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. The sleep physiological signal feature extraction method comprises the following steps: a sleep physiological signal is collected, and a multi-guide time sequence of the sleep physiological signal is converted into tensor representation, wherein the sleep physiological signal is expressed as an N-order tensor, and the N-order tensor is composed of N-order sub-tensors; and the difference between the elements in each sub-tensor and the elements in the global sub-tensor is judgedto determine the approximate entropy of the N-order tensor, the tensor approximate entropy is used as the feature of sleep physiological signal extraction, the extracted feature can accurately reflectthe internal feature of the sleep physiological signal data, and then the sleep staging is more accurate in the subsequent classification processing.

Description

technical field [0001] The invention belongs to the technical field of physiological signal feature extraction, in particular to a sleep physiological signal feature extraction method and system based on tensor complexity. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may 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 the physiologic...

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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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