Towards an ARM based low cost and mobile biomedical device test bed for improved multi-channel pulmonary diagnosis

In this paper, we present our research results towards developing an ARM9 based biomedical device for multi-channel pulmonary diagnosis. There is a vast literature on analysis of pulmonary sounds, and related diagnosis procedures. Most of the reported results are based on using either desktop sized or handheld sized computers. Compared to these, the system proposed in this paper has several advantages, which include being low cost, low power, mobile, and compact. The proposed ARM9 based system has analog and digital subsystems. Analog front end, including filtering stages, overall computer system architecture, and software development procedures are described in detail. To assess the computational capabilities of the system, FFT benchmark tests are done, and performance of the system is analyzed.

[1]  P Caminal,et al.  Analysis of tracheal sounds during forced exhalation in asthma patients and normal subjects: bronchodilator response effect. , 1999, Chest.

[2]  Antoni Homs-Corbera,et al.  Time-frequency detection and analysis of wheezes during forced exhalation , 2004, IEEE Transactions on Biomedical Engineering.

[3]  E. H. Dooijes,et al.  Asthmatic airways obstruction assessment based on detailed analysis of respiratory sound spectra , 2000, IEEE Transactions on Biomedical Engineering.

[4]  R. Jane,et al.  Monitoring of wheeze duration during spontaneous respiration in asthmatic patients , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[5]  E. H. Dooijes,et al.  Classification of Asthmatic Breath Sounds: Preliminary Results of the Classifying Capacity of Human Examiners versus Artificial Neural Networks , 1999, Comput. Biomed. Res..

[6]  R. Jane,et al.  Spectral analysis of respiratory sounds to assess bronchodilator effect in asthmatic patients , 1998, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286).

[7]  E Ademovic,et al.  Wheezing Lung Sounds Analysis with adaptive local trigonometric transform. , 1998, Technology and health care : official journal of the European Society for Engineering and Medicine.

[8]  Ignacio Sánchez,et al.  Response to bronchodilator in infants with bronchiolitis can be predicted from wheeze characteristics , 2005, Respirology.

[9]  P Piirilä,et al.  Averaged and time-gated spectral analysis of respiratory sounds. Repeatability of spectral parameters in healthy men and in patients with fibrosing alveolitis. , 1996, Chest.

[10]  Zahra Moussavi Vocal noise cancellation from respiratory sounds , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  R. Jane,et al.  Algorithm for time-frequency detection and analysis of wheezes , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[12]  L Pesu,et al.  Classification of respiratory sounds based on wavelet packet decomposition and learning vector quantization. , 1998, Technology and health care : official journal of the European Society for Engineering and Medicine.

[13]  Raimon Jané,et al.  Detection of wheezing during maximal forced exhalation in patients with obstructed airways. , 2002, Chest.

[14]  P Helistö,et al.  A new method for automatic wheeze detection. , 1998, Technology and health care : official journal of the European Society for Engineering and Medicine.

[15]  A. Dittmar,et al.  The relationship between normal lung sounds, age, and gender. , 2000, American journal of respiratory and critical care medicine.