Archives of Acoustics, 47, 3, pp. 383–388, 2022
10.24425/aoa.2022.142012

Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms

Meng-Lun HSUEH
Hwa Hsia University of Technology
Taiwan, Province of China

Jin-Peng CHEN
Guangdong University of Petrochemical Technology
China

Bing-Yuh LU
Guangdong University of Petrochemical Technology
China

Huey-Dong WU
National Taiwan University Hospital
Taiwan, Province of China

Pei-Yi LIU
Guangdong University of Petrochemical Technology
China

A moving average (MA) is a commonly used noise reduction method in signal processing. Several studies on wheeze auscultation have used MA analysis for preprocessing. The present study compared the performance of MA analysis with that of differential operation (DO) by observing the produced spectrograms. These signal preprocessing methods are not only applicable to wheeze signals but also to signals produced by systems such as machines, cars, and flows. Accordingly, this comparison is relevant in various fields. The results revealed that DO increased the signal power intensity of episodes in the spectrograms by more than 10 dB in terms of the signal-to-noise ratio (SNR). A mathematical analysis of relevant equations demonstrated that DO could identify high-frequency episodes in an input signal. Compared with a two-dimensional Laplacian operation, the DO method is easier to implement and could be used in other studies on acoustic signal processing. DO achieved high performance not only in denoising but also in enhancing wheeze signal features. The spectrograms revealed episodes at the fourth or even fifth harmonics; thus, DO can identify high-frequency episodes. In conclusion, MA reduces noise and DO enhances episodes in the high-frequency range; combining these methods enables efficient signal preprocessing for spectrograms.
Keywords: differential operation; moving average; signal; lung sound; wheeze
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References

Bae W., Kim K., Yoon J.-S. (2021), Interrater reliability of spectrogram for detecting wheezing in children, Pediatrics International, 64: e15003, doi: 10.1111/ped.15003.

Bertran K., Sánchez T., Brockmann P.E. (2019), Monitoring asthma during sleep: Methods and techniques, [in:] Allergy and Sleep, pp. 175–183, Springer, Cham, doi: 10.1007/978-3-030-14738-9_14.

Carbone A., Kiyono K. (2016), Detrending moving average algorithm: Frequency response and scaling performances, Physical Review E, 93(6): 063309, doi: 10.1103/PhysRevE.93.063309.

Charbonneau G., Ademovic E., Cheetham B.M.G., Malmberg L.P., Vanderschoot J., Sovijärvi A.R.A. (2000), Basic techniques for respiratory sound analysis, European Respiratory Review, 10(77): 625–635.

Comajuncosas J.M. (2009), Expressive Breath Modeling, Master Thesis, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona.

de Cheveigné A., Nelken I. (2019), Filters: when, why, and how (not) to use them, Neuron, 102(2): 280–293, doi: 10.1016/j.neuron.2019.02.039.

Fraiwan L., Hassanin O., Fraiwan M., Khassawneh B., Ibnian A.M., Alkhodari M. (2021), Automatic identification of respiratory diseases from stethoscopic lung sound signals using ensemble classifiers, Biocybernetics and Biomedical Engineering, 41(1): 1–14, doi: 10.1016/j.bbe.2020.11.003.

Gavriely N., Palti Y., Alroy G., Grotberg J.B. (1984), Measurement and theory of wheezing breath sounds, Journal of Applied Physiology, 57(2): 481–492, doi: 10.1152/jappl.1984.57.2.481.

Hashemi A., Arabalibiek H., Agin K. (2011), Classification of wheeze sounds using wavelets and neural networks, [in:] 2011 International Conference on Biomedical Engineering and Technology, pp. 127–131, Singapore.

Haykin S., Van Veen B. (1998), Signals and Systems, pp. 72–87, Wiley, New York.

Jeeru S. (2020), Wheezing sound source separation applied to single channel respiratory audio mixtures, Master Thesis, Department of Telecommunication Engineering, Universidad de Jaén, https://hdl.handle.net/10953.1/13601.

Kumar A., Abhishek K., Chakraborty C., Kryvinska N. (2021), Deep learning and internet of things based lung ailment recognition through coughing spectrograms, IEEE Access, 9: 95938–95948, doi: 10.1109/ACCESS.2021.3094132.

Lang R., Fan Y., Liu G., Liu G. (2021), Analysis of unlabeled lung sound samples using semi-supervised convolutional neural networks, Applied Mathematics and Computation, 411: 126511, doi: 10.1016/j.amc.2021.126511.

Li J. et al. (2021), LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram, Physiological Measurement, 42(10): 105006, doi: 10.1088/1361-6579/ac27b9.

Li J., Hong Y. (2015), Wheeze detection algorithm based on spectrogram analysis, [in:] 2015 8th International Symposium on Computational Intelligence and Design (ISCID), pp. 318–322, China, doi: 10.1109/ISCID.2015.310.

Lin B.-S., Wu H.-D., Chen S.-J. (2015), Automatic wheezing detection based on signal processing of spectrogram and back-propagation neural network, Journal of Healthcare Engineering, 6(4): 649–672, doi: 10.1260/2040-2295.6.4.649.

Lu B.-Y., Hsueh M.-L., Wu H.-D. (2021), Transmission Perspective on the Mechanism of Coarse and Fine Crackle Sounds, Archives of Acoustics, 46(2): 289–300, doi: 10.24425/aoa.2021.136583.

Mendes L. et al. (2015), Detection of wheezes using their signature in the spectrogram space and musical features, [in:] 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5581–5584, doi: 10.1109/EMBC.2015.7319657.

Nguyen T., Pernkopf F. (2022), Lung sound classification using co-tuning and stochastic normalization, IEEE Transactions on Biomedical Engineering (Early Access), doi: 10.1109/TBME.2022.3156293.

Sovijärvi A.R.A., Dalmasso F., Vanderschoot J., Malmberg L.P., Righini G., Stoneman S.A.T. (2000), Definition of terms for applications of respiratory sounds, European Respiratory Review, 10(77): 597–610.

Tabata H. et al. (2018), Changes in the breath sound spectrum during methacholine inhalation in children with asthma, Respirology, 23(2): 168–175, doi: 10.1111/resp.13177.

Taplidou S.A., Hadjileontiadis L.J. (2007), Wheeze detection based on time-frequency analysis of breath sounds, Computers in Biology and Medicine, 37(8): 1073–1083, doi: 10.1016/j.compbiomed.2006.09.007.

Taplidou S.A., Hadjileontiadis L.J., Penzel T., Gross V., Panas S.M. (2003), WED: An efficient wheezing-episode detector based on breath sounds spectrogram analysis, [in:] Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), pp. 2531–2534, doi: 10.1109/IEMBS.2003.1280431.

Wang Y., Guan X., Du Y., Nan N. (2020), Harmonics based representation in clarinet tone quality evaluation, [in:] 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020), pp. 766–770, doi: 10.1109/ICASSP40776.2020.9054020.




DOI: 10.24425/aoa.2022.142012