CN112750468A - Parkinson disease screening method, device, equipment and storage medium - Google Patents
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Abstract
The invention provides a Parkinson disease screening method, a Parkinson disease screening device, Parkinson disease screening equipment and a storage medium, wherein the method comprises the following steps: collecting voice data of a Parkinson patient and a healthy contrast; preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient; extracting the spectral features and the voice features of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method; inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model; inputting the voice data of the patient into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism. The invention realizes early intelligent diagnosis of the Parkinson's disease by only utilizing the sound of the patient.
Description
Technical Field
The invention relates to the field of Parkinson disease screening, in particular to a Parkinson disease screening method, a Parkinson disease screening device, Parkinson disease screening equipment and a storage medium.
Background
With the aging of the national population, more and more senile diseases need to be paid attention to in advance, wherein the Parkinson's Disease (PD) is a common nervous system degenerative disease of the elderly, and the average onset age is about 60 years old. Parkinson's disease is almost incurable and patients already have very severe mental impairment, typically waiting for the physician to diagnose, which is very disadvantageous for late treatment. Therefore, the Parkinson is diagnosed in advance, and the early intervention is carried out by using some neuroprotective drugs, so that the development of the disease can be delayed and the life quality of the patient can be improved. Therefore, early screening for parkinson's disease is very interesting.
The Parkinson's disease people have low accuracy in early diagnosis, lack of sensitivity and good specificity. Of the various manifestations of parkinson's disease, approximately nine patients with parkinson's disease present different degrees of speech impairment. Meanwhile, voice data can be collected conveniently and rapidly without damaging the body of a person to be diagnosed, and more people begin to pay attention to the Parkinson disease detection based on voice obstacle.
The oscillation pattern of the vocal cords is periodic in normal persons, that is, the interval between two consecutive openings or closures is the same when the vocal cords vibrate, so that the spectrogram of the emitted sound also shows a periodicity, and the parkinson patient shows certain dysphonia due to the pathological changes, so that the phonic pattern of the vocal cords is greatly different from that of normal persons. This is also an important reason why we can use speech to accomplish the detection of parkinson patients.
Disclosure of Invention
The invention aims to provide a Parkinson disease screening method, a Parkinson disease screening device, a Parkinson disease screening equipment and a storage medium, so as to solve the existing problems.
In order to achieve the above object, an embodiment of the present invention provides a parkinson's disease screening method, which includes
Collecting voice data of a Parkinson patient and a healthy contrast;
preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient;
extracting the spectral features and the voice features of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method;
inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model;
inputting the voice data of the patient into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism.
Further, the collecting of the voice data of the parkinson patient and the healthy control is specifically: and respectively collecting voice data which are 3s in duration and contain a plurality of vowels for the Parkinson patients and the healthy contrast persons, wherein the sampling frequency is 24kHz, and the sampling precision is 16 bits.
Further, the preprocessing the voice data to obtain the first voice database of the healthy contrast and the second voice database of the parkinson patient specifically includes:
cutting off 0.5s voice before and after the voice data to eliminate the influence of respiration on pronunciation;
the method comprises the steps of obtaining a first voice database of a healthy contrast person and a second voice database of a Parkinson patient by performing deep processing on each voice data which corresponds to a preset audio sample and belongs to the healthy contrast person and each voice data which corresponds to the preset audio sample and belongs to the Parkinson patient.
Further, extracting speech spectrum features of the first speech database and the second speech database, wherein the extracting of the speech spectrum features based on a deep learning network model specifically comprises:
constructing a spectrogram based on the voice data, and graying the spectrogram;
inputting the grayed spectrogram into 10 convolution layers to obtain 10 feature maps after convolution;
passing the 10 convolved feature maps through a maximum pooling layer to obtain 10 pooled feature maps;
passing the 10 pooled feature maps through 32 convolutional layers to obtain 32 convolved feature maps;
obtaining 32 pooled feature maps by passing the obtained 32 convolved feature maps through a maximum pooling layer;
and drawing the obtained 32 pooled feature graphs into a single dimension, and inputting the single dimension into a full connection layer to obtain a multi-dimensional feature vector.
Further, the inputting the spectral feature and the speech feature into the neural network model for training to obtain the trained neural network model specifically comprises:
splicing the multidimensional feature vector with the voice features extracted based on the traditional statistical method to obtain spliced voice features;
inputting the spliced voice features into two full-connection layers, and performing feature fusion to obtain fused features;
and performing classification prediction by using the fused features.
Furthermore, the inputting the voice data of the patient into the trained neural network model and the predicting the parkinson disease by using the soft voting mechanism specifically comprises:
collecting voice data of a patient, and inputting the trained neural network model to output a disease probability value and a health probability value;
and selecting the category with the maximum probability value as a final judgment result by adopting a soft voting mechanism based on the pronunciation sickness probability value and the health probability value.
Further, the step of selecting the category with the maximum probability value as the final judgment result by adopting a soft voting mechanism based on the pronunciation ill probability value and the health probability value specifically includes:
respectively calculating the mean value of the disease probability value and the mean value of the health probability value;
and comparing the mean value of the disease probability value with the mean value of the health probability value, and taking the maximum value as a final judgment result.
The invention also provides a Parkinson disease screening device, which comprises
The acquisition module is used for acquiring voice data of the Parkinson patient and the healthy contrast;
the preprocessing module is used for preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient;
the extraction module is used for extracting the speech spectrum characteristics and the speech characteristics of the first speech database and the second speech database, wherein the speech spectrum characteristics are extracted based on a deep learning network model, and the speech characteristics are extracted based on a traditional statistical method;
the training module is used for inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model;
and the prediction module is used for inputting the voice data of the patient into the trained neural network model and predicting the Parkinson disease by using a soft voting mechanism.
The invention also provides Parkinson disease screening equipment which comprises a storage and a processor, wherein the storage is internally provided with a computer program, and the processor is used for operating the computer program to realize the Parkinson disease screening method.
The present invention also provides a storage medium storing a computer program executable by a processor of a device in which the storage medium is located to implement the parkinson's disease screening method.
The invention provides a Parkinson disease screening method, which comprises the following steps: collecting voice data of a Parkinson patient and a healthy contrast; preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient; extracting the spectral features and the voice features of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method; inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model; inputting the voice data of the patient into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism. The early intelligent diagnosis of the Parkinson's disease is realized by only utilizing the sound of a patient through data acquisition, preprocessing, voice feature extraction based on deep learning and voice feature extraction based on a traditional statistical method, a neural network and model prediction based on a soft voting mechanism.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a parkinson's disease screening method according to a first embodiment of the present invention.
FIG. 2 is another schematic flow chart of a Parkinson's disease screening method according to the first embodiment of the present invention.
Fig. 3 is a schematic flow chart of a parkinson's disease screening device according to a second embodiment of the present invention.
Detailed Description
In order to make 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 described clearly and completely with reference to the accompanying drawings of the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention.
Referring to fig. 1-2, a first embodiment of the present invention provides a parkinson's disease screening method, including:
speech data of the parkinson patient and the healthy control were collected S11.
In the embodiment, the voice data acquisition needs to be fixed at the position 5cm in front of the mouth of a patient by a professional recording device in an environment with noise less than 60dB under the condition that a Parkinson patient does not take medicine or takes more than 24h from the last time and violent movement is prohibited within one hour before the voice data acquisition, after a doctor guides training pronunciation for several times, voice data with the duration of 3s and containing a plurality of vowels are respectively acquired for the Parkinson patient and a healthy pair such as a contrast, for example, voice data with the duration of 3 s/a/,/o/,/u/for the Parkinson patient; the healthy contrast persons adopt the same equipment and method to collect voice data under the same environment, wherein the sampling frequency is 24kHz, and the sampling precision is 16 bits.
And S12, preprocessing the voice data to obtain a first voice database of healthy contrast persons and a second voice database of Parkinson patients.
In this embodiment, the 0.5s speech before and after the speech data is cut off, and the original speech data is processed into 2s speech data to eliminate the influence of breathing on pronunciation.
And then, performing deep processing on each voice data which corresponds to the preset audio sample and belongs to the healthy contrast person and each voice data which corresponds to the preset audio sample and belongs to the Parkinson patient to obtain a first voice database of the healthy contrast person and a second voice database of the Parkinson patient.
S13, extracting the spectrum feature and the voice feature of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method.
In this embodiment, the first speech database and the second speech database are subjected to speech spectrum feature extraction, where the speech spectrum feature extraction based on a deep learning network model specifically includes:
and constructing a spectrogram based on the voice data, and graying the spectrogram.
And inputting the grayed spectrogram into 10 convolution layers to obtain 10 feature maps after convolution.
And passing the 10 convolved feature maps through a maximum pooling layer to obtain 10 pooled feature maps.
And passing the 10 pooled feature maps through 32 convolutional layers to obtain 32 convolved feature maps.
And obtaining 32 pooled feature maps by passing the obtained 32 convolved feature maps through a maximum pooling layer.
And drawing the obtained 32 pooled feature graphs into a single dimension, and inputting the single dimension into a full connection layer to obtain a multi-dimensional feature vector.
For example, if the spectrogram is 28 × 28 and the convolutional layer is a 5 × 5 convolutional layer, the 28 × 28 spectrogram of the speech database is used as an input of the convolutional neural network, and 10 24 × 24 feature maps are obtained by 10 5 × 5 convolutional layers, and then 10 12 × 12 feature maps are obtained by the maximum pooling layer. Then, 32 8 × 8 feature maps are obtained through 32 5 × 5 convolutional layers, a maximum pooling layer is input to obtain 32 4 × 4 feature maps, the 32 4 × 4 feature maps are drawn into one dimension and then sent into a full connection layer, and a 512-dimensional feature vector is obtained.
In the present embodiment, the speech feature extraction is based on the conventional statistical method. The published parkinsonism voice diagnosis related database and common algorithms in recent years know pathological voice characteristics of some parkinsonism patients, such as fundamental frequency change, periodic change and the like; meanwhile, the speech nonlinear characteristic parameters such as repetition period density entropy (PRDE) and the like are obtained by improving the traditional linear characteristic parameters. The embodiment selects 28-dimensional original speech features from the original speech features as shown in table 1.
TABLE 1 Speech characteristics parameter Table
And S14, inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training to obtain a trained neural network model.
In this embodiment, the multidimensional feature vector is spliced with the voice features extracted based on the traditional statistical method to obtain spliced voice features; for example, the 28-dimensional features extracted based on the traditional method are spliced with the 512-dimensional features extracted through the neural network model to obtain 540-dimensional speech features.
And inputting the spliced voice features into the two full-connection layers, and performing feature fusion to obtain fused features.
And performing classification prediction by using the fused features, namely judging whether the voice patient has the Parkinson disease.
And S15, inputting the voice data of the doctor into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism.
In the embodiment, the voice data of the doctor is collected and input into the trained neural network model to output the illness probability value and the health probability value.
And respectively calculating the mean value of the illness probability value and the mean value of the health probability value based on the pronunciation illness probability value and the health probability value.
And comparing the mean value of the disease probability value with the mean value of the health probability value, and taking the maximum value as a final judgment result.
Taking the voice data of vowels/a/,/o/,/u/, as an example, the three groups of pronunciation data of each patient are respectively input into the trained neural network model, then the probability values of the three groups of pronunciation diseases and health are obtained, and the category with the maximum probability value is selected as the final judgment result by adopting a soft voting mechanism as shown in table 2.
TABLE 2 Soft voting mechanism
The embodiment collects the voice data of the Parkinson patients and the healthy contrast persons; preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient; extracting the spectral features and the voice features of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method; inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model; inputting the voice data of the patient into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism. The early intelligent diagnosis of the Parkinson's disease is realized by only utilizing the sound of a patient through data acquisition, preprocessing, voice feature extraction based on deep learning and voice feature extraction based on a traditional statistical method, a neural network and model prediction based on a soft voting mechanism.
The second embodiment of the present invention provides a parkinson's disease screening device, referring to fig. 3, comprising an acquisition module 110 for acquiring voice data of parkinson's patients and healthy controls.
In this embodiment, the collecting module 110 collects voice data containing a plurality of vowels with a duration of 3s for the parkinson patient and the healthy contrast respectively, wherein the sampling frequency is 24kHz and the sampling precision is 16 bits.
A preprocessing module 120, configured to preprocess the voice data to obtain a first voice database of a healthy control and a second voice database of a parkinson patient.
In this embodiment, the pre-processing module 120 cuts off 0.5s of speech before and after the speech data to eliminate the influence of respiration on pronunciation.
And then, performing deep processing on each voice data which corresponds to the preset audio sample and belongs to the healthy contrast person and each voice data which corresponds to the preset audio sample and belongs to the Parkinson patient to obtain a first voice database of the healthy contrast person and a second voice database of the Parkinson patient.
And an extraction module 130, configured to perform speech spectrum feature and speech feature extraction on the first speech database and the second speech database, where the speech spectrum feature is extracted based on a deep learning network model, and the speech feature is extracted based on a traditional statistical method.
In this embodiment, the extracting module 130 first constructs a spectrogram based on the voice data and grays the spectrogram.
And inputting the grayed spectrogram into 10 convolution layers to obtain 10 feature maps after convolution.
And passing the 10 convolved feature maps through a maximum pooling layer to obtain 10 pooled feature maps.
And passing the 10 pooled feature maps through 32 convolutional layers to obtain 32 convolved feature maps.
And obtaining 32 pooled feature maps by passing the obtained 32 convolved feature maps through a maximum pooling layer.
And drawing the obtained 32 pooled feature graphs into a single dimension, and inputting the single dimension into a full connection layer to obtain a multi-dimensional feature vector.
And the training module 140 is configured to input the speech spectrum features and the speech features into a neural network model for training, so as to obtain a trained neural network model.
In this embodiment, the training module 140 splices the multidimensional feature vector and the speech features extracted based on the traditional statistical method to obtain spliced speech features.
And inputting the spliced voice features into the two full-connection layers, and performing feature fusion to obtain fused features.
And performing classification prediction by using the fused features.
And the prediction module 150 is used for inputting the voice data of the doctor into the trained neural network model and predicting the Parkinson disease by using a soft voting mechanism.
In this embodiment, the prediction module 150 is configured to collect voice data of a patient and input the trained neural network model to output a disease probability value and a health probability value.
And respectively calculating the mean value of the ill probability value and the mean value of the healthy probability value.
And comparing the mean value of the ill probability value with the mean value of the healthy probability value, and selecting the category with the maximum probability value as a final judgment result by adopting a soft voting mechanism.
A third embodiment of the present invention provides a parkinson's disease screening apparatus, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used for operating the computer program to realize the parkinson's disease screening method.
A fourth embodiment of the present invention provides a storage medium storing a computer program executable by a processor of a device on which the storage medium is located to implement the parkinson's disease screening method.
In the embodiments provided in the embodiments of the present invention, it should be understood that the apparatus and method provided may be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A parkinson's disease screening method, comprising:
collecting voice data of a Parkinson patient and a healthy contrast;
preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient;
extracting the spectral features and the voice features of the first voice database and the second voice database; the speech spectrum features are extracted through a deep learning network model, and the speech features are extracted based on a traditional statistical method;
inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model;
inputting the voice data of the patient into the trained neural network model, and predicting the Parkinson disease by using a soft voting mechanism.
2. The parkinson's disease screening method of claim 1, wherein the collecting speech data of parkinson's patients and healthy controls is specifically: and respectively collecting voice data which are 3s in duration and contain a plurality of vowels for the Parkinson patients and the healthy contrast persons, wherein the sampling frequency is 24kHz, and the sampling precision is 16 bits.
3. The parkinson's disease screening method of claim 2, wherein the preprocessing of the speech data to obtain a first speech database of healthy controls and a second speech database of parkinson's patients is specifically:
cutting off 0.5s voice before and after the voice data to eliminate the influence of respiration on pronunciation;
the method comprises the steps of obtaining a first voice database of a healthy contrast person and a second voice database of a Parkinson patient by performing deep processing on each voice data which corresponds to a preset audio sample and belongs to the healthy contrast person and each voice data which corresponds to the preset audio sample and belongs to the Parkinson patient.
4. The parkinson's disease screening method of claim 1, wherein the first speech database and the second speech database are subjected to speech spectrum feature extraction, wherein the speech spectrum feature extraction is specifically based on deep learning network model:
constructing a spectrogram based on the voice data, and graying the spectrogram;
inputting the grayed spectrogram into 10 convolution layers to obtain 10 feature maps after convolution;
passing the 10 convolved feature maps through a maximum pooling layer to obtain 10 pooled feature maps;
passing the 10 pooled feature maps through 32 convolutional layers to obtain 32 convolved feature maps;
obtaining 32 pooled feature maps by passing the obtained 32 convolved feature maps through a maximum pooling layer;
and drawing the obtained 32 pooled feature graphs into a single dimension, and inputting the single dimension into a full connection layer to obtain a multi-dimensional feature vector.
5. The parkinson's disease screening method of claim 4, wherein the inputting the spectral features and the speech features into the neural network model for training to obtain the trained neural network model specifically comprises:
splicing the multidimensional feature vector with the voice features extracted based on the traditional statistical method to obtain spliced voice features;
inputting the spliced voice features into two full-connection layers, and performing feature fusion to obtain fused features;
and performing classification prediction by using the fused features.
6. The Parkinson disease screening method according to claim 5, wherein the speech data of the doctor is input into a trained neural network model, and the prediction of the Parkinson disease is carried out by using a soft voting mechanism, specifically:
collecting voice data of a patient, and inputting the trained neural network model to output a disease probability value and a health probability value;
and selecting the category with the maximum probability value as a final judgment result by adopting a soft voting mechanism based on the pronunciation sickness probability value and the health probability value.
7. The parkinson's disease screening method of claim 6, wherein the selecting the category with the highest probability value as the final decision result by adopting a soft voting mechanism based on the pronunciation illness probability value and the health probability value specifically comprises:
respectively calculating the mean value of the disease probability value and the mean value of the health probability value;
and comparing the mean value of the disease probability value with the mean value of the health probability value, and taking the maximum value as a final judgment result.
8. The Parkinson disease screening device is characterized by comprising an acquisition module for
Collecting voice data of a Parkinson patient and a healthy contrast;
the preprocessing module is used for preprocessing the voice data to obtain a first voice database of a healthy contrast person and a second voice database of a Parkinson patient;
the extraction module is used for extracting the speech spectrum characteristics and the speech characteristics of the first speech database and the second speech database, wherein the speech spectrum characteristics are extracted based on a deep learning network model, and the speech characteristics are extracted based on a traditional statistical method;
the training module is used for inputting the speech spectrum characteristics and the speech characteristics into a neural network model for training so as to obtain a trained neural network model;
and the prediction module is used for inputting the voice data of the patient into the trained neural network model and predicting the Parkinson disease by using a soft voting mechanism.
9. A parkinson's disease screening apparatus comprising a memory having a computer program stored therein and a processor for executing the computer program to implement a parkinson's disease screening method of any of claims 1-7.
10. A storage medium storing a computer program executable by a processor of a device on which the storage medium is located to implement a parkinson's disease screening method as claimed in any of claims 1-7.
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