RU2354285C1 - Acoustic spectral analysis of obstructive pulmonary diseases - Google Patents

Abstract

FIELD: medicine.

SUBSTANCE: invention refers to medicine, namely to pulmonology. Breathing samples (patterns) are formed for healthy persons and patients suffering from obstructive pulmonary diseases to ensure comparative acoustically-spectral analysis of respiratory sounds. The following parametres are estimated: acoustic equivalent of respiratory muscular work (ARW) in various frequency ranges: ARW0 - 200-1200 Hz, ARW1 - 1200-12600 Hz, ARW2 - 5000-12600 Hz, ARW3 - 1200-5000 Hz. Factors K1, K2, K3 are calculated: K1=ARW1/ARW0×100, K2=ARW2/ARW0×100, K3=ARW3/ARW0×100; ΔK, corresponding to gain K factors, namely ΔK = K of forced expiration - K of quiet breathing/ K of quiet breathing ×100; factor gain index (FGI) = ΔK2/ΔK3. If ARW1 and ARW3 in quiet breathing exceed 100 mJ; K1 and K3 exceed 15; ΔK1 and ΔK3 are 200% and less, and FGI is 2 and more, obstructive disorders of external respiration function are diagnosed. Breathing samples formed for healthy persons and patients suffering from obstructive pulmonary diseases within the method allow for comparative acoustic spectral analysis of respiratory sounds.

EFFECT: analysis of respiratory sounds used as efficiency criterion of applied therapy, improves treatment quality.

2 cl, 2 tbl, 2 ex

Description

The invention relates to medicine, namely to pulmonology.

In the literature there are descriptions of various methods for studying respiratory noise. For example, pneumophonography is a study of respiratory noise with the determination of the amplitude and frequency of the spectrum in parallel with the severity of the bronchopulmonary process (Kraman S. et al., 1995, Schreur H. et al., 1994), which uses computer-aided spectral analysis of sound parameters using sensory sensors on the trachea and chest wall (Gavriely N., 1995). Tussofonobarography - analysis of the sound of a cough push (Provotorov V.M. et al. 2003), tracheophonography - analysis of noise over the trachea that occurs when performing a forced expiratory maneuver (Pasterkamp H. et al., 1996, Aeries J. et al., 2000) . In Europe, a research project is underway to standardize computer analysis of pulmonary sounds (CORSA - Computerized Respiratory Sound Analysis). Chilean scientists (Sanchez J., Vizcaya S. 2003) obtained spectra of patterns of tracheal and pulmonary sounds, which, according to the authors, will allow us to compare normal patterns with those of patients with restrictive and obstructive diseases. Cavriely N., Nissan M. et al. (1995) recorded breath sounds in 353 healthy subjects from the surface of the chest for comparison with pathological sounds in various diseases.

Criticism of analogues

1. Most studies are conducted in the range of 1-2 kHz, which is unreasonable, in our opinion, narrows the range of frequencies used for acoustic analysis. The following classification of the frequency range of respiratory sounds is proposed: low - up to 100 Hz, medium - 200-600 Hz, high 600-1200 Hz. It was noted that in the low sound range, heart murmur is superimposed and therefore this spectrum of the range should be filtered to assess pulmonary sounds (Pasterkamp H., Gavriely N.).

2. The equipment used (for example, contact accelerometers) is expensive and fragile. In addition, they give an internal resonance in the range close to the frequencies of pulmonary sounds (Pasterkamp H.).

3. In a number of works, tracheal and pulmonary sounds patterns were obtained when examining small groups of patients (17 people in the work of Chilean scientists) or one-time recording of breathing sounds from the surface of the chest (Gavriely N. et al.). Moreover, the “noise” of the working heart, the different thickness of the chest wall in various patients significantly affect, according to the authors themselves, the research results.

4. There is no quantitative assessment of acoustic phenomena. The research results are descriptive.

Prototype of the invention

The analysis of time and frequency characteristics of the spectrum of respiratory noise arising from a change in the diameter of the airways (VP), and formed the basis of the method of bronchophonography (BFG). The method is based on registration (scanning) of the respiratory cycle in order to detect specific acoustic signs of changes in the respiratory tract. The method was developed for use in the diagnosis of pulmonary diseases (Malyshev BC et al. Method for recording respiratory sounds caused by bronchopulmonary pathology in children. RF patent No. 5062396, А61В 5/08. Publish. 06/27/95. Bull. N 18) - prototype .

Parameters evaluated using BFG:

1. The duration of inhalation and exhalation.

2. The duration of the respiratory cycle.

3. The instant spectrum of the breathing process with integration in three frequency ranges (0.2-12.6 kHz, 1.2-5.0 kHz, 5.0-12.6 kHz).

4. The acoustic equivalent of breathing (ARD), which is the total integral characteristic, which is a quantitative estimate of the energy costs of the bronchopulmonary system to excite a specific acoustic phenomenon throughout the entire respiratory cycle or its individual phase, is calculated as the area under the curve on the BFG in the time domain, unit measurements are millijoules (mJ).

Computer-diagnostic complex (CDC) “Pattern” consists of a sensor and an analog-to-digital converter (built-in board for a personal computer).

The principle of operation of the “Pattern” is based on recording and subsequent evaluation of the amplitude-frequency characteristics of respiratory noise and allows visualizing and objectively evaluating the sound characteristics of breathing, which are often not detected during physical examination.

A sensor equipped with a special mouthpiece is placed in the patient's oral cavity (nasal breathing is blocked by a clamp), the sensor element is directed towards the larynx. The procedure for recording respiratory noise is performed in a sitting position with calm breathing for a short period of time 5-7 seconds three times. Direct registration of respiratory noise is carried out using a sensor with high sensitivity in a wide band of sensing frequencies (including those frequencies that are not fixed when listening with a traditional phonendoscope) 0.2-12.6 kHz. The hardware of the complex includes a set of special filters designed to form the frequency spectrum, which contains information about specific acoustic phenomena that occur during the respiratory cycle. In order to eliminate the masking effect of cardiac noises due to the work of the heart, special low-pass filters are applied and the respiratory cycle is scanned in the frequency range from 200 to 12,600 Hz. The results of computer processing of the scanning data are displayed on the computer screen in the form of a multitude of equidistant instantaneous spectra forming a three-dimensional “state surface” that displays specific acoustic phenomena. The graphic image of the bronchophonogram thus obtained is called the “breathing pattern” (PD) (Malyshev BC “Scientific method of processing information in the acoustic diagnosis of the influence of the working environment on human health.” Abstract of dissertation ... Dr. Biol. Sciences. - Tula, 2002 )

For acoustic monitoring of the patient’s breathing, an individual scan channel is included in the configuration (through the headphones, the doctor can hear the patient’s breathing and compare with the results of computer processing). The diagnostic complex, along with the hardware designed to register a specific acoustic effect that occurs when the air flows through the airspace, includes a package of application programs:

- Pattern - processing and visualization of the results of the registration of specific acoustic signals;

- Pattern Analyzer - data processing for calculating quantitative indicators characterizing the respiratory cycle: ARD - the acoustic equivalent of respiratory muscles in different frequency ranges (ARD0 - 0.2-1.2 kHz - “zero” or basic range, ARD1 - 1.2 -12.6 kHz - the general range, ARD2 - 5.0-12.6 kHz - the high-frequency range, ARD3 - 1.2-5.0 kHz - the low-frequency range), expressed in mJ. K - coefficient reflecting the same parameters in relative units: K1 = ARD1 / ARD0 × 100 - reflects the entire frequency spectrum, K2 = ARD2 / ARD0 × 100 - high-frequency range, K3 = ARD3 / ARD0 × 100 - low-frequency range.

The Committee on New Medical Technology of the Ministry of Health of the Russian Federation (protocol No. 4 of the meeting of the Commission on Devices, Devices and Instruments of April 28, 2000), the computer diagnostic complex is recommended for production and use in medical practice.

Prototype criticism

1. Studies were conducted among children from 2 to 14 years old.

2. There are no breath patterns (“averaged” patterns) of healthy and sick with various lung diseases.

3. The developed model for differential diagnosis and attributing the pattern of respiration to a specific nosological form of bronchopulmonary disease is based on the criterion of proximity of centroids in the form of a generalized measure of distance.

The purpose of the invention: the creation of a method for the acoustic analysis of obstructive pulmonary disease. The goal is realized by forming breathing patterns of healthy individuals and patients with obstructive pulmonary diseases to conduct a comparative acoustic spectral analysis of respiratory sounds.

SUMMARY OF THE INVENTION

The invention consists in the following.

The developed method for the acoustic analysis of obstructive pulmonary diseases is based on the formation of breathing patterns (samples) of healthy individuals and patients with obstructive pulmonary diseases for conducting a comparative acoustic spectral analysis of respiratory sounds. The term “pattern” is mentioned by us for the convenience of conducting a comparative analysis with the prototype. In the prototype, the term "pattern" is used. In the present invention, we use the term "sample". Designed samples are compared with individual patient acoustic samples, or patterns, as they can be called in the medical literature.

Samples of individual acoustic breathing sounds (the so-called acoustic portrait of breathing) were formed as follows.

We examined 108 healthy individuals (50 men and 58 women without complaints from the respiratory system, who had normal indicators of external respiration function), 166 patients (85 men and 81 women) with chronic obstructive pulmonary diseases (COPD): 91 of them had bronchial asthma ( BA), 62 - chronic obstructive bronchitis (COPD) and 13 patients with a combination of symptoms of these diseases. All patients had obstructive respiratory dysfunction. Bronchophonography was performed on the computer-diagnostic complex “Pattern” (recording of respiratory sounds in the mode of calm and forced breathing and subsequent mathematical processing based on the apparatus of fast Fourier transform with the result of a quantitative assessment of the acoustic work of breathing). More than 1000 bronchophonograms of calm and forced breathing were analyzed.

The following parameters were evaluated:

- ARD in various frequency ranges (ARD0 - 200-1200 Hz - “zero” or basic range, ARD1 - 1200-12600 Hz general, ARD2 - 5000-12600 Hz - high-frequency, ARD3 - 1200-3000 Hz - low-frequency ranges, respectively, expressed in millijoules)

- K - coefficient reflecting the same parameters in relative units (K1 = ARD1 / ARD0 × 100 reflects the entire frequency spectrum, K2 = ARD2 / ARD0 × 100 - high-frequency range, K3 = ARD3 / ARD0 × 100 - low-frequency range),

- ΔK - an increase in the indicators of the coefficients K (defined as K force - K spock / K spock × 100),

- IPC - growth index K, i.e. ΔК2 / ΔК1 ratio.

In the statistical processing of the material, nonparametric criteria were used, since the distribution of ARD and K indices was different from normal. For the characteristics of the variation, the median (Me) was calculated, its confidence limits (25 and 75 percentiles). The statistical significance of the differences between the BFG indices in the compared groups was evaluated according to the Mann-Whitney (M-U) criteria.

The results are shown in tables 1, 2.

As we can see (table 1), the indicators of ARD1, ARD3, K1, K3 (i.e., over the whole spectrum and its low-frequency part) in healthy individuals and patients with COPD significantly (p <0.05) differ in the mode of calm breathing . In forced expiratory mode, these differences are even more pronounced and are already observed in all ranges of ARD and K. This does not contradict clinical practice (we recall how, with scanty auscultatory data, we ask the patient to breathe deeply and forcefully and often listen to wheezing where they were not with calm breathing).

ΔK (table 2) of healthy individuals in all ranges is almost the same and exceeds 400% (median), and in patients with COPD in the high-frequency range (K2) there is a significant increase comparable to that of healthy individuals. But in the low-frequency range and throughout the spectrum, the indicators are significantly lower (164.9% and 185.9% in the median, respectively), that is, the so-called "Disruption of turbulence." This allows you to enter the so-called “Growth index" K (IPC), i.e. ΔК2 / ΔК1 ratio, which is significantly different in the studied groups (see Table 2): the confidence interval at the 25th and 75th percentiles was 0.35-1.76 in the group of healthy individuals (Me = 0.86). In patients with COPD 1.16-5.04 (Me = 2.58). That is, the IPC indicator is less than 2, with a high degree of reliability, may indicate the absence of obstructive disturbances in the function of external respiration. The accuracy of the diagnosis is improved with a comprehensive accounting of the indicators of ARD and K. Summarizing all of the above, we offer the following algorithm for the diagnosis of obstructive pulmonary diseases:

1. Indicators ARD1 and ARD3 more than 100 mJ (in the mode of calm breathing). If it is possible to perform forced expiration, the same indicators (more than 900 mJ) can be used as additional criteria.

2. Indicators K1, K3 more than 15 with calm breathing and an additional criterion of more than 50 with forced.

3. ΔК1 and ΔК3 less than 200%.

4. IPC 2 and more.

Thus, on the basis of the foregoing, a new method for the diagnosis of obstructive pulmonary diseases is proposed, based on a comparative analysis of breathing patterns (acoustic spectral analysis of pulmonary sounds) according to the developed criteria: ARD, K, ΔK, IPC.

An example of a specific implementation of the method

1. Patient O (medical history No. 218). Received with complaints of shortness of breath, cough, sometimes "whistles" in the chest at night. In 2003, she was treated inpatiently with a diagnosis of chronic obstructive bronchitis. In January 2006, after a cold, the temperature rose to 39 degrees, a strong cough. She was treated on an outpatient basis with antibiotics for an exacerbation of COPD, she was sent to a stationary examination with the same diagnosis. On examination: in the blood of white blood cells

- 5.0 × 10 ⁹ / l, ESR - 10 mm / hour, the absolute number of blood eosinophils - 0.250 × 10 ⁹ / l, mucous sputum, white, L - single in the field of view, VK (-), chest fluoroscopy - focal-infiltrative changes were not detected. The vascular pattern is reinforced. Consultation of ENT - allergic rhinosinusitis. FVD - VEL 2.92 L (99.26% of the due), VVF 2.85 L (102.08%), FEV1 - 1.82 L (76.43%), FV1 / VVF 63.76% (74 ,9%). After the test with salbutamol, FEV1 increased by 25%. Conclusion FVD: bronchial obstruction is moderate. A positive test with salbutamol. The patient is exposed to a preliminary diagnosis: Bronchial asthma, first detected. Coughing flow? Anamnestic and objective data for COB have not been identified. The patient underwent BFG. The following indicators were obtained: ARD1 - 46.6 / 685.1 mJ, ARD2 - 3.7 / 58.2 mJ, ARD3 - 42.9 / 626.9 mJ, K1 - 6.45 / 30.26 (ΔK1 - 369 %), K2 - 0.52 / 2.57 (ΔK2 - 394.2%), K3 - 5.93 / 27.69 (ΔK3 - 367%). IPC = 1.1.

Thus, the BFG indices did not correspond to the data of the COPD patient group, which was correlated with the minimal changes in the VFD of this patient, auscultatory symptoms (vesicular breathing, single dry rales). These data also did not confirm the diagnosis of the referring institution (COB) and differed from the indicators characteristic of clinically expressed AD (not excluding, possibly, a mild degree of AD).

2. Patient I. (medical history No. 199). The clinical diagnosis is severe asthma, an exacerbation phase. In the lungs during auscultation, there are many scattered dry wheezing, mainly on exhalation. Examination data: general blood test L - 4.9 × 10 ⁹ / l, ESR - 11 mm / hour, eos. - 3% (absolute amount - 0.149 × 10 ⁹ / l), chest fluorography - strengthening of the root pattern, focal-infiltrative changes were not detected. FVD: VEL 3.2 L (64.7% of the due), VVF 2.88 L (60.2%), FEV1 - 1.59 L (40.53%), FV1 / VVF 55.23% (67 , 33%). After the test with berotek, there is an increase in FEV1 by 66.7%. Conclusion FVD: Significant reduction in ventilation capacity of the lungs due to ventilation disorders of the obstructive-restrictive type. Bronchial obstruction is pronounced. Positive test with berotek.

The patient underwent BFG. The following indicators were obtained: ARD1 - 133.5 / 1328.8 mJ, ARD2 - 9.2 / 172 mJ, ARD3 - 124.3 / 1156.7 mJ, K1 - 17.47 / 53.66 (ΔK1 - 207.2 %), K2 - 1.2 / 6.95 (ΔK2 - 479.2%), K3 - 16.27 / 46.71 (ΔK3 - 187.1%). IPC = 2.3.

Thus, BFH indices indicated obstructive pulmonary pathology.

Features of the invention, distinctive from the prototype

1. Studies were conducted among adults (18 years of age and older).

2. Respiratory samples (“acoustic portraits”) of healthy individuals and patients with obstructive pulmonary diseases (BA, COPD) were created.

3. An algorithm has been developed for the diagnosis of obstructive pulmonary diseases based on a comparative analysis of the samples of the subject and the created samples according to the developed criteria: ARD, K, ΔK, IPC.

The beneficial effect of the invention

The results obtained allow us to hope that the objective characterization of respiratory sounds will improve understanding of pathological processes in the lungs. Computer analysis of pulmonary sounds will help practitioners diagnose lung diseases. The formation of breathing patterns of healthy individuals and patients with obstructive pulmonary diseases allows a comparative acoustic spectral analysis of respiratory sounds. An analysis of respiratory sounds, used as a criterion for the effectiveness of the therapy, improves the quality of treatment.

Information sources

1. Kraman SS., Wodicka GR, Oh Y et al. Measurement of respiratory acoustic signals. Effect of microphone air cavity width, shape and venting Chest 1995; 108; 1004-1008.

2. Schreur HJ., Vanderchoot J., Zwinderman AN et al. Abnormal lung sounds in patient with asthma during episodes with normal lung function. 1994; Chest 106, 91-99.

3. Gavriely N., Nissan M., Rubin AE et al. Spectral characteristics of chest wall breath sounds in normal subjects. Thorax 1995; 50: 1292-1300.

4. Provotorov V.M., Semenkova G.G. The study of bronchial obstruction in bronchial asthma using tussofonobarografii. In the book. 13 National Congress on Respiratory Diseases, St. Petersburg, 2003, p.307.

5. Pasterkamp H., Powell, RE, Sanchez I. Lung sounds spectra at standardized air flow in normal infants, children and adults. Am. J. Respir. Crit. Care Med. 1996; 154: (2, p1): 424-430.

6. Aeries JE., Cheethman BM. Current methods used for computerized respiratory sound analysis. Eur. Respir. Rev. 2000; 10 (77): 586-590.

7. Sanchez J., Vizcaya C. Tracheal and lung sounds repeatability in normal adults. Resp. Med. 2003; 97: 1257-1260.

8. RF patent No. 5062396. A method of registering respiratory sounds. / Malyshev B.C., Ardashnikova S.N., Kaganov S.Yu. et al. Bull. invented. 1995; No. 18 is a prototype.

9. Malyshev B.C. The scientific method of information processing in acoustic diagnostics of the influence of the working environment on human health: Abstract. diss. ... Dr. Biol. sciences. - Tula, 2002 .-- 45 p.

Table 1 Indicators of acoustic work of respiration (mJ)
Calm breathing Indicators ARD0 ARD1 ARD2 ARD3 K1 K2 K3 Rear About Rear About Rear About Rear About Rear About Rear About Rear About n 108 166 108 166 108 166 108 166 108 166 108 166 108 166 Me 676.1 594.01 45.77 95.48 4.69 3,7 38.73 89.47 7.17 15.73 0.61 0.56 6.43 15.1 25 percentile 247.68 305.27 18.88 37.52 2.01 1.44 16,43 33.90 5.00 9.56 0.43 0.40 4.35 8.54 75 percentile 1221.8 1327.5 117.0 218.6 8.6 11.6 110.3 209.5 11.3 25,4 1.00 1.10 10.3 23.3 Crete MU 0.384 0,000 0.855 0,000 0,000 0.429 0,000 * Crete M-U - statistical significance of the difference between the indicators of BFG in the compared groups according to the Mann-Whitney criteria Forced breathing Indicators ARD0 ARD1 ARD2 ARD3 K1 K2 K3 Rear About Rear About Rear About Rear About Rear About Rear About Rear About n 108 154 108 154 108 154 108 154 108 154 108 154 108 154 Me 1626.6 1789.9 706.4 874.6 53.7 78.5 641.7 793.5 42.1 49.8 3,59 4.30 38.9 46.9 25 percentile 1223.66 1391.9 456.7 631.3 31,0 47.3 418.9 563.6 29.6 32,0 2.01 2.78 26.9 29.8 75 percentile 1865.7 2246.0 956.0 1247 97.0 130 860.7 1100 56.7 68.7 5.27 6.80 52,2 63.7 Crete MU 0.001 0,000 0.001 0,000 0.018 0.017 0,021 • - the number of patients is less than with calm breathing (see table 1), because not all patients could perform a forced maneuver

table 2 The increase in indicators of "work of breathing" when performing forced expiration (in%) and the determination of IPC Indicators ΔK1 ΔK2 ΔК3 IPC Rear About Rear About Rear About Rear About n 108 154 108 154 108 155 108 154 Me 406.84 185.9 411.46 515.67 430.01 164.9 0.86 2,58 25 percentile 227.14 73.42 147.57 241.08 239.18 64.36 0.35 1.16 75 percentile 830.9 432.0 825.57 1033.8 853.29 401.6 1.76 5.04 Crete MU 0,000 0.017 0,021 0,000

Claims (2)

1. A method for diagnosing obstructive disturbances in the functions of external respiration by conducting bronchophonography and registering the respiratory cycle, characterized in that the following parameters are evaluated: the acoustic equivalent of the respiratory muscles (ARD) in different frequency ranges: ARD0 - 200-1200 Hz, ARD1 - 1200-12600 Hz, ARD2 - 5000-12600 Hz, ARD3 - 1200-5000 Hz; the coefficients K1, K2, K3 are calculated: K1 = ARD1 / ARD0 × 100, K2 = ARD2 / ARD0 × 100, K3 = ARD3 / ARD0 × 100; ΔK, corresponding to the increase in the indicators of the coefficients K, namely ΔK = K forced expiration - K calm breathing / K calm breathing × 100; coefficient growth index (IPC) = ΔК2 / ΔК3 and with values in the quiet breathing mode: ARD1 and ARD3 more than 100 mJ; K1 and K3 more than 15; ΔК1 and ΔК3 less than 200% and IPC 2 or more diagnose obstructive disturbances in the functions of external respiration. 2. The method according to claim 1, characterized in that it additionally determines the indicators of ARD1 and ARD3, K1 and K3 with forced expiration and with a value of ARD1 and ARD3 of more than 900 mJ .; K1 and K3 more than 50 are diagnosed with obstructive dysfunction of external respiration.