DOI: http://dx.doi.org/10.18203/issn.2454-5929.ijohns20180722

Dyadic wavelet analysis and detection of sinusitis using near infrared sensor

S. Kamatchi, M. Sundararajan

Abstract


Background: Sinusitis is a chronic infection or inflammation which affects the paranasal sinus cavities and the associated nasal cavities. As the symptoms of sinusitis greatly resemble upper respiratory infections, diagnosing sinusitis clinically is a major issue. Though imaging techniques serves as a standard in confirming the diagnosis of chronic sinusitis, the availability at the primary care settings, affordability and diagnosing acute cases calls upon an alternative technique in practice. Recent researches confirming the diagnosis of sinusitis using Near-infrared imaging gives us hope in taking up the research using optical sensing. The objective of the study was to successfully diagnose sinusitis using NIR-LED optical sensor and to signal process the data obtained from the patients using Dyadic Wavelet Transform (DyWT) to confirm and to validate diagnosis using regression analysis. The study also correlates the plain radiographic findings with the NIR device sensing to make the device feasible.

Methods: This was a one year pilot study (June 2014–May 2015) conducted with forty patients suspected of sinusitis and with clinical history along with ten healthy individuals as controls.  

Results: Patients age ranged from 18-65 years were included in the study. Results from NIR sensing device well correlate with the radiographic examination of the registered candidates. The regression result perfectly matches with the dyadic wavelet results of the patients, confirming the diagnosing of sinusitis using near- infrared sensor. Radiographic examination well correlates with the results from the NIR diagnostic device providing a valuable evidence of the hardware.

Conclusions: NIR-LED sensor device can provide qualitative evidence in differentiating the mild and severe patients based on air-fluid level present in the sinus. The results strongly recommend that NIR sensing device can be a best alternative in case of frequently sinus affected patients and for the unaffordable patients without the risk of radiation.


Keywords


Sinusitis, Dyadic wavelet transform, Near infrared sensor, Regression analysis, Plain radiography, NIR-LED sensor, NIR sensing

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References


Lanza DD, Kennedy DD. Adult rhinosinusitis defined. Otolaryngol Head Neck Surg. 1997;117:S1–7.

Fokkens W, Lund V, Mullol J. European position paper on Rhinosinusitis and Nasal polyps. Rhinology. 2007;45(20):1-139.

Jarvis D, Newson R, Lotvall J, Hastan D, Tomassen P, Keil T, et al. P: Asthma in adults and its association with chronic rhinosinusitis: the GA2LEN survey in Europe. Allergy. 2012;67(1):91–8.

Gulliford MC, Dregan A, Moore MV, Ashworth M, van Staa T, McCann G, et al. Continued high rates of antibiotic prescribing to adults with respiratory tract infection: survey of 568 UK general practices. BMJ Open. 2014;4(10):1-5.

Bell BG, Schellevis F, Stobberingh E, Goossens H, Pringle M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect Dis. 2014;14:13.

Aalᴓkken TM, Hagtvedt T, Dalen I. Conventional sinus radiography compared with CT in the diagnosis of acute sinusitis. Dentomaxillofac Radiol. 2003;32:60-2.

Gujrathi PT, Wakode. Haziness in X-Ray Paranasal Sinus Water’s view in Sinusitis: A Fact or Fiction. Indian J Otolaryngol Head Neck Surg. 2013;65(2):S242-6.

Chandrasekhar Y, Shaw LJ, Narula J. Diagnostic Imaging, radiation exposure and carcinogenic risk. JACC: Cardiovascular imaging. 2015;8(8):885-7.

Mahmood U, Cerussi A, Dehdari R, Nguyen Q, Kelley T, Tromberg B, et al. Near Infrared Imaging of the sinuses: Preliminary evaluation of a new technology for diagnosing maxillary sinusitis. J Biomed Opt. 2010;15(3):0360111-5.

You SJ, Niguel L, Cerussi A, Santa R. et al. Near- Infrared Imaging for Diagnosis of sinusitis. Pub. No.US 2014/0221843A1. 2014.

You SJ, Cerussi EA, Kim JH, Ison S, Wong B, Cui H, et al. Near-Infrared imaging for management of chronic maxillary sinusitis. 9314. Proc. SPIE 9314, Optics and Biophotonics in Low-Resource Settings. 2015: 93140D.

Galyanin V, Melenteva A, Bogomolov A. Selecting optimal wavelength intervals for an optical sensor: A casestudy of milk fat and total protein analysis in the region 400–1100 nm. Sens. Actuators B Chem. 2015;218:97–104.

Mansfield CD, Attas EM, Gall RM. Evaluation of static thermal and near- infrared hyperspectral imaging for the diagnosis of acute maxillary rhinosinusitis. J Otolaryngol. 2005;34:99-108.

Bogomolov A, Ageev V, Zabarylo U, Usenov I, Schulte F, Kirsanov D, et al. LED-based near infrared sensor for cancer diagnostics. Proc SPIE. 2016:9715.

Bogomolov A, Zabarylo U, Kirsanov D, Belikova V, Ageev V, Usenov I, et al. Development and Testing of an LED-Based Near-Infrared Sensor for Human Kidney Tumor Diagnostics. Sensors. 2017;17:1914.

Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal Mach Intell.1992;14:710-32.

Sun YK. Lifting construction of spline dyadic wavelet filters with any number of vanishing moments. Int. J. Wavelets Multiresolution. Information Processing. 2009;7:693-710.

Sangeetha P, Hemamalini S. Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor. IET signal processing. 2017;1(5):604-12.

Eli O. Meltzer, Daniel L. Hamilos. Rhinosinusitis Diagnosis and Management for the Clinician: A Synopsis of Recent Consensus Guidelines. Mayo Clin Proc. 2011;86(5):427-43.

Kamatchi S, Sundararajan M. Earlier diagnose of sinusitis in Frontal and Maxillary cavities using NIR radiations. J Computational Theroetical NanoscI. 2017;14:1-4.

Bhattacharyya T, Piccirillo J, Wippold FJ. Relationship between patient- based descriptions of sinusitis and paranasal sinus computed tomographic findings. Arch Otolaryngol Head Neck Surg. 1997;123:1189-92.

Konen E, Faibel M, Kleinbaum Y, Wolf M, Lusky A, Hoffman C, et al. The value of the occipitomental (Waters') view in diagnosis of sinusitis: a Comparative study with computed tomography. Clin Radiol. 2000;55:856-60.

Kolo ES, Ezeanolue BC. Chronic rhinosinusitis in kano: A correlational study of the symptomatology and plain radiographic findings. Nigerian J Otorhinolaryngol. 2010;7:19.

Kenny TJ, Duncavage J, Bracikowski J, Yildirim A, Murray JJ, Tanner SB. Prospective analysis of sinus symptoms and correlation with paranasal computed tomography scan. Otolaryngol Head Neck Surg. 2001;125:40-3.

Al-Azzawi AA, Al-Umeri KK. Comparison of clinical symptoms, plain radiographs, coronal CT and Antral Lavage in patients with chronic maxillary sinusitis. Medical J Babylon. 2011;8:1.