CN116458898A - Parkinson's disease depression feature extraction method and system - Google Patents
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- 208000018737 Parkinson disease Diseases 0.000 title claims description 13
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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Abstract
The invention provides a method for extracting characteristics of parkinsonism depression, which belongs to the technical field of biomedicine and comprises the following specific steps: acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed; performing continuous wavelet transformation on the local field potential to be processed, and reserving alpha, lowbeta, highbeta and beta frequency bands; and extracting the blasting characteristics of each frequency band. According to the invention, after the local field potential is processed by using continuous wavelet transformation, the blasting characteristics of alpha, lowbeta, high beta and beta frequency bands are extracted, and the high-precision classification of the Parkinson depression degree is realized by utilizing the Parkinson depression feature extraction system based on the extracted blasting features.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a method and a system for extracting characteristics of parkinsonism depression.
Background
Parkinson's disease, the second most common neurodegenerative disease, affects 1% -3% of the population over 60 years of age worldwide. It is characterized by non-motor (sleep, sensory, cognitive and autonomic disturbances) and motor symptoms (tremors, bradykinesia, stiffness and gait disturbances) with a great impact on the patient's autonomy and quality of life. Depression is a prominent non-motor symptom in parkinson's disease. The parkinsonism suffers from 20% -40% of the patients suffering from depression, which is twice that of the general population. More importantly, doctors often neglect the influence of depression on parkinsonism, resulting in a great increase in the incidence of parkinsonism and a drastic decrease in quality of life.
In the prior art, a means based on biochemical reagents and brain electricity is generally adopted to detect parkinsonism depression, or depression detection is carried out on the basis of feature extraction and classification. In terms of feature extraction, early speech-based depression-related studies focused mainly on time-domain features such as pause time, recording time, feedback time to questions, speech rate, etc. Later, it was found that a single feature could not cover information with sufficient discrimination to aid clinical diagnosis, and that with intensive research into speech signals, a large number of the remaining speech signal features were constructed. However, the existing characteristic method lacks topic scenario-related speech information, has insufficient expressive force in the field of depression detection, and limits the performance of a final parkinsonism depression detection system; thus, there is a need for improved methods for extracting features of parkinsonism.
Therefore, the method and the system for extracting the characteristics of the parkinsonism depression are provided for objectively and effectively extracting the characteristics, and further are used for realizing accurate classification of parkinsonism depression degree, so that the method and the system are the problems to be solved by the person skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a method and a system for extracting depression characteristics of parkinsonism, which are used for classifying depression degrees based on local field potential characteristics obtained by wavelet transformation processing.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in one aspect, the invention provides a method for extracting characteristics of parkinsonism depression, which comprises the following steps:
s1, acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed;
s2, carrying out continuous wavelet transformation on the local field potential to be processed, and reserving alpha, low beta, high beta and beta frequency bands;
s3, extracting blasting characteristics of each frequency band in the S2.
Preferably, the S2 includes:
s21, comparing the initial part of the local field potential signal f (t) to be processed with a wavelet ψ (t), and calculating a wavelet coefficient;
s22, shifting the wavelet psi (t) rightward to obtain a wavelet psi (t-d), wherein d is the distance of the shift rightward, and continuously comparing the current local field potential signal f (t-d) to be processed with the wavelet psi (t-d) to obtain a wavelet coefficient of the current part;
s23, repeating the S21 and the S22, and continuously moving the wavelet to the right until the input of the local field potential signal to be processed is finished;
s24, expanding the wavelet to obtain a current wavelet function psi (t/ns), wherein n is an expansion multiple; and repeating the S21, the S22 and the S23, and reserving the alpha, the low beta, the high beta and the beta frequency bands.
Preferably, the wavelet coefficient calculation formula is as follows:
where a represents the positioning frequency and b represents the positioning time.
Preferably, the extracting the blasting characteristics of each frequency band in S2 in S3 includes:
and respectively averaging wavelet coefficients on the alpha, lowbeta, highbeta frequency band and the beta frequency band on the corresponding frequency bands to obtain an envelope curve fitting the local field potential signal to be processed, and obtaining the blasting characteristics by taking 75% quantiles passing through the envelope curve as a threshold value.
Preferably, the blasting characteristics include blasting duration, blasting probability, blasting amplitude.
Preferably, a time amplitude graph of a to-be-processed local field potential signal waveform is fitted based on wavelet coefficients of each frequency band, so that a blasting area of the to-be-processed local field potential signal is obtained, the width of the blasting area represents the blasting duration, the amplitude of the blasting area envelope is the blasting amplitude, and the blasting probability is the blasting frequency in a period of time.
In another aspect, the present invention provides a parkinsonism depression feature extraction system for implementing the parkinsonism depression feature extraction method, which comprises:
the preprocessing module is used for acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed;
the CWT processing module is used for carrying out continuous wavelet transformation on the local field potential to be processed and reserving alpha, lowbeta, highbeta and beta frequency bands;
and the feature extraction module is used for extracting the blasting features of each frequency band in the CWT processing module.
The classification module is used for classifying the depression degree of the Parkinson's disease according to the blasting characteristics; wherein the classification is by two kinds of labels: normal, depressive are classified.
Compared with the prior art, the parkinsonism depression feature extraction method and system provided by the invention realize effective classification of parkinsonism depression through signal analysis and feature extraction of local field potential, and have the advantages of high-efficiency and direct flow, and the specific beneficial effects are as follows:
(1) According to the invention, after the local field potential is processed by using continuous wavelet transformation, the blasting characteristics of alpha, lowbeta, highbeta and beta frequency bands are extracted, and the high-precision classification of the parkinsonism depression degree is realized by utilizing the parkinsonism depression feature extraction system provided by the invention.
(2) The average blast magnitude in the alpha frequency band is significantly different between patients suffering from parkinsonism and non-depressed patients (p=0.001), so that the characteristic is considered to be a marker of PD with depression, and a new algorithm is provided for the development of a brain-computer interface.
Drawings
Fig. 1 is a flowchart of a parkinsonism depression feature extraction method provided by an embodiment of the invention;
FIG. 2 is a flowchart of a continuous wavelet process provided by an embodiment of the present invention;
FIG. 3 is a time-amplitude diagram of a to-be-processed local field potential signal fitted by a CWT processed beta-band wavelet coefficient according to an embodiment of the present invention;
FIG. 4 is a graph of the time and frequency spectrum amplitudes of a partial field potential signal to be processed after CWT processing according to an embodiment of the present invention;
FIG. 5 is a graph comparing items in the alpha frequency band over the depressed and normal groups; wherein A is a blasting probability comparison chart, B is a blasting duration comparison chart, C is a blasting amplitude comparison chart, and D is a comparison chart of blasting lengths between two groups;
FIG. 6 is a graph comparing items in the beta band over the depressed and normal groups; wherein A is a blasting probability comparison chart, B is a blasting duration comparison chart, C is a blasting amplitude comparison chart, and D is a comparison chart of blasting lengths between two groups;
FIG. 7 is a graph comparing items on the lowbeta band over the depressed and normal groups; wherein A is a blasting probability comparison chart, B is a blasting duration comparison chart, C is a blasting amplitude comparison chart, and D is a comparison chart of blasting lengths between two groups;
FIG. 8 is a graph comparing items on the highbeta band over the depressed and normal groups; wherein A is a blasting probability comparison chart, B is a blasting duration comparison chart, C is a blasting amplitude comparison chart, and D is a comparison chart of blasting lengths between two groups;
FIG. 9 is a flowchart of an integrated learning process according to an embodiment of the present invention;
fig. 10 is a characteristic curve of each machine learning model provided in the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In one aspect, the embodiment of the invention discloses a method for extracting characteristics of depression of parkinsonism, wherein a flow chart is shown in fig. 1, and the method specifically comprises the following steps:
s1, acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed; preprocessing includes denoising local field potential, removing poorly recorded data, filtering.
S2, carrying out continuous wavelet transformation on the local field potential to be processed, and reserving alpha, low beta, highbeta and beta frequency bands.
Wavelet transformation is a method for analyzing time-frequency of signals, and has the characteristic of multi-resolution analysis. I.e. low time-domain resolution and high frequency-domain resolution for the low frequency part of the signal and high time-domain resolution and low frequency-domain resolution for the high frequency part of the signal. Wavelet analysis has a strong sensitivity to local features of the signal. It can more clearly show the time-dependent change of the frequency of the non-stationary signal than the fourier transform.
The continuous wavelet transform flow chart is shown in fig. 2, and includes the following steps:
s21, comparing an initial part of a local field potential signal f (t) to be processed with a wavelet ψ (t), and calculating a wavelet coefficient; where the coefficient C is used to represent the approximation of the portion to the wavelet, the larger the value of C represents the more similar, and conversely, the lower the less similar.
S22, shifting the wavelet psi (t) rightward to obtain a wavelet psi (t-d), wherein d is the distance of the shift rightward, and continuously comparing the current local field potential signal f (t-d) to be processed with the wavelet psi (t-d) to obtain the wavelet coefficient of the current part;
s23, repeating the steps S21 and S22, and continuously moving the wavelet to the right until the input of the local field potential signal to be processed is finished;
s24, expanding the wavelet to obtain a current wavelet function psi (t/ns), wherein n is an expansion multiple; and repeating S21, S22 and S23, and reserving alpha, low beta, high beta and beta frequency bands.
In the specific implementation process, the wavelet coefficient calculation formula is as follows:
where a represents the positioning frequency and b represents the positioning time.
S3, extracting blasting characteristics of each frequency band in the S2.
Wavelet coefficients on the alpha frequency band, the low beta frequency band, the high beta frequency band and the beta frequency band are averaged on the corresponding frequency bands to obtain an envelope curve fitting the local field potential signal to be processed, and 75% quantiles passing through the envelope curve are used as thresholds to obtain blasting characteristics (blasting duration, blasting probability and blasting amplitude).
Fitting a time amplitude graph of a to-be-processed local field potential signal waveform based on wavelet coefficients of each frequency band, and obtaining a blasting area of the to-be-processed local field potential signal, wherein the width of the blasting area represents blasting duration, the amplitude of the blasting area envelope is blasting amplitude, and the blasting probability is blasting frequency in a period of time.
Taking the beta frequency band as an example, fig. 3 shows a time-amplitude diagram of the wavelet coefficient of the beta frequency band fitted to the local field potential signal to be processed after the CWT processing. The time amplitude and frequency spectrum amplitude graphs of the to-be-processed local field potential signal CWT are shown in fig. 4, wavelet coefficients on the beta frequency band are averaged on the beta frequency band to obtain an envelope curve fitting the original signal, and the explosion characteristics (explosion duration, explosion probability and explosion amplitude) are obtained by taking 75% quantiles passing through the envelope curve as a threshold value, wherein the explosion with the duration, the explosion probability and the explosion amplitude of less than 100ms does not account for statistics. As shown in fig. 3, black lines in the diagram are original signals, i.e., local field potential signals to be processed; the red line is the envelope curve fitted by the wavelet coefficient, and the blue line is 75% quantile; the shaded area in the figure is the area where the signal has a burst, the width of the shaded area represents the duration of the burst, the amplitude of the burst area envelope is the burst amplitude, the burst probability is the burst frequency over a period of time (as in fig. 3, the burst probability is: 7/4 (bursts/s)).
Specifically, parkinsonism is divided into two groups of depression and normal according to the HAMD-24 scale, 3 features are extracted from each of the 4 frequency bands, three features of each frequency band are compared, and compared with each other, as shown in fig. 5-8, analysis shows that the average blasting amplitude in the alpha frequency band has a significant difference between the two groups, so that the features can be used as markers of PD associated with depression. Provides a new algorithm for the development of the brain-computer interface.
Based on the above conclusion, the embodiment of the invention also provides a parkinsonism depression feature extraction system, which can realize the method and further classify the parkinsonism depression degree, and specifically comprises the following steps:
the preprocessing module is used for acquiring an electroencephalogram signal, processing the electroencephalogram signal to acquire a local field potential, and preprocessing the local field potential to acquire a local field potential to be processed;
the CWT processing module is used for carrying out continuous wavelet transformation on the local field potential to be processed and reserving alpha, lowbeta, highbeta and beta frequency bands;
and the feature extraction module is used for extracting the blasting features of each frequency band in the CWT processing module.
The classification module is used for classifying the depression degree of the Parkinson's disease according to the blasting characteristics.
Specifically, in the classification module of the present embodiment, the 12 features extracted through wavelet transformation are classified by using an ensemble learning method, so as to verify that the blasting features provided by the present invention have a good characterization effect on classification of parkinson's depression, and a specific flow is shown in fig. 9:
and extracting blasting characteristics based on the information of the to-be-processed local field potential signals processed by the CWT, and inputting the extracted characteristics into a classification prediction model.
In this embodiment, through analysis of 6 machine learning models, it is finally determined that the integrated learning with the highest accuracy is adopted as the classification prediction model. Specific results of classification accuracy of each machine learning model are shown in table 1, and fig. 10 shows characteristic curves (Receiver Operating Characteristic, ROC) of each machine learning model. Ensemble learning is a predictive model algorithm that combines multiple machine learning techniques to achieve the effect of reducing variance, bias, or improving predictions.
TABLE 1 machine learning model classification accuracy
Parkinsonism is divided into a depression group and a normal group according to the HAMD-24 scale and used for training a classification prediction model. The ensemble learning classification prediction model comprises the following steps: a neighbor algorithm (K-nearest neighbor), a multi-layer perceptron (Multilayer Perceptron), random Forests (Random Forests), decision trees (Decision trees), and a grid search method is used to find the optimal parameter combinations, wherein the corresponding specific gravity is [1, 1]. Voting is carried out on classification results predicted by a neighboring algorithm, a multi-layer perceptron, a random forest and a decision tree, and a minority obeys majority to determine the classification results.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method for extracting characteristics of parkinsonism depression, which is characterized by comprising the following steps:
s1, acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed;
s2, carrying out continuous wavelet transformation on the local field potential to be processed, and reserving alpha, low beta, high beta and beta frequency bands;
s3, extracting blasting characteristics of each frequency band in the S2.
2. The method for extracting features of depression in parkinson' S disease according to claim 1, wherein said S2 comprises:
s21, comparing the initial part of the local field potential signal f (t) to be processed with a wavelet ψ (t), and calculating a wavelet coefficient;
s22, shifting the wavelet psi (t) rightward to obtain a wavelet psi (t-d), wherein d is the distance of the shift rightward, and continuously comparing the current local field potential signal f (t-d) to be processed with the wavelet psi (t-d) to obtain a wavelet coefficient of the current part;
s23, repeating the S21 and the S22, and continuously moving the wavelet to the right until the input of the local field potential signal to be processed is finished;
s24, expanding the wavelet to obtain a current wavelet function psi (t/ns), wherein n is an expansion multiple; and repeating the S21, the S22 and the S23, and reserving the alpha, the low beta, the high beta and the beta frequency bands.
3. The method for extracting features of depression in parkinson's disease according to claim 2, wherein said wavelet coefficients are calculated as follows:
where a represents the positioning frequency and b represents the positioning time.
4. The method for extracting features of depression in parkinson' S disease according to claim 2, wherein said extracting the blasting features of each frequency band in S2 in S3 comprises:
and respectively averaging wavelet coefficients on the alpha frequency band, the low beta frequency band, the high beta frequency band and the beta frequency band to obtain an envelope curve fitting the local field potential signal to be processed, and obtaining the blasting characteristics by taking 75% quantiles passing through the envelope curve as a threshold value.
5. A method of feature extraction for depression in parkinson's disease according to claim 4, wherein said blast features comprise duration of blast, probability of blast, magnitude of blast.
6. The method for extracting the characteristics of the depression of the parkinson's disease according to claim 5, wherein a blasting area of the local field potential signal to be processed is obtained based on a time amplitude graph of a waveform of the local field potential signal to be processed fitted with wavelet coefficients of each frequency band, the width of the blasting area represents the blasting duration, the amplitude of an envelope of the blasting area is the blasting amplitude, and the blasting probability is the blasting frequency in a period of time.
7. A feature extraction system for parkinson's disease depression, comprising:
the preprocessing module is used for acquiring an electroencephalogram signal, processing the electroencephalogram signal to obtain a local field potential, and preprocessing the local field potential to obtain a local field potential to be processed;
the CWT processing module is used for carrying out continuous wavelet transformation on the local field potential to be processed and reserving alpha, lowbeta, highbeta and beta frequency bands;
and the feature extraction module is used for extracting the blasting features of each frequency band in the CWT processing module.
8. The parkinsonism depression feature extraction system of claim 7, wherein said system further comprises:
and the classification module is used for classifying the depression degree of the Parkinson's disease according to the blasting characteristics.
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