Research On Sleep Staging Algorithm Based On Hidden Markov Model Processing

Traditional sleep staging is researched by collecting brain electrical signals,and monitoring human physiological information through patch-type components.This direct contact with the human body will affect the normal sleep state.In recent years,the use of various near-body devices has enriched the ways of collecting human body signals,and piezoelectric sensing mattresses can minimize the impact of sleep monitoring on sleep.The piezoelectric sensor placed in the mattress is used to collect heart shocks.Figure(Ballistocardiogram,BCG)signal to complete the subsequent staging work.Hidden Markov Model(HMM)is a statistical analysis model that can be used for sequence data processing.With the progress of its related theories,it has been widely used in pattern recognition,computer vision,fault detection and other fields in recent years.Combining these issues,this paper proposes a sleep staging algorithm based on hidden Markov model processing.The content of the research process is as follows:1.Signal source analysis: Investigate and analyze the signal source that is suitable for the research.Select the non-contact type among the electroencephalogram(Electroencephalogram,EEG)signal,the electrocardiogram(ECG)signal and the BCG signal to minimize the interference to the normal signal acquisition.The contactless signal source BCG.2.Research on staging algorithm: Explore the sleep staging methods used by predecessors,and provide ideas for the proposed algorithm of this article through their different attempts and practices for EEG signals and ECG signals.3.The sleep staging algorithm proposed in this paper uses the different characteristics of the heartbeat and breathing signals to calculate and separate them from the BCG signal,and then uses the correlation between the variability of the heart rate and respiration rate and different sleep periods to realize the sleep stage discrimination.The heart rate time series uses Time-Variant Autoregressive Model(TVAR)to process and construct a power spectral density map,extract time and frequency characteristics,and use them as input values to establish a hidden Markov model for the corresponding staging to meet the requirements of sleep staging.By comparing the sleep stages marked by experts,the accuracy of the algorithm in this paper has reached 78.4%,which verifies the accuracy of the hidden Markov model in identifying different patterns in signal sources,and can be used in real sleep monitoring projects.This sleep staging algorithm combined with a non-contact sleep monitoring solution is very suitable for use in homes,hospitals and various health care institutions.It can help build an intelligent monitoring system for scientific sleep,and provide product improvement and upgrade services for the sleep-related smart home industry.Supported by other data,there are good development prospects.