Multi-lead physiological signal analysis method and device

Abstract

The invention relates to a human body monitoring technology, in particular to a multi-lead physiological signal analysis method and device, which are used for reducing the analysis complexity of multi-lead physiological signals and reducing the hardware implementation cost. The method comprises the following steps of: converting the multi-lead physiological signal of the monitored object into two-dimensional time-frequency domain information from one-dimensional time domain information; since the two-dimensional time-frequency domain information can be regarded as a 'image-like', depth featuredata extraction and analysis processing can be carried out on the data frame by adopting an image algorithm in a subsequent process. Therefore, the current physiological state of the monitored objectis accurately determined, the analysis efficiency of physiological signals is improved, the early-stage working pressure of doctors can be reduced, the working efficiency of hospitals is improved, meanwhile, the feature extraction process can be simplified, the hardware implementation cost is reduced, and algorithm popularization is facilitated.

Description

technical field [0001] The invention relates to human body monitoring technology, in particular to an analysis method and device for multi-lead physiological signals. Background technique [0002] In clinical medicine, it is usually necessary to monitor the patient's physiological signals, and then the doctor will judge the patient's health status based on the obtained monitoring data and give targeted treatment advice. [0003] Take sleep disorder diagnosis, for example. According to the International Classification of Sleep Disorders (ISCDII, 2005), there are more than 90 sleep-related diseases, including insomnia, sleep-disordered breathing, narcolepsy and idiopathic hypersomnia, restless legs syndrome, periodic limb movement disorder and parasomnia etc. [0004] Multi-lead sleep monitoring is an important technology in today's sleep medicine. The continuous breathing, arterial blood oxygen saturation, EEG, ECG, heart rate and other indicators of patients during night s...

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Problems solved by technology

[0007] However, in the above solutions, the feature vector extraction method is too complicated, and the analysis efficiency is low, and the vector process of feature extraction also requires the operator to have certain prior knowledge in the field of sleep analysis; at the same time, it also needs to use high-configuration hardware Perform data analysis, which greatly increases the cost of implementation, and the generalizability is not high enough

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