INTELLIGENT SCREENING ALGORITHM AND AUTOMATIC UPGRADING SYSTEM FOR CONGENITAL HEART DISEASES OF NEWBORNS BASED ON BIG DATA

Abstract

The present disclosure discloses an intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data. The key point of the technical solution is as follows: The intelligent screening algorithm and automatic upgrading system includes a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module; the heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing; and the heart sound data processing module is configured to process the data in the heart sound data module and extract heart sound feature parameters.

Claims

1. An intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data, comprising a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module; wherein the heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing; the heart sound data processing module is configured to process the data in the heart sound data module and extract heat sound feature parameters; the blood oxygen data module is configured to acquire various data of blood oxygen of the newborn for centralized processing; the blood oxygen data processing module is configured to process the data in the blood oxygen data module and extract blood oxygen feature parameters; the network upgrading module is configured to receive the heart sound feature parameters and the blood oxygen feature parameters and update in real time internal data of the database; the database is in communication connection with a network and configured to update in real the feature parameters of the congenital heart disease of the newborn; an artificial neural network algorithm, a support vector machine algorithm, the hidden Markov model (HMM) algorithm and the K-nearest neighbor algorithm are respectively built in the intelligent analysis module; the intelligent analysis module analyzes the heart sound feature parameters and the blood oxygen feature parameters on the basis of big data analysis for congenital heart disease screening, so as to distinguish signals from a healthy person and a patient; the congenital heart disease evaluation module is configured to analyze and evaluate a congenital heart disease screening result of the intelligent analysis module.

2. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein the heart sound data processing module comprises a heart sound wavelet denoising unit, an envelope extraction unit and a segmentation unit; the heart sound wavelet denoising unit performs wavelet transform on a noisy heart sound signal, processes, in a certain way, a wavelet coefficient obtained by the transform to remove noise contained in the signal, and performs inverse wavelet transform on the processed wavelet coefficient to obtain a denoised signal; the envelope extraction unit is configured to process a heart sound signal to obtain an envelope data point of the heart sound signal, and perform, on the basis of a filter, smoothing on the envelope data point of the heart sound signal; and the segmentation unit is configured to segment the heart sound signal.

3. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein a blood oxygen wavelet denoising unit is arranged in the blood oxygen data processing module; and the blood oxygen wavelet denoising unit is configured to denoise a noisy blood oxygen signal.

4. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein the network upgrading module comprises a network connection unit and an update detection unit; the network connection unit is configured to establish a communication connection between the database and Internet big data through a network; and the update detection unit is configured to detect in real time data parameters of the congenital heart diseases of the newborns in the Internet big data.

5. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein a firewall unit is arranged in the database, and the firewall unit is configured to defense hacker attack.

6. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein the HMM algorithm comprises a direct computing method, a forward algorithm and a backward algorithm.

7. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein the database comprises a data classification unit and a data inquiry unit; the data classification unit is configured to classify data in terms of familiarity; and the data inquiry unit is configured to inquire data information by means of entering key words.

8. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein the intelligent analysis module further comprises a heart murmur grading unit and a blood oxygen value unit; the heart murmur grading unit is configured to classify heart murmur; and the blood oxygen value unit is configured to calculate a specific numerical value of blood oxygen.

9. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 1, wherein each of the heart sound data processing module and the blood oxygen data processing module is provided with a signal classification unit; and the signal classification unit is configured to identify murmur and hypoxemia and establish a relationship between a two-indicator result and a corresponding congenital heart disease.

10. The intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of the newborns based on the big data according to claim 8, wherein the intelligent analysis module classifies the heart murmur using the Lasso algorithm and calculates a classification result; the formula of the Lasso algorithm is as follows: $H\left(X\right)=\underset{\beta \in R^P}{\arg \min }{.Math.y-X\beta .Math.}^2+\lambda .Math.\beta .Math.$ where R is a set of all real numbers; RP represents a p-dimensional vector; each component is a real number; β is a related coefficient; $\underset{\beta \in R^P}{\arg \min }{.Math.y-X\beta .Math.}^2$ is the least squares term; X represents an input result of each classifier; y represents a desired result; and λ represents a coefficient of regularization.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] FIG. 1 is a structural block diagram of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] The technical solutions in the embodiments of the present disclosure will be described clearly and completely below in combination with the accompanying drawings of the embodiments of the present disclosure. Apparently, the described embodiments are only part of the embodiments of the present disclosure, not all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative work shall fall within the protection scope of the present disclosure.

Embodiment 1

[0033] Referring to FIG. 1, the present disclosure provides an intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data. The technical solution is as follows: the intelligent screening algorithm and automatic upgrading system includes a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module.

[0034] The heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing.

[0035] The heart sound data processing module is configured to process the data in the heart sound data module and extract heat sound feature parameters.

[0036] The blood oxygen data module is configured to acquire various data of blood oxygen of the newborn for centralized processing.

[0037] The blood oxygen data processing module is configured to process the data in the blood oxygen data module and extract blood oxygen feature parameters.

[0038] The network upgrading module is configured to receive the heart sound feature parameters and the blood oxygen feature parameters and update in real time internal data of the database.

[0039] The database is in communication connection with a network and configured to update in real the feature parameters of the congenital heart disease of the newborn.

[0040] An artificial neural network algorithm, a support vector machine algorithm, the HMM algorithm and the K-nearest neighbor algorithm are respectively built in the intelligent analysis module; and the intelligent analysis module analyzes the heart sound feature parameters and the blood oxygen feature parameters on the basis of big data analysis for congenital heart disease screening, so as to distinguish signals from a healthy person and a patient.

[0041] The congenital heart disease evaluation module is configured to analyze and evaluate a congenital heart disease screening result of the intelligent analysis module.

[0042] In this embodiment, preferably, the heart sound data processing module includes a heart sound wavelet denoising unit, an envelope extraction unit and a segmentation unit; the heart sound wavelet denoising unit performs wavelet transform on a noisy heart sound signal, processes, in a certain way, a wavelet coefficient obtained by the transform to remove noise contained in the signal, and performs inverse wavelet transform on the processed wavelet coefficient to obtain a denoised signal; the envelope extraction unit is configured to process a heart sound signal to obtain an envelope data point of the heart sound signal, and perform, on the basis of a filter, smoothing on the envelope data point of the heart sound signal; and the segmentation unit is configured to segment the heart sound signal.

[0043] In this embodiment, preferably, a blood oxygen wavelet denoising unit is arranged in the blood oxygen data processing module; and the blood oxygen wavelet denoising unit is configured to denoise a noisy blood oxygen signal.

[0044] In this embodiment, preferably, the network upgrading module includes a network connection unit and an update detection unit; the network connection unit is configured to establish a communication connection between the database and Internet big data through a network; and the update detection unit is configured to detect in real time data parameters of the congenital heart diseases of the newborns in the Internet big data.

[0045] In this embodiment, preferably, a firewall unit is arranged in the database, and the firewall unit is configured to defense hacker attack.

[0046] In this embodiment, preferably, the HMM algorithm includes a direct computing method, a forward algorithm and a backward algorithm.

[0047] In this embodiment, preferably, the database includes a data classification unit and a data inquiry unit; the data classification unit is configured to classify data in terms of familiarity; and the data inquiry unit is configured to inquire data information by means of entering key words.

[0048] In this embodiment, preferably, the intelligent analysis module further includes a heart murmur grading unit and a blood oxygen value unit; the heart murmur grading unit is configured to classify heart murmur; and the blood oxygen value unit is configured to calculate a specific numerical value of blood oxygen.

[0049] In this embodiment, preferably, each of the heart sound data processing module and the blood oxygen data processing module is provided with a signal classification unit; and the signal classification unit is configured to identify murmur and hypoxemia and establish a relationship between a two-indicator result and a corresponding congenital heart disease.

Embodiment 2

[0050] Referring to FIG. 1, the present disclosure provides an intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data. The technical solution is as follows: the intelligent screening algorithm and automatic upgrading system includes a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module.

[0051] The heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing.

[0052] The heart sound data processing module is configured to process the data in the heart sound data module and extract heat sound feature parameters.

[0053] The blood oxygen data module is configured to acquire various data of blood oxygen of the newborn for centralized processing.

[0054] The blood oxygen data processing module is configured to process the data in the blood oxygen data module and extract blood oxygen feature parameters.

[0055] The network upgrading module is configured to receive the heart sound feature parameters and the blood oxygen feature parameters and update in real time internal data of the database.

[0056] The database is in communication connection with a network and configured to update in real the feature parameters of the congenital heart disease of the newborn.

[0057] An artificial neural network algorithm, a support vector machine algorithm, the HMM algorithm and the K-nearest neighbor algorithm are respectively built in the intelligent analysis module; and the intelligent analysis module analyzes the heart sound feature parameters and the blood oxygen feature parameters on the basis of big data analysis for congenital heart disease screening, so as to distinguish signals from a healthy person and a patient.

[0058] The congenital heart disease evaluation module is configured to analyze and evaluate a congenital heart disease screening result of the intelligent analysis module.

[0059] In this embodiment, preferably, the heart sound data processing module includes a heart sound wavelet denoising unit, an envelope extraction unit and a segmentation unit; the heart sound wavelet denoising unit performs wavelet transform on a noisy heart sound signal, processes, in a certain way, a wavelet coefficient obtained by the transform to remove noise contained in the signal, and performs inverse wavelet transform on the processed wavelet coefficient to obtain a denoised signal; the envelope extraction unit is configured to process a heart sound signal to obtain an envelope data point of the heart sound signal, and perform, on the basis of a filter, smoothing on the envelope data point of the heart sound signal; and the segmentation unit is configured to segment the heart sound signal.

[0060] In this embodiment, preferably, a blood oxygen wavelet denoising unit is arranged in the blood oxygen data processing module; and the blood oxygen wavelet denoising unit is configured to denoise a noisy blood oxygen signal.

[0061] In this embodiment, preferably, the network upgrading module includes a network connection unit and an update detection unit; the network connection unit is configured to establish a communication connection between the database and Internet big data through a network; and the update detection unit is configured to detect in real time data parameters of the congenital heart diseases of the newborns in the Internet big data.

[0062] In this embodiment, preferably, a firewall unit is arranged in the database, and the firewall unit is configured to defense hacker attack.

[0063] In this embodiment, preferably, the HMM algorithm includes a direct computing method, a forward algorithm and a backward algorithm.

[0064] In this embodiment, preferably, the database includes a data classification unit and a data inquiry unit; the data classification unit is configured to classify data in terms of familiarity; and the data inquiry unit is configured to inquire data information by means of entering key words.

[0065] In this embodiment, preferably, the intelligent analysis module further includes a heart murmur grading unit and a blood oxygen value unit; the heart murmur grading unit is configured to classify heart murmur; and the blood oxygen value unit is configured to calculate a specific numerical value of blood oxygen.

[0066] In this embodiment, preferably, each of the heart sound data processing module and the blood oxygen data processing module is provided with a signal classification unit; and the signal classification unit is configured to identify murmur and hypoxemia and establish a relationship between a two-indicator result and a corresponding congenital heart disease.

[0067] In this embodiment, preferably, the intelligent analysis module classifies the heart murmur using the Lasso algorithm and calculates a classification result; the formula of the Lasso algorithm is as follows:

[00003]$H\left(X\right)=\underset{\beta \in R^P}{\arg \min }{.Math.y-X\beta .Math.}^2+\lambda .Math.\beta .Math.$

[0068] where R is a set of all real numbers; RP represents a p-dimensional vector; each component is a real number; β is a related coefficient;

[00004]$\underset{\beta \in R^P}{\arg \min }{.Math.y-X\beta .Math.}^2$

is the least squares term; X represents an input result of each classifier; y represents a desired result; and λ represents a coefficient of regularization.

[0069] The working principle and use flow of the present disclosure are as follows:

[0070] During use of the intelligent screening algorithm and automatic upgrading system for the congenital heart diseases of newborns based on the big data, the blood oxygen and heart sound data of a newborn are collected through the heart sound data module and the blood oxygen data module; the heart sound feature parameters and the blood oxygen feature parameters are extracted respectively through the heart sound data processing module and the blood oxygen data processing module; the network upgrading module is configured to receive the heart sound feature parameters and the blood oxygen feature parameters to update in real time the internal data of the database; the heart sound feature parameters and the blood oxygen feature parameters are analyzed on the basis of big data analysis for congenital heart disease screening, so as to distinguish signals from a healthy person and a patient; and the congenital heart disease screening result is obtained by analysis by the congenital heart disease evaluation module.

[0071] Although the embodiments of the present disclosure have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present disclosure. The scope of the present disclosure is defined by the attached claims and their equivalents.