Sleep physiological signal feature extraction method and system based on tensor complexity

Abstract

The invention discloses a sleep physiological signal feature extraction method and system based on tensor complexity, comprising: collecting the sleep physiological signal and converting the polyconductive time series of the sleep physiological signal into a tensor representation; the sleep physiological signal is expressed as an N-order tensor , the N-order tensor is composed of N-order sub-tensors; judge the difference between the elements in each sub-tensor and each element in the global sub-tensor to determine the size of the N-order tensor approximate entropy, and use the tensor approximate entropy as sleep physiology The features of the signal extraction, the extracted features can accurately reflect the internal features of the sleep physiological signal data, and then achieve more accurate sleep staging in the subsequent classification processing.

Description

technical field [0001] The invention belongs to the technical field of physiological signal feature extraction, and in particular relates to a method and system for sleep physiological signal feature extraction based on tensor complexity. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The tensor representation of data and the corresponding data processing in tensor format, such as tensor decomposition, provide a new solution for biomedical signal processing, and have been successfully applied in the field of EEG. [0004] Processing signals in the form of tensors has its own unique advantages. First of all, almost all biomedical signals are multidimensional, such as 32-lead sleep EEG signals or 12-lead sleep ECG signals, which are the physiological manifestations of the same signal source at different positions on the body surface, and ...

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Problems solved by technology

[0006] However, the inventor found in the research that in the current sleep staging research, more methods are based on feature extraction of single-lead or multi-lead physiological signals, and there are few sleep staging methods based on tensor data processing. Several sleep staging methods based on tensor data processing mainly apply tensor decomposition method, and there is no literature on sleep staging by extracting the complexity of tensor data

Moreover, the current literature also lacks a method that can accurately evaluate the complexity of tensor data

[0007] In short, in many tensor data processing schemes, there is no entropy method to evaluate the complexity of tensor data, and there is no extraction of physiological signals using the complexity of tensor data. Therefore, because the internal structure of the tensor is not considered In the complex situation, the current feature extraction accuracy of physiological signals is low, which will bring certain errors in the later classification and other applications.

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