Conclusion(2)  Different data were involved in each study, as present in table 1.  Hard to determining the most successful technique.  PSD were used.

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Presentation transcript:

Conclusion(2)  Different data were involved in each study, as present in table 1.  Hard to determining the most successful technique.  PSD were used to compared filtered data with original lung sound within and outside heart sound region is most useful [14], [21], [25], [29], [32], [35].  Strict qualitative was achieved in only one study[25] by a panel unbiased researchers listen before and after reduction, and fill out a questionnaires.

Conclusion(3)  Indeed, all studies were based on data that had been in acquired under ideal conditions, such as a quiet environment, and also in know cardiac and respiratory state, with most subjects being healthy.  In addition, heart sound cancellation studies did not account for heart sounds other than first and second sounds.

Conclusion(4)  The potential useful of any method for filtering heart sound from lung sound rests on its ability to perform clinical setting.  Future study need to focus on challenging the performance of techniques in data recording that are appropriate to clinical application in terms of environment and respiratory cardiac illness.

References(1) [1] H. Pasterkamp, S.S. Kraman, and G.R. Wodicka, “Respiratory sounds. advances beyond the stethoscope,” Amer. J. Respir. Crit Care Med., vol. 156, no.3, Pt1, pp. 974–987, Sept [2] H. Pasterkamp, R. Fenton, A. Tal, and V. Chernick, “Interference of cardiovascular sounds with phonopneumography in children,” Am. Rev. Respir. Dis., vol. 131, no. 1, pp. 61–64, Jan [3] W.K. Blake, Mechanics of Flow-Induced Sound and Vibration. Orlando, FL: Academic Press, [4] J.C. Hardin and J.L. Patterson, Jr., “Monitoring the state of the human airways by analysis of respiratory sound,” Acta Astronaut., vol. 6, no. 9, pp. 1137–1151, Sept [5] I.V. Vovk, V.T. Grinchenko, and V.N. Oleinik, “Modeling the acoustic properties of the chest and measuring breath sounds,” Acoustical Physics, vol. 41, no. 5, pp. 667–676, [6] N. Gavriely, Y. Palti, and G. Alroy, “Spectral characteristics of normal breathsounds,” J. Appl. Physiol, vol. 50, no. 2, pp. 307–314, Feb [7] I. Hossain and Z. Moussavi, “Relationship between airflow and normal lung sounds,” in Proc. 24th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc.,EMBC’02, Oct. 2002, pp. 1120–1122. [8] N. Gavriely and D.W. Cugell, “Airflow effects on amplitude and spectral content of normal breath sounds,” J. Appl. Physiol, vol. 80, no. 1, pp. 5–13, Jan [9] G.R. Manecke Jr, J.P. Dilger, L.J. Kutner, and P.J. Poppers, “Auscultation revisited: The waveform and spectral characteristics of breath sounds during general anesthesia,” Int. J. Clin. Monit. Comput., vol. 14, no. 4, pp. 231–240, Nov [10] N. Gavriely, M. Nissan, A.H. Rubin, and D.W. Cugell, “Spectral characteristics of chest wall breath sounds in normal subjects,” Thorax, vol. 50, no. 12, pp. 1292–1300, Dec

References(2) [11] A.A. Luisada, “The areas of auscultation and the two main heart sounds,” Med. Times, vol.92, pp. 8–11, Jan [12] L. Sherwood, Human Physiology: From Cells to Systems, 4th ed. Pacific Grove, CA: Brooks/Cole, [13] P.J. Arnott, G.W. Pfeiffer, and M.E. Tavel, “Spectral analysis of heart sounds: Relationships between some physical characteristics and frequency spectra of first and second heart sounds in normals and hypertensives,” J Biomed. Eng., vol. 6, no. 2, pp. 121– 128, Apr [14] J. Gnitecki, Z. Moussavi, and H. Pasterkamp, “Recursive least squares adaptive noise cancellation filtering for heart sound reduction in lung sounds recordings,” in Proc. 25th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’03, Sept. 2003, pp. 2416–2419. [15] S. Haykin, Adaptive Filter Theory, 4th ed. Upper Saddle River, NJ: Prentice-Hall, [16] A. Cohen, Biomedical Signal Processing, Volume 1: Time and Frequency Domains Analysis. Boca Raton, FL: CRC Press, [17] V.K. Iyer, P.A. Ramamoorthy, H. Fan, and Y. Ploysongsang, “Reduction of heart sounds from lung sounds by adaptive filtering,” IEEE Trans. Biomed. Eng., vol. 33, no. 12, pp. 1141–1148, Dec [published erratum appears in IEEE Trans. Biomed. Eng., vol. 35, no. 1, p. 76, Jan. 1988]. [18] V.K. Iyer, Y. Ploysongsang, and P.A. Ramamoorthy, “Adaptive filtering in biological signal processing,” Crit Rev. Biomed. Eng., vol. 17, no. 6, pp. 531–584, [19] Y. Ploysongsang, V.K. Iyer, and P.A. Ramamoorthy, “Characteristics of normal lung sounds after adaptive filtering,” Am. Rev. Respir. Dis., vol. 139, no. 4, pp. 951–956, Apr [20] V.K. Iyer, P.A. Ramamoorthy, and Y. Ploysongsang, “Quantification of heart sounds interference with lung sounds,” J. Biomed. Eng, vol. 11, no. 2, pp. 164–165, Mar

References(3) [21] Y. Ploysongsang, V.K. Iyer, and P.A. Ramamoorthy, “Inspiratory and expiratory vesicular breath sounds,” Respiration, vol. 57, no. 5, pp. 313–317, [22] M. Kompis and E. Russi, “Adaptive heart-noise reduction of lung sounds recorded by a single microphone,” in Proc. 14th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC ’92, 1992, pp. 691–692. [23] L. Yip and Y.T. Zhang, “Reduction of heart sounds from lung sound recordings by automated gain control and adaptive filtering techniques,” in Proc. 23rd Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’01, pp. 2154–2156, [24] L.J. Hadjileontiadis and S.M. Panas, “Adaptive reduction of heart sounds from lung sounds using fourth-order statistics,” IEEE Trans. Biomed. Eng., vol. 44, no. 7, pp. 642– 648, July [25] J. Gnitecki, I. Hossain, H. Pasterkamp, and Z. Moussavi, “Qualitative and quantitative evaluation of heart sound reduction from lung sound recordings,”IEEE Trans. Biomed. Eng., vol. 52, no. 10, pp. 1788–1792, Oct [26] L. Yang-Sheng, L. Wen-Hui, and Q. Guang-Xia, “Removal of the heart sound noise from the breath sound,” in Proc. 10th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’88, 1988, pp. 175–176. [27] S. Charleston and M.R. Azimi-Sadjadi, “Reduced order Kalman filtering for the enhancement of respiratory sounds,” IEEE Trans. Biomed. Eng., vol. 43, no. 4, pp. 421– 424, Apr [28] S. Charleston, M.R. Azimi-Sadjadi, and R. Gonzalez-Camarena, “Interference cancellation in respiratory sounds via a multiresolution joint time-delay and signal-estimation scheme,” IEEE Trans. Biomed. Eng., vol. 44, no. 10, pp. 1006–1019, Oct

References(4) [29] Z. Moussavi, D. Flores, and G. Thomas, “Heart sound cancellation based on multiscale products and linear prediction,” in Proc. 26th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’04, Sept. 2004, pp. 3840–3843. [30] L.T. Hall, J.L. Maple, J. Agzarian, and D. Abbott, “Sensor system for heart sound biomonitor,” Microelectronics J., vol. 31, no. 7, pp. 583–592, [31] O. Bratteli and P. Jorgenson, Wavelets Through a Looking Glass: The World of the Spectrum. Boston, MA: Birkh¨auser, [32] M.T. Pourazad, Z.K. Moussavi, and G. Thomas, “Heart sound cancellation from lung sound recordings using adaptive threshold and 2D interpolation in timefrequency domain,” in Proc. 25th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’03, 2003, pp. 2586–2589. [33] S. Charleston and M.R. Azimi-Sadjadi, “Multi-resolution joint time delay and signal estimation for processing lung sounds,” in Proc. 17th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’95, 1995, pp. 985–986. [34] L.J. Hadjileontiadis and S.M. Panas, “A wavelet-based reduction of heart sound noise from lung sounds,” Int. J. Med. Inf., Oct. 1998, vol. 52, no. 1–3, pp. 183–190. [35] I. Hossain and Z. Moussavi, “An overview of heart-noise reduction of lung sound using wavelet transform based filter,” in Proc. 25th Ann. Int. Conf. IEEE Eng. Medicine Biology Soc., EMBC’03, Sept. 2003, pp. 458–461. [36] R.R. Coifman and M.V. Wickerhauser, “Adapted waveform ‘denoising’ for medical signals and images,” IEEE Eng. Med. Biol. Mag., vol. 14, no. 5,pp. 578–586, 1995.