Automated Cough Assessment on a Mobile Platform
Mark Sterling, Hyekyun Rhee, Mark Bocko, Mark Sterling, Hyekyun Rhee, Mark Bocko
Abstract
The development of an Automated System for Asthma Monitoring (ADAM) is described. This consists of a consumer electronics mobile platform running a custom application. The application acquires an audio signal from an external user-worn microphone connected to the device analog-to-digital converter (microphone input). This signal is processed to determine the presence or absence of cough sounds. Symptom tallies and raw audio waveforms are recorded and made easily accessible for later review by a healthcare provider. The symptom detection algorithm is based upon standard speech recognition and machine learning paradigms and consists of an audio feature extraction step followed by a Hidden Markov Model based Viterbi decoder that has been trained on a large database of audio examples from a variety of subjects. Multiple Hidden Markov Model topologies and orders are studied. Performance of the recognizer is presented in terms of the sensitivity and the rate of false alarm as determined in a cross-validation test.
Figures
References
- Smith J. Ambulatory methods for recording cough. Pulmonary Pharmacology and Therapeutics. 2007;20(4):313–318. doi: 10.1016/j.pupt.2006.10.016.
- Shin S.-H., Hashimoto T., Hatano S. Automatic detection system for cough sounds as a symptom of abnormal health condition. IEEE Transactions on Information Technology in Biomedicine. 2009;13(4):486–493. doi: 10.1109/TITB.2008.923771.
- Matos S., Birring S. S., Pavord I. D., Evans D. H. Detection of cough signals in continuous audio recordings using hidden Markov models. IEEE Transactions on Biomedical Engineering. 2006;53(6):1078–1083. doi: 10.1109/TBME.2006.873548.
- Murata A., Ohota N., Shibuya A., Ono H., Kudoh S. New non-invasive automatic cough counting program based on 6 types of classified cough sounds. Internal Medicine. 2006;45(6):391–397. doi: 10.2169/internalmedicine.45.1449.
- Krajnik M., Damps-Konstanska I., Gorska L., Jassem E. A portable automatic cough analyser in the ambulatory assessment of cough. BioMedical Engineering Online. 2010;9, article 17 doi: 10.1186/1475-925X-9-17.
- Goldshtein E., Tarasiuk A., Zigel Y. Automatic detection of obstructive sleep apnea using speech signals. IEEE Transactions on Biomedical Engineering. 2011;58(5):1373–1382. doi: 10.1109/TBME.2010.2100096.
- Arias-Londoño J. D., Godino-Llorente J. I., Sáenz-Lechón N., Osma-Ruiz V., Castellanos-Domínguez G. Automatic detection of pathological voices using complexity measures, noise parameters, and mel-cepstral coefficients. IEEE Transactions on Biomedical Engineering. 2011;58(2):370–379. doi: 10.1109/TBME.2010.2089052.
- Fonseca E., Pereira J. Normal versus pathological voice signals. IEEE Engineering in Medicine and Biology Magazine. 2009;28(5):44–48. doi: 10.1109/MEMB.2009.934248.
- Ozdas A., Shiavi R. G., Silverman S. E., Silverman M. K., Wilkes D. M. Investigation of vocal jitter and glottal flow spectrum as possible cues for depression and near-term suicidal risk. IEEE Transactions on Biomedical Engineering. 2004;51(9):1530–1540. doi: 10.1109/TBME.2004.827544.
- Price J. H., Khubchandani J., McKinney M., Braun R. Racial/Ethnic disparities in chronic diseases of youths and access to health care in the united states. BioMed Research International. 2013;2013:12. doi: 10.1155/2013/787616.787616
- Rhee H., Belyea M. J., Elward K. S. Patterns of asthma control perception in adolescents: associations with psychosocial functioning. Journal of Asthma. 2008;45(7):600–606. doi: 10.1080/02770900802126974.
- Rhee H., Wenzel J., Steeves R. H. Adolescents psychosocial experiences living with asthma: a focus group study. Journal of Pediatric Health Care. 2007;21(2):99–107. doi: 10.1016/j.pedhc.2006.04.005.
- Wildhaber J., Carroll W. D., Brand P. L. P. Global impact of asthma on children and adolescents' daily lives: the room to breathe survey. Pediatric Pulmonology. 2012;47(4):346–357. doi: 10.1002/ppul.21557.
- Davis K. J., DiSantostefano R., Peden D. B. Is Johnny wheezing? Parent-child agreement in the Childhood Asthma in America survey. Pediatric Allergy and Immunology. 2011;22(1):31–35. doi: 10.1111/j.1399-3038.2010.01016.x.
- Cai L.-H., Lu L., Hanjalic A., Zhang H.-J., Cai L. A flexible framework for key audio effects detection and auditory context inference. IEEE Transactions on Audio, Speech and Language Processing. 2006;14(3):1026–1038. doi: 10.1109/TSA.2005.857575.
- Karnjanadecha M., Zahorian S. A. Signal modeling for high-performance robust isolated word recognition. IEEE Transactions on Speech and Audio Processing. 2001;9(6):647–654. doi: 10.1109/89.943342.
- Wilpon J. G., Rabiner L. R., Lee C., Goldman E. R. Automatic recognition of keywords in unconstrained speech using hidden Markov models. IEEE Transactions on Acoustics, Speech, and Signal Processing. 1990;38(11):1870–1878. doi: 10.1109/29.103088.
- Jain A. K., Duin R. P. W., Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000;22(1):4–37. doi: 10.1109/34.824819.
- Morgan N., Bourlard H. Continuous speech recognition. IEEE Signal Processing Magazine. 1995;12(3):24–42. doi: 10.1109/79.382443.
- Wold E., Blum T., Keislar D., Wheaton J. Content-based classification, search, and retrieval of audio. IEEE Multimedia. 1996;3(3):27–36. doi: 10.1109/93.556537.
- Doğan E., Sert M., Yazici A. Content-based classification and segmentation of mixed-type audio by using MPEG-7 features. Proceedings of the 1st International Conference on Advances in Multimedia (MMEDIA '09); July 2009; pp. 152–157.
- Ganapathiraju A., Hamaker J. E., Picone J. Applications of support vector machines to speech recognition. IEEE Transactions on Signal Processing. 2004;52(8):2348–2355. doi: 10.1109/TSP.2004.831018.
- Rabiner L. R. Tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE. 1989;77(2):257–286. doi: 10.1109/5.18626.
- Young S., Evermann G., Gales M., et al. The HTK Book (for HTK Version 3.4) Cambridge, UK: Department of Engineering, University of Cambridge; 2006. (1em plus 0.5em minus 0.4em).
- Matos S., Birring S. S., Pavord I. D., Evans D. H. An automated system for 24-h monitoring of cough frequency: the Leicester cough monitor. IEEE Transactions on Biomedical Engineering. 2007;54(8):1472–1479. doi: 10.1109/TBME.2007.900811.
- Rhee H., Miner S., Sterling M., Halterman S. J., Fairbanks E. The development of an automated device for asthma monitoring for adolescents: Methodologic approach and user acceptability. JMIR mHealth uHealth. 2014;2(2):p. e27. doi: 10.2196/mhealth.3118.
- Lee C., Hyun D., Choi E., Go J., Lee C. Optimizing feature extraction for speech recognition. IEEE Transactions on Speech and Audio Processing. 2003;11(1):80–87. doi: 10.1109/TSA.2002.805644.
- O'Shaughnessy D. Acoustic analysis for automatic speech recognition. Proceedings of the IEEE. 2013;101(5):1038–1053. doi: 10.1109/JPROC.2013.2251592.
- Young S., Russel N. H., Thornton J. H. S. A. Cambridge University; 1989. Token passing: a simple conceptual model for connected speech recognition systems.
- Adamson C., Avila K. Learning Core Audio: A Hands-On Guide to Audio Programming for Mac and iOS. New York, NY, USA: Addison-Wesley; 2012.
Source: PubMed