In the field of engine maintenance and assurance, the technology of unit condition detection through vibration analysis is relatively mature. More and more patents and technical products have been released, proving the practical value of the technology in mechanical vibration from the application level. In medical science, signals such as heart sounds and pulses are also vibration signals in nature, in order to expand the application of the technology and explore the value of the technology in medical applications. In order to extend the application of the technology and to explore the value of the technology in medical applications, the wavelet analysis technology was used to program the Labview2022 software to implement the corresponding analysis program for the analysis of the collected physiological signals. Finally, the wavelet transform-based analysis of the physiological signals was successfully implemented. It is demonstrated that the design concept can be achieved by applying this technique, which makes it valuable in the field of physiological signal detection and analysis.
In the field of engine maintenance, the technology of vibration analysis for unit condition detection has been relatively mature, and more and more patents and technical products are available, which prove the practical value of the technology in mechanical vibration from the application level [
For the work of signal analysis and research, it is often difficult to use a single method to achieve the purpose of comprehensive and objective research, and it will show its own disadvantages when analyzing the signal of a specific condition, so here the integrated analysis method of wavelet transform is introduced in order to achieve the desired purpose [
LabVIEW2022 was used to design the system and the human heart sound signal was used as the microacoustic signal for experimental analysis.
The wavelet transform window changes with the change of frequency to adapt to it, incorporating the idea of Fourier local transform, which can highlight the characteristics of the corresponding aspects flexibly through the transform, and is more utilized for the analysis of the signal [
In view of its various excellent properties, this experiment introduces: the DWT (discrete wavelet transform) multi-resolution analysis method; the multi-resolution wavelet analysis method; and the WA resolved wavelet transform method, which are three types of methods, and the functional design diagram of the system can be seen in
A multi-resolution discrete wavelet transform (DWT) analysis can be expressed in the following concrete form [
1) ∀ ( j , k ) ∈ Z 2 , x ( t ) ∈ V j , then x ( t − 2 j k ) ∈ V j ;
2) ∀ j ∈ Z , v j ⊃ v j + 1 , ⋯ V 0 ⊃ V 1 ⊃ ⋯ v j ⊃ v j + 1 ⋯ ;
3) ∀ j ∈ Z , if x ( t ) ∈ V j , then x ( t 2 ) ∈ v j + 1 ;
4) lim j → ∞ V j = ∩ j = − ∞ ∞ V j = { 0 } ;
5) lim j → ∞ V j = C l o s u r e ( ∪ j = − ∞ ∞ V j ) = L 2 ( R ) ;
6) ∀ θ ( t ) , let { θ ( t − k ) } betheRieszbasein V 0 , k ∈ Z .
Ψ ( t ) = 2 ∑ n ∈ Z h n ϕ ( 2 t − n ) (1)
h n is the scale function. Based on this, the relationship between the scale function and the wavelet mother function can be understood by the above analysis, where g n is the coefficient derived from h n [
Ψ ( t ) = 2 ∑ n ∈ z g n ϕ ( 2 t − n ) (2)
The translational representation of Ψ ( t ) is ψ j , k ( t ) ; p L ( t ) is the translational representation of the ruler function p ( t ) , and any f ( t ) ∈ L 2 ( R ) can be decomposed and expressed as:
f ( t ) = ∑ k ∈ Z c k p L ( t ) + ∑ j < J , k ∈ Z d j , k ψ j , k ( t ) (3)
c k = ∫ R f ( t ) p L ( t ) d t ; d j , k = ∫ R f ( t ) ψ j , k ( t ) d t (4)
The resolution of the wavelet analysis is controlled by the parameter J.
The analytic wavelet transform method, abbreviated as WA, is a special case of continuous wavelet transform with complex-valued Morlet wavelets, also known as Gabor wavelets, and the desired transform model can be constructed using CWT [
ϕ 0 ( t ) = 1 σ 2 π 4 exp ( − t 2 2 σ 2 + j w 0 t ) (5)
The system can be used with a variety of types of wavelets, and this experiment uses the db02 wavelet as an example for demonstration.
The results are shown in
The difference between the normal signal and the case signal after wavelet transform analysis is already visible by comparing
The signal profiles after the discrete wavelet transform multi-resolution analysis method processing can be seen through
By comparing
The contrast between the heart sounds of a normal person and a patient with mitral stenosis after multi-resolution wavelet analysis can be seen in
Through the analysis of the above-mentioned figures, it can be seen that all the designed sub-functions can be successfully implemented, and the system can be flexibly set for detailed analysis of different processing requirements.
The micro-acoustic signal analysis system based on wavelet transform method is successfully applied to the analysis of complex physiological signals by LabVIEW2022, and achieves the expected goal, which will be designed as a part of a comprehensive analysis system for corresponding integration.
The successful application of this technology will help improve the accuracy of auscultation. It is expected that in the future, small-scale integrated physiological signal analysis devices equipped with such technology will be available, making medical diagnosis more informative and intelligent.
The authors declare no conflicts of interest regarding the publication of this paper.
Qin, H., Xin, M., Cai, Y.H. and Chen, C.H. (2023) Exploring the Application of Vibration Analysis Technology in the Medical Field. Journal of Biosciences and Medicines, 11, 210-217. https://doi.org/10.4236/jbm.2023.116017