CN211300037U - Lung sound signal acquisition device - Google Patents

Abstract

The utility model relates to a lung sound signal acquisition device, including the collection system that lung sound sensor, amplifier, AD collection card, host computer carried on that the electricity is connected in proper order, based on the lung sound signal that the device gathered to upload to the cloud and carry out the analysis to it: receiving an original lung sound signal; decomposing the original lung sound signals, and extracting a plurality of intrinsic mode functions IMF from the original lung sound signals; after obtaining the intrinsic mode function IMF, selecting the intrinsic mode function IMF closest to the original lung sound signal, and performing Hilbert-Huang transform based on the intrinsic mode function to obtain a Hilbert time-frequency spectrogram and a Hilbert marginal spectrogram of the original lung sound signal; and analyzing the instantaneous frequency extreme value and the instantaneous frequency amplitude in the Hilbert marginal spectrogram to judge the lung condition of the tested person.

Description

Lung sound signal acquisition device

Technical Field

The utility model relates to a biomedical signal analysis technical field, in particular to lung sound signal pickup assembly.

Background

The lung is the respiratory organ of the human body and also an important hematopoietic organ of the human body. The lung sound is a sound signal generated when the lung exchanges gas with the outside, and reflects the health degree of the lung of a human body by reflecting the condition of the respiratory tract of the human body. The lung sound signals are subjected to feature extraction and analysis to judge the types of the diseases, and the method is a hot research field at present.

Common respiratory diseases can be roughly classified into: asthma, chronic obstructive pulmonary disease, chronic bronchitis, emphysema, lung cancer, cystic fibrosis, pneumonia, pleural effusion and the like. In a hospital, a doctor usually adopts a traditional stethoscope to listen to lung sounds as a basis for judging lung diseases, but the doctor is limited by factors such as the hearing condition of the doctor and medical experience, and the judgment result has objectivity. For this reason, it is desirable to introduce a digital sound analysis algorithm to perform the lung sound identification. Due to the non-linear and non-stationary characteristics of the lung sounds, the results are not ideal by using the traditional time domain analysis or frequency domain analysis method.

SUMMERY OF THE UTILITY MODEL

An object of the utility model is to improve the not enough that exists among the prior art, provide a lung sound signal pickup assembly.

In order to realize the purpose of the utility model, the embodiment of the utility model provides a following technical scheme:

a lung sound signal acquisition apparatus comprising:

the lung sound sensor is used for collecting lung sound signals;

the amplifier is used for amplifying the lung sound signals collected by the lung sound sensor;

the A/D acquisition card is used for converting the amplified lung sound signals into lung sound signals which can be identified by an upper computer;

and the upper computer is used for receiving and analyzing the lung sound signals converted by the A/D acquisition card.

The lung sound sensor is used for collecting lung sound signals of a tested person, after the lung sound signals are filtered and amplified by the amplifier, the USB is connected with the A/D collecting card to convert the lung sound signals into lung sound signals which can be identified by the upper computer, and the upper computer temporarily stores the lung sound signals and uploads the lung sound signals to the cloud for storage and analysis.

Further, in order to better realize the present invention, the model of the lung sound sensor is HKY-06F.

The lung sound signal adopts a novel polymer material micro sound sensing element to collect heart pulsation signals and other body surface artery pulsation signals, and is processed by a highly integrated signal processing circuit to output low-impedance audio signals and increase the function of resisting environmental noise.

Compared with the prior art, the beneficial effects of the utility model are that:

the utility model discloses with lung sound collection system that lung sound sensor, amplifier, AD collection card, host computer were built, its easy operation, small, conveniently gather lung sound signal, do not have any side effect to the testee.

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 block diagram of the acquisition device of the present invention;

FIG. 2 is a flow chart of the analysis method of the present invention;

FIG. 3(a) is a waveform diagram of lung sound signal of normal healthy person;

FIG. 3(b) is a waveform diagram of lung sound signals of a patient with respiratory diseases;

FIG. 4(a) is a waveform diagram of an intrinsic mode function IMF of a lung sound signal of a normal healthy person;

FIG. 4(b) is an IMF waveform of the intrinsic mode function of lung sound signals of a patient with respiratory tract diseases;

FIG. 5(a) is a Hilbert time-frequency spectrogram of a lung sound signal of a normal healthy person;

FIG. 5(b) is a Hilbert time-frequency spectrogram of a lung sound signal of a respiratory disease patient;

FIG. 6(a) is a Hilbert marginal spectrum of a lung sound signal of a normal healthy person;

fig. 6(b) is a Hilbert marginal spectrum of a lung sound signal of a respiratory disease patient;

FIG. 7 is a graphical representation of Hilbert marginal spectral mean waveforms for normal healthy persons.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. The components of embodiments of the present invention, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the accompanying drawings, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. Based on the embodiment of the present invention, all other embodiments obtained by the person skilled in the art without creative work belong to the protection scope of the present invention.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

Example 1:

the utility model discloses a following technical scheme realizes, as shown in fig. 1, a lung sound signal collection system, including the lung sound sensor of electricity connection in proper order, the amplifier, the AD collection card, the host computer, use the lung sound sensor to gather the person's sound signal of measurationing lung, hereinafter be referred to as lung sound signal for short, carry out filtering amplification through the amplifier, because the lung sound signal that the lung sound sensor was gathered is analog signal, consequently will use USB to connect the AD collection card to convert the lung sound signal from the lung sound sensor into the distinguishable digital signal of host computer, the lung sound signal after the host computer will change keeps in, upload the high in the clouds again and save and the analysis.

The lung sound sensor comprises a highly integrated signal processing circuit, has a certain environmental noise resistance function, and has the advantages of high reliability, high sensitivity, small size, convenience in use and the like. The A/D acquisition card is 16 bits, converts the original lung sound signal into a digital signal which can be identified by an upper computer, and then uses audio recording software in the upper computer to realize the waveform and data storage of the lung sound signal.

The model of the lung sound sensor is HKY-06F, and the heart pulse and other body surface artery pulse signals are acquired by adopting a novel high polymer material micro-sound sensing element and are processed by a highly integrated signal processing circuit to output low-impedance audio signals. The sensor is added with the function of resisting environmental noise on the basis of HKY-06B.

This device directly obtains effectual human lung sound signal to real signal waveform reflects the characteristic of wave form in the host computer, thereby judges the person's of being surveyed lung condition, has got rid of the artificial inaccurate influence of monitoring lung sound.

Based on the lung sound signal acquisition device, the lung sound signal acquired is analyzed, and a lung sound signal analysis method is provided, as shown in fig. 2, the method specifically includes the following steps:

step S100: the original lung sound signal is received.

The lung sound sensor collects the lung sound signal of the tested person, and the lung sound signal is filtered and amplified by the amplifier and then converted by the external A/D acquisition card.

Step S200: and decomposing the original lung sound signals, and extracting a plurality of intrinsic mode functions IMF from the original lung sound signals.

The acquired original lung sound signals are decomposed into a plurality of intrinsic mode functions IMF as shown in fig. 4, wherein fig. 4(a) is a waveform diagram of normal healthy people, and fig. 4(b) is a waveform diagram of respiratory disease patients.

Step S300: and after obtaining the intrinsic mode functions IMF, selecting one intrinsic mode function IMF closest to the original lung sound signal, and performing Hilbert-Huang transform based on the intrinsic mode function to obtain a Hilbert time-frequency spectrogram and a Hilbert marginal spectrogram of the original lung sound signal.

After the original lung sound signal is decomposed into a plurality of intrinsic mode functions IMFs in step S200, it can be determined according to the frequency that, in the intrinsic mode functions shown in fig. 4(a) or fig. 4(b), the frequencies of IMFs 1 to IMF5 are too high, which are background noise or introduced noise, the frequencies of IFMs 7 to IMF10 are too low, which belong to noise of other organs of the body, and the waveform characteristics of IMF6 conform to the waveform characteristics of the lung sound signal, so that IMF6 is selected as the closest one of the intrinsic mode functions to the original lung sound signal.

The method comprises the following steps of performing Hilbert-yellow transform based on the intrinsic mode function to obtain a Hilbert time-frequency spectrogram and a Hilbert marginal spectrogram of original lung sound signals, wherein the Hilbert time-frequency spectrogram and the Hilbert marginal spectrogram of the original lung sound signals comprise the following steps:

step S301: real signal p to the eigenmode function IMF6 closest to the original lung sound signal_i(t) performing a Hilbert-Huang transform:

defining t as an interval (- ∞ < t < + ∞);

step S302: for real signal p transformed by Hilbert-Huang_i(t) constructing its complex function z_i(t) and calculating the phase derivative thereof, wherein the constructed complex function is as follows:

z_i(t)＝p_i(t)+jH[p_i(t)]

will complex function z_i(t) is expressed in exponential form:

wherein the amplitude a_i(t) is:

phase phi_i(t) is:

further, for the phase phi_i(t) differentiating to obtain p_i(t) instantaneous frequency f_i(t)：

It can be seen that f_i(t) is a single valued function of time t, i.e. a time corresponds to a frequency and the sequence for Hilbert yellow transform must be a single component signal, thus again proving IMF6 useful.

Step S303: for real signal p_i(t) performing Hilbert time-frequency spectrum calculation:

is recorded as:

h (w, t) accurately describes the change law of the amplitude of the signal along with time and frequency.

Step S304: integration over the time axis of H (w, t) yields the Hilbert marginal spectrum:

step S305: and drawing a Hilbert time-frequency spectrogram and a Hilbert marginal spectrogram of the original lung sound signal according to the Hilbert time-frequency spectrum and the Hilbert marginal spectrum.

Passing the instantaneous frequency f in the Hilbert-time spectrum and the Hilbert-marginal spectrum_i(t) and amplitude a_i(t) and the like, a Hilbert time-frequency spectrogram and a Hilbert marginal spectrogram of the original lung sound signal can be drawn. The original lung sound signal is self-adaptive when being decomposed, so that the obtained Hilbert marginal spectrogram can accurately and intuitively reflect the actual frequency components of the signal.

Step S4: and analyzing the instantaneous frequency extreme value and the instantaneous frequency amplitude in the Hilbert marginal spectrogram to judge the lung condition of the tested person.

And analyzing instantaneous frequency extreme values and amplitudes in the obtained Hilbert marginal spectrogram, analyzing the spectrum difference of a normal testee and a typical lung disease patient, and briefly analyzing the result.

The utility model adopts hardware such as a lung sound sensor, an amplifier, an A/D acquisition card, an upper computer and the like to build a lung sound signal acquisition device, original lung sound signals acquired by the device are uploaded to a cloud for storage and analysis, when in analysis, the original lung sound signals are subjected to Hilbert-Huang transform, and clutter signals with various frequencies overlapped under the influence of internal organs are eliminated; and providing a lung sound intrinsic mode function, analyzing an intrinsic lung sound signal by using a Hilbert time-frequency spectrogram and a marginal spectrogram, and taking the frequency amplitude change of the lung sound as a judgment basis for detecting respiratory diseases.

Example 2:

acquiring lung sound signals and analyzing Hilbert-Huang transform on a plurality of experimenters with known health conditions, wherein an original lung sound signal oscillogram of a typical normal health person is shown in fig. 3(a), an original lung sound signal oscillogram of a typical respiratory disease patient is shown in fig. 3(b), Hilbert-Huang transform is respectively performed on the graphs in fig. 3(a) and 3(b), an intrinsic mode function IMF is extracted, obtained results are respectively shown in fig. 4(a) and 4(b), and Hilbert time-frequency spectrograms shown in fig. 5(a) and 5(b) are obtained, and Hilbert marginal spectrograms shown in fig. 6(a) and 6 (b).

By comparing the time-frequency spectrograms of fig. 5(a) and 5(b), it can be seen that the energy is intensively distributed at the low-frequency part along with the time change of the time-frequency spectrogram of the normal healthy person; in contrast, the time-frequency spectrum of a respiratory disease patient changes with time, and energy is dispersed in high frequency parts.

Further, the mean value of the lung sound signal of the normal healthy person is selected for analysis, fig. 7 is a waveform diagram of the mean value of the marginal spectrum of the normal healthy person, and the parameters in fig. 7 are the frequency extreme value, the amplitude value, and the area of the marginal spectrum. The results of the calculation of the marginal spectral mean of normal healthy persons are shown in table 1:

marginal spectrum analysis	Frequency of	Amplitude value	Area of
				Mean value	11.945Hz	0.366	5.757

TABLE 1

The average of the marginal spectral frequency and amplitude of normal healthy persons was (11.945Hz, 0.366), the area of the graph region was 5.757; in addition, the mean value of the marginal spectral frequency and amplitude of the respiratory disease patient is (358.313Hz, 0.031), and the area of the graph area is 0.530. The results of the calculation of the marginal spectral mean of normal healthy persons and patients with respiratory diseases are shown in table 2:

TABLE 2

Through the data comparison and analysis in table 2, the frequency corresponding to the maximum peak in the marginal spectrum of the normal lung sound signal is concentrated in the low frequency part, and the frequency corresponding to the maximum peak in the marginal spectrum of the abnormal lung sound signal is higher than that of the normal lung sound signal. The result shows that when diseases occur in the human body to form abnormal sounds in the lung, such as the sounds of the gouty, the peak value of the lung sound signal gradually shifts to a high-frequency part, the amplitude of the sounds is reduced, and the frequency domain area is reduced.

The utility model provides a lung sound signal analysis method records testee's breathing sound through lung sound signal acquisition device, utilizes sibert's transform to carry out characteristic analysis, obtains following conclusion:

the hubert time spectrum and the boundary spectrum of the lung sound signal of a respiratory disease patient may have a different condition from those of a healthy person. The energy of the lung sound signal time-frequency spectrogram of a healthy person is intensively distributed at the low-frequency part of dozens of hertz, and the energy of the lung sound signal time-frequency spectrogram of a lung disease patient is relatively dispersed and is distributed at the high-frequency part of hundreds of hertz. Therefore, the normal healthy population and the population suffering from the respiratory disease can be classified by comparing and analyzing the time-frequency spectrogram and the frequency, amplitude and area corresponding to the maximum peak value of the marginal spectrogram, so as to judge whether the testee suffers from the respiratory disease.

Other parts of the embodiment are the same as those of the above embodiment, and thus are not described again.

In order to implement the present invention, the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (2)

1. The utility model provides a lung sound signal pickup assembly which characterized in that: the method comprises the following steps:

the lung sound sensor is used for collecting lung sound signals;

the amplifier is used for amplifying the lung sound signals collected by the lung sound sensor;

the A/D acquisition card is used for converting the amplified lung sound signals into lung sound signals which can be identified by an upper computer;

and the upper computer is used for receiving and analyzing the lung sound signals converted by the A/D acquisition card.

2. The lung sound signal collecting device according to claim 1, wherein: the model of the lung sound sensor is HKY-06F.

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