CN115989998A - Method for detecting sleep stage of parkinsonism patient - Google Patents
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Abstract
The invention relates to a sleep detection method, in particular to a method for detecting sleep stages of a patient suffering from parkinsonism, which comprises the following steps: the method comprises the steps of collecting and determining a frequency sub-band B in which energy is concentrated in an electroencephalogram signal of a patient, dividing the electroencephalogram signal of the patient according to set time, detecting once when the set time is reached, calculating power P1 of the collected signal in a frequency band B, calculating power P2 of the collected signal in a frequency band Delta, setting thresholds thr1 and thr2, and simultaneously meeting the requirements of P1< thr1 and P2> thr2, detecting that the patient enters a sleep state, setting a corresponding threshold again according to power change in the Delta frequency band, taking a value of the set threshold in the power of the frequency band Delta in a awake state, and determining the sleep stage of the patient according to the fact that the power in the Delta frequency band is in a corresponding threshold interval. According to the invention, the sleep state and sleep stage of the patient are determined according to the frequency characteristics of the electroencephalogram signals of the patient suffering from Parkinson's disease, the influence of stimulus on the sleep quality of the patient is reduced, and the DBS treatment effect is improved.
Description
Technical Field
The invention relates to a sleep detection method, in particular to a method for detecting sleep stages of a patient suffering from parkinsonism.
Background
Parkinson's disease (Parkinson Disease, PD) is a common neurological disorder, the main features of which include dopaminergic neuronal death in specific areas of the substantia nigra and α -synuclein aggregation within neurons. Parkinson's disease is not common under age 50 and its incidence increases with age. The probability of parkinsonism of men is higher than that of women, and the ratio of men and women suffering from parkinsonism in the world is about 1.4:1. Most cases of parkinson's disease are idiopathic, but also have genetic and environmental effects, and studies have shown that exposure to pesticides, herbicides and heavy metals can lead to increased incidence of disease, while smoking and consumption of caffeine can lead to decreased incidence of disease.
The seizure symptoms of parkinson's disease include motor symptoms and non-motor symptoms, and motor symptoms mainly include: resting tremor, stiffness of the limbs, slow movement, body imbalance, etc. The non-motor symptoms mainly include: cognitive disorders, sleep disorders, depression, olfactory loss, urinary dysfunction, and the like.
Loss of dopaminergic neurons in the substantia nigra pars compacta results in loss of striatal dopamine as a core mechanism of the primary motor features of parkinson's disease. Thus, supplementing dopamine with drugs is the primary treatment for parkinson's disease, and therapeutic drugs mainly include levodopa, dopamine receptor agonists and monoamine oxidase b inhibitors, but drugs often have certain side effects and have no effect on certain patients.
Deep brain stimulation (Deep Brain Stimulation, DBS) is a novel and effective treatment for drug-refractory parkinson's disease. DBS was first shown in 1993 to achieve a therapeutic effect by applying high frequency electrical stimulation to the subthalamic nucleus or internal pallidum via an implantable pulse generator (Implantable Pulse Generator, IPG). Although the mechanism of DBS treatment of parkinson's disease is not completely understood, it is effective to improve parkinsonian motor symptoms as long as the stimulation parameters are set reasonably.
DBS may affect the sleep quality of a patient. The sleep stages of a human include stage 1, stage 2, deep sleep stage, also known as slow wave sleep stage, and fast eye movement (Rapid Eye Movement, REM) stage, which can also be subdivided into stage 3 and stage 4. Stage 1, stage 2, and deep sleep stages are collectively referred to as the non-rapid eye movement (non-Rapid Eye Movement, NREM) stage. In a complete sleep, a human may go through stage 1, stage 2, deep sleep stage, and rapid eye movement stage more than once. DBS may cause parkinsonism patients to fail to enter or maintain deep sleep stages for a long period of time, affecting patient sleep quality, which may exacerbate patient symptoms, worsen conditions, e.g., movement disorder in dyskinesia patients, and depression in depressed patients.
Disclosure of Invention
The invention aims to solve the defects and provides a method for detecting sleep stages of a patient suffering from Parkinson's disease, and the sleep state and the sleep stages of the patient are determined according to the frequency characteristics of the brain electrical signals of the patient suffering from Parkinson's disease.
In order to overcome the defects in the background art, the technical scheme adopted by the invention for solving the technical problems is as follows: a method of detecting sleep stages in a parkinson's disease patient, comprising the steps of:
s1: before a patient falls asleep, collecting and determining a frequency sub-band B in which energy is concentrated in an electroencephalogram signal of the patient;
s2: the method comprises the steps that a patient continuously collects brain electrical signals of the patient in the whole period from before to after falling asleep, and meanwhile, the collected brain electrical signals of the patient are divided according to set time to achieve one-time detection for the set time;
s3: calculating the power P1 of the picked-up signal in the frequency band B;
s4: calculating the power P2 of the acquired signal in the frequency band Delta;
s5: when thresholds thr1 and thr2 are set while satisfying P1< thr1 and P2> thr2, then the patient is detected to enter a sleep state.
S6: and setting a corresponding threshold again according to the power change in the Delta frequency band, and taking the value of the set threshold by the power of the frequency band Delta in the awake state, wherein the power in the Delta frequency band is in a corresponding threshold interval, so that the sleep stage of the patient is determined.
Further improvement comprises that in the step S2, the set time is divided into one minute or five minutes. .
Further improvement comprises in step S5, the threshold thr1 is set to a value according to the power in the frequency band B, and the threshold thr2 is set to a value according to the power in the frequency band Delta.
The beneficial effects of the invention are as follows: the method has the advantages that once the sleep stage of the patient is accurately detected, the treatment scheme of the patient can be adjusted according to the sleep stage, for example, in a DBS system, if the patient is detected to be in an awake state or a first sleep stage, stimulation is started, if the patient is detected to be in a second or deep sleep state, the stimulation intensity is reduced or stopped, so that the influence of the stimulation on the sleep quality of the patient is reduced, and the DBS treatment effect is improved.
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The invention will be further described with reference to the drawings and examples.
FIG. 1 is a flow chart of the invention for detecting sleep stages from brain electrical signals of a patient suffering from Parkinson's disease;
FIG. 2 is a graph of time-frequency analysis of brain electrical signals before and after sleep in a patient with Parkinson's disease;
FIG. 3 is a graph of time-frequency analysis of brain electrical signals before administration of a drug to a patient suffering from Parkinson's disease;
FIG. 4 is a graph of time-frequency analysis of brain electrical signals after administration of a drug to a patient suffering from Parkinson's disease;
FIG. 5 is a graph of time-frequency analysis of brain electrical data of a patient suffering from Parkinson's disease;
FIG. 6 is a one second brain electrical signal waveform of a Parkinson's disease patient;
FIG. 7 is a graph of one minute electroencephalogram for a patient with Parkinson's disease;
FIG. 8 is a graph of band B power variation during sleep;
FIG. 9 is a power variation graph of band Delta;
FIG. 10 is a Delta band power variation graph during sleep;
fig. 11 is a sleep stage detection diagram.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. Embodiments of the invention are described herein in terms of various specific embodiments, including those that are apparent to those of ordinary skill in the art and all that come within the scope of the invention.
According to the illustration of fig. 1, a method for detecting sleep stages in a parkinson's disease patient comprises the steps of:
s1: before a patient falls asleep, a frequency sub-band B of the energy concentration in the brain electrical signal of the patient is acquired and determined, different frequency bands of the brain electrical signal are related to different brain activities, and the frequency sub-band B mainly comprises Delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-35 Hz) and Gamma (above 35 Hz), the brain electrical signal of the patient with the Parkinson disease can have higher energy intensity in the Beta frequency band, which is a typical characteristic of the Parkinson disease, and research shows that the energy concentration in different sub-bands of the Beta frequency band can correspond to different symptoms of Parkinson disease, such as low Beta frequency band (13-20 Hz) and high Beta frequency band (20-35 Hz). Therefore, for a specific parkinsonism, it is first necessary to perform a frequency-domain or time-frequency-domain analysis on its electroencephalogram signal to determine the frequency band in which its energy is mainly concentrated for subsequent analysis.
S2: the method comprises the steps that a patient continuously collects brain electrical signals of the patient in the whole period from before to after falling asleep, and meanwhile, the collected brain electrical signals of the patient are divided according to set time to achieve one-time detection for the set time;
s3: calculating the power P1 of the picked-up signal in the frequency band B;
s4: calculating the power P2 of the acquired signal in the frequency band Delta;
the first calculation method of the band power is to carry out Fourier transformation on the signal to obtain the frequency and the amplitude corresponding to each frequency, calculate the sum of the amplitudes corresponding to each frequency in the band B as the power in the band B, and the second calculation method is to carry out band-pass filtering with the range of the signal as the band B to obtain the waveform after filtering, calculate the line length or the area as the power in the band, and the steps S3 and S4 adopt the same calculation method.
S5: when thresholds thr1 and thr2 are set while satisfying P1< thr1 and P2> thr2, then the patient is detected to enter a sleep state.
According to the illustration of fig. 2, the abscissa in the figure represents time, the ordinate represents frequency, the middle color represents the energy corresponding to a certain frequency band at a certain moment, the darker the color represents the larger energy, the energy in the signal is mainly concentrated between the low Beta frequency bands before the patient falls asleep, because of the electroencephalogram characteristics caused by parkinson's disease, the low Beta frequency band energy is reduced after the patient falls asleep, and the low frequency band Delta and the low frequency band Theta energy 202 start to rise at the same time, as the sleep stage of the patient goes deep, the slow wave in the signal becomes more, the low frequency band energy further increases, and peaks when the patient goes deep. In addition, the high Beta band energy 203 rises after the patient falls asleep, so while sleep results in a decrease in low Beta band energy, the overall energy level of Beta (13-35 Hz) remains unchanged or changes less, so using Beta (13-35 Hz) to describe the pathological brain electrical characteristics of the patient before and after sleep is inaccurate.
According to fig. 2, when the patient enters the sleep state, the power of the low Beta frequency band 201 in the electroencephalogram signal is reduced, but simply judging that the patient enters the sleep state according to the condition is inaccurate, because the power of the Beta frequency sub-band is reduced when the parkinson disease treatment medicine is taken. According to fig. 3, the frequency sub-band 301 of Beta in the brain signal of the patient keeps a strong energy until the therapeutic drug is taken, according to fig. 4, after the patient takes the drug and the drug effect is effective, the energy of the Beta frequency sub-band is obviously reduced, the power of Theta (4-8 Hz) band 401 is increased, and the power of Delta (1-4 Hz) band 402 is not obviously fluctuated.
Therefore, the energy in the frequency band B and the frequency band Delta is calculated, and the threshold value judgment is carried out twice, so that the aim of eliminating the influence of medicaments and other potential factors on the sleep state detection is achieved, and the detection accuracy is improved.
S6: and setting a corresponding threshold again according to the power change in the Delta frequency band, and taking the value of the set threshold by the power of the frequency band Delta in the awake state, wherein the power in the Delta frequency band is in a corresponding threshold interval, so that the sleep stage of the patient is determined. After the patient enters a sleep state, the Delta frequency band energy is increased, the slow wave in the brain electrical signal becomes more along with the deep sleep stage, the Delta frequency band energy is further increased, and the peak is reached when the patient enters a deep sleep stage. Therefore, the Delta frequency band energy change curve should show a stepwise rise and then a stepwise fall in the sleeping process, and the cycle is repeated. And setting a corresponding threshold according to the Delta frequency band energy change curve to obtain the sleep stage of the parkinsonism patient.
In the step S2, the set time is divided into one minute or five minutes, and the real-time performance of the sleep detection is not required, so that the detection can be performed once every one minute, five minutes or longer signal is acquired.
In the step S5, the threshold thr1 is set to a value according to the power in the frequency band B, and the threshold thr2 is set to a value according to the power in the frequency band Delta.
Embodiment one:
taking the sleep brain electrical signal of the parkinsonism patient as an example for analysis, the sampling rate of the signal is 250Hz.
Step one, determining a frequency band B in which energy is mainly concentrated in a signal according to the frequency characteristics of the brain electrical signal of the patient. From the graph shown in fig. 5, it is known that the energy in the brain electrical signal of the patient is mainly concentrated between about 13-19Hz, which is a typical brain electrical characteristic caused by parkinson's disease, and thus the frequency band B is 13-19Hz.
And step two, collecting brain electrical signals of the patient, and detecting after dividing the brain electrical signals in one minute.
Calculating the power P1 of the signal in the frequency band B, obtaining a spectrogram of the signal by utilizing Fourier transform, wherein the abscissa in the spectrogram represents the frequency, the ordinate represents the amplitude according to the graph shown in FIG. 7, the spectrogram can intuitively reflect the energy corresponding to each frequency in the signal, the frequency band B (13-19 Hz) is shown in a dotted line in the graph, the signal shows higher amplitude in the frequency band B according to the graph, the sum of the amplitudes is calculated to be used as the energy P1 in the frequency band B, and the energy P1 of the frequency band B in the signal is calculated to be 26.73.
And step four, calculating the power P2 of the signal in the frequency band Delta, wherein the calculation method is the same as that in the step three, the frequency band B is changed into the frequency band Delta, and the energy of the frequency band Delta in the signal is calculated to be 25.91.
Step five, calculating the brain electrical data of the whole sleep stage of the patient according to the methods described in the step three and the step four, wherein the abscissa in the diagram represents time, the whole data is 401min, the ordinate represents the amplitude of the energy of the frequency band, and the dotted line in the diagram is a set threshold thr1, in this embodiment thr1 is set to 20, and as can be seen from the diagram, when the time exceeds 30min, the power P1 of the frequency band B starts to drop below thr1, and then is kept all the time.
As shown in fig. 9, the broken line is a set threshold thr2, and thr2 is set to 37 in this embodiment, and it can be seen from the figure that when the time exceeds 37min, the power P2 of the frequency band Delta rises above the threshold thr2, then gradually rises, and drops below thr2 at 394 min.
Therefore, the time interval of the conditions P1< thr1 and P2> thr2 is 37-394min, and the patient is judged to be in a sleep state in the interval.
Step six, corresponding thresholds thr2, thr3 and thr4 are set, and the sleep stage of the patient is determined according to the range of the Delta frequency band power P2, and the corresponding relationship is shown in table 1.
The sleep stage of the patient is determined according to the correspondence relationship in table 1, and thr2 is 37, thr3 is 48, and thr4 is 70 in the present embodiment, as shown in fig. 10 and 11.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (3)
1. A method of detecting sleep stages in a parkinson's disease patient, comprising the steps of:
s1: before a patient falls asleep, collecting and determining a frequency sub-band B in which energy is concentrated in an electroencephalogram signal of the patient;
s2: the method comprises the steps that a patient continuously collects brain electrical signals of the patient in the whole period from before to after falling asleep, and meanwhile, the collected brain electrical signals of the patient are divided according to set time to achieve one-time detection for the set time;
s3: calculating the power P1 of the picked-up signal in the frequency band B;
s4: calculating the power P2 of the acquired signal in the frequency band Delta;
s5: setting threshold values thr1 and thr2, and detecting that the patient enters a sleep state when P1< thr1 and P2> thr2 are met simultaneously;
s6: and setting a corresponding threshold again according to the power change in the Delta frequency band, and taking the value of the set threshold by the power of the frequency band Delta in the awake state, wherein the power in the Delta frequency band is in a corresponding threshold interval, so that the sleep stage of the patient is determined.
2. A method of detecting sleep stages in parkinson's disease patients as claimed in claim 1, wherein: in the step S2, the set time is divided into one minute or five minutes.
3. A method of detecting sleep stages in parkinson's disease patients as claimed in claim 1, wherein: in the step S5, the threshold thr1 is set to a value according to the power in the frequency band B, and the threshold thr2 is set to a value according to the power in the frequency band Delta.
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