Lung disease detection by sound analysis techniques
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The document discusses various lung sounds including normal and abnormal breath sounds, detailing characteristics such as frequency, duration, and associated disorders. It explains the generation and classification of sounds like wheeze, rhonchi, and crackles, and outlines the processing steps involved in separating heart and lung sounds using microphones and signal processing techniques. References to studies and algorithms related to lung sound analysis and their applications in diagnosing respiratory conditions are also included.
![References
[1] Neeraj Sharma, Prashant Krishnan, Rohit Kumar, Shreyas Ramoji, Srikanth Raj Chetupalli, Nirmala R., Prasanta Kumar Ghosh, and Sriram Ganapathy “Coswara - A
Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis”.
[2] Malay Sarkar, Irappa Madabhavi1 , Narasimhalu Niranjan, Megha Dogra “Auscultation of the respiratory system”.
[3] Fatma Ayaria, Mekki Ksouria, Ali T. Alouan “Lung sound extraction from mixed lung and heart sounds FASTICA algorithm”.
[4] Abhishek Jain, Jithendra Vepa “Lung Sound Analysis for Wheeze Episode Detection”.
[5] Jen-Chien Chien1 ,Ming-Chuan Huang1 , Yue-Der Lin2 ,Fok-ching Chong “A Study of Heart Sound and Lung Sound Separation by Independent Component Analysis
Technique”.
[6] Chengyu Liu1, David Springer2, Qiao Li1, Benjamin Moody3, Ricardo Abad Juan4,5, Francisco J Chorro6, Francisco Castells5, José Millet Roig5, Ikaro Silva3,
Alistair E W Johnson3, Zeeshan Syed7, Samuel E Schmidt8, Chrysa D Papadaniil9, Leontios Hadjileontiadis9, Hosein Naseri10, Ali Moukadem11, Alain Dieterlen11,
Christian Brandt12, Hong Tang13, Maryam Samieinasab14, Mohammad Reza Samieinasab15, Reza Sameni14, Roger G Mark3 and Gari D Clifford, “An open access
database for the evaluation of heart sound algorithms”.
[7] Bruno M. Rocha1 , Dimitris Filos2 , Luís Mendes3,1, Gorkem Serbes4 , Sezer Ulukaya5,6, Yasemin P. Kahya6 , Nikša Jakovljević7 , Tatjana L. Turukalo7 , Ioannis M.
Vogiatzis2 , Eleni Perantoni2 , Evangelos Kaimakamis2 , Pantelis Natsiavas2 , Ana Oliveira8 , Cristina Jácome8 , Alda Marques8 , Nicos Maglaveras2,9, Rui Pedro Paiva1
, Ioanna Chouvarda2 , Paulo de Carvalh “An Open Access Database for the Evaluation of Respiratory Sound Classification Algorithms”
[8] Hong Wang1 , Le Yi Wang2 , Han Zheng3 , Razmig Haladjian4 , Meghan Wallo5 “Lung Sound/Noise Separation for Anesthesia Respiratory Monitoring”.
[9] V. Gross’, L: J. Hadjileontiadisz, Th. Penzel’, U. Koehler’, C. k‘ogelmeier’ “Multimedia Database “Marburg Respiratory Soun ds (MARS)“”.
[10] Rajkumar Palaniappan *, Kenneth Sundaraj, Nizam Uddin Ahamed “Machine learning in lung sound analysis: A systematic review”.](https://image.slidesharecdn.com/diseasedetection-201129132214/85/Lung-disease-detection-by-sound-analysis-techniques-12-320.jpg)
Lung disease detection by sound analysis techniques
- 2. Lung Sound characteristic Vesicular Breath Sound Abnormal Breath Sound Adventitous Breath Sound Tracheal (High pitch) Vesicular (Low pitch) Bronchial (High pitch) Bronchovesicular (Low pitch) Absent / Decreased Normal Breath Bronchial Sound in Abnormal Continuous Lung Sound Discontinuous Lung Sound Wheeze Dominant frequency range:- 400Hz or more Duration:- 250ms or more ; High pitch Sensor location:- any location over lung/ Trachea or most of chest wall area Disorder:- Asthma Ronchi Dominant frequency range:- 200Hz or more Duration:- 250ms or more ; Low pitch Sensor location:- any location over lung/ Trachea or most of chest wall area Disorder :- COPD ,Acute or severe Crakles/Rales Dominant frequency Range:-200 to 2000Hz Coarse ckrakles Duration:- 100ms or more ; Low pitch; Location:- anterior of posterior of chest wall Fine crakles Duration:- 100ms or more; High pitch Sensor location:- posterior chest wall
- 3. Wave form of Crackle • IDW or initial deflection width represents the duration between the beginning of the crackle and the first deflection. • 2CD (two-cycle duration) is the duration from the beginning of the crackle to the date at which the waveform did two complete cycles • It is accepted that the duration of a crackle is lower than 20 ms and the frequency range is between 100 and 200 Hz • Crackles are generated by small airways snapping open on inspiration. Therefore, they are predominantly inspiratory. The difference between the course and fine crackles is believed to come from the size of the airway snapping open (larger airways, deeper pitched, courser crackles) Wave form of Wheeze • A wheeze is a continuous, coarse, whistling sound produced in the respiratory airways during breathing. • For wheezes to occur, some part of the respiratory tree must be narrowed or obstructed (for example narrowing of the lower respiratory tract in an asthmatic attack), or airflow velocity within the respiratory tree must be heightened. • Rhonchi are rattling, continuous and low-pitched breath sounds that are often hear to be like snoring. • It is caused by thick secretions in large airways as air passes by. • Rhonchi can be heard in patients with pneumonia, chronic bronchitis, cystic fibrosis or COPD (chronic obstructive pulmonary disease). Coughing will often clear rhonchi. • They are lower in pitch than wheezes and have a snoring quality. They also have a sinusoidal pattern on waveform, but the number of deflections per unit time is less than that of wheezes as they are of lower frequency. Wave form of Rhonchi
- 4. Figure:- Location on posterior of lung to receive the most pure sound from lung Location of the Chest sound receiving devices
- 5. Lung sound Processing Steps:- 1.) Receiving of Sound from different sources. 2.) Extraction of Noise and Conversion of sound into signals/message. 3.) Processing of sound in computer in line. 4.) Separated sound of lung and heart shown on computer display. Step 1 Step 2 Step 3 Step 4
- 6. 1.)Receiving of Sound from different sources Heart sound wave Lung sound wave Noise sound wave Received sound wave contain sound from three sources:- Heart, lung and Noise 20Hz to 150Hz Noise Microphone as sound receiver Main sources of Sound 25Hz to 1500Hz
- 7. 2.)Extraction of Noise The firs section of the step is to extract the lung sound from mixture of heart sound and lung sound :- Microphone:- Receive sound Amplifier:- Amplifies sound Low pass filter Computer in line Set at cut-off of 2000Hz Output message Input message Right chest sound Left chest sound
- 8. 3.)Processing of sound in computer In line Computer Sound card Processing Algorithm Output sound signals of right chest and left chest when the noise sound waves are filtered by the low pass filter Graph of sounds produce by heart and lung separately Right chest sound Left chest sound
- 9. Graph shown on display 4.)Separated sound of lung and heart shown on computer display Lungs Heart
- 10. Sound Sample WavePad Audio Editor (Free Version) Wave Graph of Sound Sample No. of small sections of sound are created and observed that which type of waveform are formed and process in Database through which result will display on screen
- 11. Algorithm for separation of the lung sound from mixture of sound ICA:- Independent component analysis technique Let us consider two sources of signal form both lung X and X Then X (t) = A S (t) + A S (t) (1) X (t) = A S (t) + A S (t) (2) Where a are correlation coefficients, the problem is to get the original signals out of the recorded ones, but separately. So we need to identify original sources S and S using only the recorded mixed signals X and X . A more simplified writing of equations (1) and (2) is; X=A.S Where X is Mixing matrix, and S is recorded signal matrix and S, is original sound source matrix. S= WX Where W is 1/A . 1 2 1 11 1 12 2 2 21 2221 ij 1 2 1 2
- 12. References [1] Neeraj Sharma, Prashant Krishnan, Rohit Kumar, Shreyas Ramoji, Srikanth Raj Chetupalli, Nirmala R., Prasanta Kumar Ghosh, and Sriram Ganapathy “Coswara - A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis”. [2] Malay Sarkar, Irappa Madabhavi1 , Narasimhalu Niranjan, Megha Dogra “Auscultation of the respiratory system”. [3] Fatma Ayaria, Mekki Ksouria, Ali T. Alouan “Lung sound extraction from mixed lung and heart sounds FASTICA algorithm”. [4] Abhishek Jain, Jithendra Vepa “Lung Sound Analysis for Wheeze Episode Detection”. [5] Jen-Chien Chien1 ,Ming-Chuan Huang1 , Yue-Der Lin2 ,Fok-ching Chong “A Study of Heart Sound and Lung Sound Separation by Independent Component Analysis Technique”. [6] Chengyu Liu1, David Springer2, Qiao Li1, Benjamin Moody3, Ricardo Abad Juan4,5, Francisco J Chorro6, Francisco Castells5, José Millet Roig5, Ikaro Silva3, Alistair E W Johnson3, Zeeshan Syed7, Samuel E Schmidt8, Chrysa D Papadaniil9, Leontios Hadjileontiadis9, Hosein Naseri10, Ali Moukadem11, Alain Dieterlen11, Christian Brandt12, Hong Tang13, Maryam Samieinasab14, Mohammad Reza Samieinasab15, Reza Sameni14, Roger G Mark3 and Gari D Clifford, “An open access database for the evaluation of heart sound algorithms”. [7] Bruno M. Rocha1 , Dimitris Filos2 , Luís Mendes3,1, Gorkem Serbes4 , Sezer Ulukaya5,6, Yasemin P. Kahya6 , Nikša Jakovljević7 , Tatjana L. Turukalo7 , Ioannis M. Vogiatzis2 , Eleni Perantoni2 , Evangelos Kaimakamis2 , Pantelis Natsiavas2 , Ana Oliveira8 , Cristina Jácome8 , Alda Marques8 , Nicos Maglaveras2,9, Rui Pedro Paiva1 , Ioanna Chouvarda2 , Paulo de Carvalh “An Open Access Database for the Evaluation of Respiratory Sound Classification Algorithms” [8] Hong Wang1 , Le Yi Wang2 , Han Zheng3 , Razmig Haladjian4 , Meghan Wallo5 “Lung Sound/Noise Separation for Anesthesia Respiratory Monitoring”. [9] V. Gross’, L: J. Hadjileontiadisz, Th. Penzel’, U. Koehler’, C. k‘ogelmeier’ “Multimedia Database “Marburg Respiratory Soun ds (MARS)“”. [10] Rajkumar Palaniappan *, Kenneth Sundaraj, Nizam Uddin Ahamed “Machine learning in lung sound analysis: A systematic review”.