4-Channel auscultation device with active noise reduction for monitoring, detecting and mapping bowel sound

Yıl 2025, Cilt: 40 Sayı: 4 , 2371 - 2380 , 31.12.2025

Halil Güvenç , Ümit Deniz Uluşar

https://doi.org/10.17341/gazimmfd.1598968

https://izlik.org/JA32RN35LW

Öz

Auscultation to and interpreting bowel sounds (BS), an indicator of bowel activity, using simple stethoscopes or microphones is a widely used method for the early, harmless, and practical detection of bowel diseases. The BS obtained through auscultation is analyzed and evaluated using signal processing techniques, yielding medically useful findings. Single-sensor designs, frequently used in recent years, are insufficient for spatial analysis of the bowel, which is spread across the abdominal region. In this study, a design was developed using four sensor microphones and one inverted microphone to perform spatial analysis of bowel activity. In this study, artificial BSs generated from the mathematical equation of the BS were played on an abdominal model using in-ear headphones, inaudible to the outside. After adaptive filtering with the inverted microphone, automatic thresholding based on the noise level, and multi-channel cross-validation, the error rates between the BS location and the reference location determined by phase transformation and stochastic region contraction were calculated. Tests were repeated at sampling frequencies of 10 kHz and 44 kHz. In our study, at a sampling frequency of 44 kHz, although the mean position error is 8.15% higher than in similar studies, its standard deviation is 25.9% lower. Consequently, we observed that BS locations were detected more consistently in our study compared to previous studies.

Anahtar Kelimeler

Acoustic mapping , bowel activity , bowel sound , multi-channel sensor array

Etik Beyan

I hereby declare that this study has been conducted in accordance with academic rules and ethical values, and that I have indicated the source of all information that does not belong to me.

Destekleyen Kurum

Akdeniz University Scientific Research Projects Coordination Unit

Proje Numarası

FBA-2020-5381

Teşekkür

This study was supported by Akdeniz University Scientific Research Projects Coordination Unit with project number FBA-2020-5381. During the preparation of the study, I would like to thank Prof. Dr. Ümit Deniz ULUŞAR, Akdeniz University for its Electrical-Electronics Engineering and Computer Engineering laboratory facilities, and my dear wife, daughter and son, my valuable elders and friends who always supported me.

Bağırsak sesinin izlenmesi, tespiti ve haritalanması için aktif gürültü azaltma özelliğine sahip 4 kanallı oskültasyon cihazı

https://izlik.org/JA32RN35LW

Öz

Bağırsak aktivitesinin bir göstergesi olan bağırsak sesinin (BS) basit stetoskoplar veya mikrofonlarla dinlenmesi ve yorumlanması, bağırsak hastalıklarının erken, zararsız ve pratik tespiti için yaygın kullanılan bir yöntemdir. Dinleme yoluyla elde edilen BS, sinyal işleme teknikleri ile analiz edilerek değerlendirilmekte ve tıbbi olarak yararlı bulgulara ulaşılmaktadır. Son yıllarda sıkça kullanılan tek algılayıcıya sahip tasarımlar, karın bölgesine yayılmış olan bağırsağın konumsal analizi için yeterli olmamaktadır. Bu çalışmada, bağırsak aktivitesinin konumsal analizlerinin yapılabilmesi için 4 algılayıcı mikrofon ve 1 ters mikrofon kullanılarak bir tasarım yapılmıştır. Çalışmada, BS’nin matematiksel denkleminden üretilen yapay BS'ler bir karın bölgesi modeli üzerinde kulak içi kulaklıkla dışardan duyulamayacak biçimde dinletilmiştir. Ters mikrofon ile adaptif filtreleme, gürültü seviyesine göre otomatik eşikleme ve çok kanallı çapraz doğrulama yapıldıktan sonra, faz dönüşümü ile yönlendirilmiş tepki gücü ve stokastik bölge daralması yöntemi ile tespit edilen BS konumu ve referans konum arasındaki hata oranları bulunmuştur. Testler, 10 kHz ve 44 kHz örnekleme frekanslarında tekrarlanmıştır. Çalışmamızda, 44 kHz örnekleme frekansında, ortalama konum hatası benzer çalışmaya oranla %8,15 daha fazla olmasına rağmen, standart sapması %25,9 oranında düşüktür. Sonuç olarak, çalışmamızda BS konumları önceki çalışmalara göre daha tutarlı tespit edildiği görülmüştür.

Anahtar Kelimeler

Akustik haritalama , bağırsak aktivitesi , bağırsak sesi , çok kanallı sensör dizisi

Etik Beyan

Bu çalışmanın, akademik kurallar ve etik değerlere uygun olarak yapıldığını belirtir, bana ait olmayan tüm bilgilerin kaynağını gösterdiğimi beyan ederim.

Destekleyen Kurum

Akdeniz Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon birimi

Proje Numarası

FBA-2020-5381

Teşekkür

Bu çalışma Akdeniz Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon birimi tarafından FBA-2020-5381 nolu proje ile desteklenmiştir. Çalışmasının hazırlanması sırasında başta Prof. Dr. Ümit Deniz ULUŞAR olmak üzere, Elektrik-Elektronik Mühendisliği ve Bilgisayar Mühendisliği laboratuvar imkanları Akdeniz Üniversitesine ve bana her zaman destek veren sevgili eşime, kızım ve oğluma, değerli büyüklerime ve arkadaşlarıma çok teşekkür ederim.

Kaynakça

• 1. Cannon, W. B., Auscultation of the rhythmic sounds produced by the stomach and intestines, American Journal of Physiology-Legacy Content, 14 (4), 339–353, 1905.
• 2. Georgoulis, B., Bowel sounds, Proceedings of The Royal Society of Medicine, 60 (9), 917–920, 1967.
• 3. Watson, W. C. and Knox, E. C., Phonoenterography: the recording and analysis of bowel sounds, Gut, 8 (1), 88–94, 1967.
• 4. Dalle, D., Devroede, G., Thibault, R., and Perrault, J., Computer analysis of bowel sounds, Computers in Biology and Medicine, 4 (3), 247–256, 1975.
• 5. Arnbjörnsson, E., Normal and pathological bowel sound patterns, Annales Chirurgiae Et Gynaecologiae, 75 (6), 314–318, 1986.
• 6. Vantrappen, G., Janssens, J., Coremans, G., and Jian, R., Gastrointestinal motility disorders, Digestive Diseases and Sciences, 31 (9 Suppl), 5S-25S, 1986.
• 7. Mansy, H. A. and Sandler, R. H., Bowel-sound signal enhancement using adaptive filtering, IEEE Engineering in Medicine and Biology Magazine: The Quarterly Magazine of the Engineering in Medicine & Biology Society, 16 (6), 105–117, 1997.
• 8. Li, M., Yang, J., and Wang, X., Research on auto-identification method to the typical bowel sound signal, 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011.
• 9. Hadjileontiadis, L. J. and Panas, S. M., On modeling impulsive bioacoustic signals with symmetric /spl alpha/-stable distributions: application in discontinuous adventitious lung sounds and explosive bowel sounds, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), 20, 1998.
• 10. Xizheng, Z., Ling, Y., and Weixiong, W., an New Filtering Methods in the Wavelet Domain for Bowel Sounds, International Journal Of Advanced Computer Science And Applications (IJACSA), 1 (5), 2010.
• 11. Hadjileontiadis, L. J., Kontakos, T. P., Liatsos, C. N., Mavrogiannis, C. C., Rokkas, T. A., and Panas, S. M., Enhancement of the diagnostic character of bowel sounds using higher-order crossings, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N), 1999.
• 12. Hadjileontiadis, L. J., Liatsos, C. N., Mavrogiannis, C. C., Rokkas, T. A., and Panas, S. M., Enhancement of bowel sounds by wavelet-based filtering, IEEE Transactions on Bio-Medical Engineering, 47 (7), 876–886, 2000.
• 13. Hadjileontiadis, L. J., Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part I: methodology, IEEE Transactions on Bio-Medical Engineering, 52 (6), 1143–1148, 2005.
• 14. Hadjileontiadis, L. J., Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part II: application results, IEEE Transactions on Biomedical Engineering, 52 (6), 1050–1064, 2005.
• 15. Ranta, R., Heinrich, C., Louis-Dorr, V., Wolf, D., and Guillemin, F., Wavelet-based bowel sounds denoising, segmentation and characterization, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001.
• 16. Ranta, R., Louis-Dorr, V., Heinrich, C., Wolf, D., and Guillemin, F., Principal component analysis and interpretation of bowel sounds, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004. 17. Sakata, O., Suzuki, Y., Matsuda, K., and Satake, T., Temporal changes in occurrence frequency of bowel sounds both in fasting state and after eating, Journal of Artificial Organs: The Official Journal of the Japanese Society for Artificial Organs, 16 (1), 83–90, 2013.
• 18. Yin, Y., Jiang, H., Feng, S., Liu, J., Chen, P., Zhu, B., and Wang, Z., Bowel sound recognition using SVM classification in a wearable health monitoring system, Science China Information Sciences, 61 (8), 084301, 2018.
• 19. Kölle, K., Fougner, A., Ellingsen, R., Carlsen, S., and Stavdahl, Ø., Feasibility of early meal detection based on abdominal sound, IEEE Journal of Translational Engineering in Health and Medicine, PP, 2019.
• 20. Huang, Y., Song, I., Rana, P., and Koh, G., Fast diagnosis of bowel activities, 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
• 21. Yin, Y., Jiang, H., Yang, W., and Wang, Z., Intestinal motility assessment based on Legendre fitting of logarithmic bowel sound spectrum, Electronics Letters, 52 (16), 1364–1366, 2016.
• 22. Emoto, T., Shono, K., Abeyratne, U. R., Okahisa, T., Yano, H., Akutagawa, M., Konaka, S., and Kinouchi, Y., ARMA-based spectral bandwidth for evaluation of bowel motility by the analysis of bowel sounds, Physiological Measurement, 34 (8), 925–936, 2013.
• 23. Kim, K. S., Seo, J. H., Ryu, S. H., Kim, M. H., and Song, C. G., Estimation algorithm of the bowel motility based on regression analysis of the jitter and shimmer of bowel sounds, Computer Methods and Programs In Biomedicine, 104 (3), 426–434 2011.
• 24. Kim, K.-S., Park, H.-J., Kang, H. S., and Song, C.-G., Awareness system for bowel motility estimation based on artificial neural network of bowel sounds, 4th International Conference on Awareness Science and Technology, 2012.
• 25. Kölle, K., Aftab, M. F., Andersson, L. E., Fougner, A. L., and Stavdahl, Ø., Data driven filtering of bowel sounds using multivariate empirical mode decomposition, BioMedical Engineering OnLine, 18 (1), 28, 2019.
• 26. Ulusar, U. D., Recovery of gastrointestinal tract motility detection using Naive Bayesian and minimum statistics, Computers in Biology and Medicine, 51, 223–228, 2014.
• 27. Longfu, Z., Yi, S., Sun, H., Zheng, L., Dapeng, H., and Yonghe, H., Identification of bowel sound signal with spectral entropy method, 2015 12th IEEE International Conference on Electronic Measurement Instruments (ICEMI), 2015.
• 28. Yin, Y., Yang, W., Jiang, H., and Wang, Z., Bowel sound based digestion state recognition using artificial neural network, 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015.
• 29. Sheu, M., Lin, P., Chen, J., Lee, C., and Lin, B., Higher-Order-Statistics-Based Fractal Dimension for Noisy Bowel Sound Detection, IEEE Signal Processing Letters, 22 (7), 789–793, 2015.
• 30. Lin, B., Sheu, M., Chuang, C., Tseng, K., and Chen, J., Enhancing Bowel Sounds by Using a Higher Order Statistics-Based Radial Basis Function Network, IEEE Journal of Biomedical and Health Informatics, 17 (3), 675–680, 2013.
• 31. Dimoulas, C., Kalliris, G., Papanikolaou, G., and Kalampakas, A., Novel wavelet domain Wiener filtering de-noising techniques: Application to bowel sounds captured by means of abdominal surface vibrations, Biomedical Signal Processing and Control, 1 (3), 177–218, 2006.
• 32. Dimoulas, C., Kalliris, G., Papanikolaou, G., and Kalampakas, A., Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: a bioacoustics application in gastrointestinal-motility monitoring, Computers in Biology and Medicine, 37 (4), 438–462, 2007.
• 33. Dimoulas, C., Kalliris, G., Papanikolaou, G., Petridis, V., and Kalampakas, A., Bowel-sound pattern analysis using wavelets and neural networks with application to long-term, unsupervised, gastrointestinal motility monitoring, Expert Systems with Applications, 34 (1), 26–41, 2008.
• 34. Dimoulas, C. A., Papanikolaou, G. V., and Petridis, V., Pattern classification and audiovisual content management techniques using hybrid expert systems: A video-assisted bioacoustics application in Abdominal Sounds pattern analysis, Expert Systems with Applications, 38 (10), 13082–13093, 2011.
• 35. Dimoulas, C. A., Audiovisual Spatial-Audio Analysis by Means of Sound Localization and Imaging: A Multimedia Healthcare Framework in Abdominal Sound Mapping, IEEE Transactions on Multimedia, 18 (10), 1969–1976, 2016.
• 36. Sakata, O. and Suzuki, Y., Optimum Unit Time on Calculating Occurrence Frequency of Bowel Sounds for Real-Time Monitoring of Bowel Peristalsis, International Journal of Signal Processing Systems, 465–468, 2016.
• 37. Kim, K.-S., Seo, J.-H., and Song, C.-G., Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds, BioMedical Engineering OnLine, 10, 69, 2011.
• 38. Ulusar, U. D., Canpolat, M., Yaprak, M., Kazanir, S., and Ogunc, G., Real-time monitoring for recovery of gastrointestinal tract motility detection after abdominal surgery, 2013 7th International Conference on Application of Information and Communication Technologies, 2013.
• 39. Öztaş, A. S., Türk, E., Uluşar, Ü. D., Canpolat, M., Yaprak, M., Kazanır, S., Öğünç, G., Doğru, V., and Canagir, O. C., Bioacoustic sensor system for automatic detection of bowel sounds, 2015 Medical Technologies National Conference (TIPTEKNO), 2015.
• 40. Türk, E., Öztaş, A. S., Uluşar, Ü. D., Canpolat, M., Kazanır, S., Yaprak, M., Öğünç, G., Doğru, V., and Canagir, O. C., Wireless bioacoustic sensor system for automatic detection of bowel sounds, 2015 19th National Biomedical Engineering Meeting (BIYOMUT), 2015.
• 41. Al-Turjman, F., Edge Computing, From Hype to Reality, Springer International Publishing, 133–144, 2019.
• 42. Güvenç, H., Wireless ECG Device with Arduino, 2020 Medical Technologies Congress (TIPTEKNO), 2020.
• 43. Du, X., Allwood, G., Webberley, K. M., Osseiran, A., Wan, W., Volikova, A., and Marshall, B. J., A mathematical model of bowel sound generation, The Journal of The Acoustical Society of America, 144 (6), EL485–EL491, 2018.
• 44. Hadjileontiadis, L. J. and Rekanos, I. T., Enhancement of explosive bowel sounds using Kurtosis-based filtering, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003.
• 45. Rekanos, I. T. and Hadjileontiadis, L. J., An iterative kurtosis-based technique for the detection of nonstationary bioacoustic signals, Signal Processing, 86 (12), 3787–3795, 2006.
• 46. Hadjileontiadis, L. J. and Rekanos, I. T., Detection of explosive lung and bowel sounds by means of fractal dimension, IEEE Signal Processing Letters, 10 (10), 311–314, 2003.
• 47. Ranta, R., Louis-Dorr, V., Heinrich, C., Wolf, D., and Guillemin, F., Automatic Segmentation and Classification of Bowel Sounds, IEEE Signal Processing Letters, 10 (8), 277, 2002.
• 48. Ranta, R., Louis-Dorr, V., Heinrich, C., Wolf, D., and Guillemin, F., Digestive activity evaluation by multichannel abdominal sounds analysis, IEEE Transactions on Bio-Medical Engineering, 57 (6), 1507–1519, 2010.
• 49. Craine, B. L., Silpa, M. L., and O’Toole, C. J., Two-dimensional positional mapping of gastrointestinal sounds in control and functional bowel syndrome patients, Digestive Diseases and Sciences, 47 (6), 1290–1296, 2002.
• 50. Delfini, A. T., Troncon, L. E. A., Baffa, O., Oliveira, R. B., and Moraes, E. R., Digital Auscultation and Processing of Abdominal Sounds, 2010.
• 51. Chien, C.-H., Huang, H.-T., Wang, C.-Y., and Chong, F.-C., Two-dimensional static and dynamic display system of bowel sound magnitude map for evaluation of intestinal motility, Biomedical Engineering: Applications, Basis and Communications, 21 (05), 333–342, 2009.
• 52. Wang, F., Wu, D., Jin, P., Zhang, Y., Yang, Y., Ma, Y., Yang, A., Fu, J., and Feng, X., A flexible skin-mounted wireless acoustic device for bowel sounds monitoring and evaluation, Science China Information Sciences, 62 (10), 202402, 2019.
• 53. Wang, G., Yang, Y., Chen, S., Fu, J., Wu, D., Yang, A., Ma, Y., and Feng, X., Flexible Dual-Channel Digital Auscultation Patch with Active Noise Reduction for Bowel Sound Monitoring and Application, IEEE Journal of Biomedical and Health Informatics, 26 (7), 2951–2962, 2022.
• 54. Guvenc, H., Ulusar, U., Ogunc, G., and Canpolat, M., Bowel sound analysis using a common vector approach, 3, 37–47, 2023.
• 55. Güvenç H., Detection and classification of bowel sounds using common vector method, Gazi University Faculty of Engineering and Architecture Journal, 39 (4), 2023-2030, 2024.
• 56. Johnson, D. H. and Dudgeon, D. E., Array Signal Processing: Concepts and Techniques, P T R Prentice Hall, 558, 1993.
• 57. Do, H., Silverman, H. F., and Yu, Y., A Real-Time SRP-PHAT Source Location Implementation using Stochastic Region Contraction (SRC) on a Large-Aperture Microphone Array, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP ’07, Honolulu, HI, USA, 2007.