear feature transformation of the fused sequence is performed by using a deep reconstruction network, an accurate mapping from non-standard input to standard lead waveform is established, and weight distribution and signal reconstruction parameter optimization are realized in combination with an error back propagation mechanism.

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