Name
Title | ||
|---|---|---|
Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration. |
Abstract | ||
|---|---|---|
In this paper, we propose a robust classification strategy for distinguishing between a healthy subject and a patient with pulmonary emphysema on the basis of lung sounds. A symptom of pulmonary emphysema is that almost all lung sounds include some abnormal (i.e., adventitious) sounds. However, the great variety of possible adventitious sounds and noises at auscultation makes high-accuracy detection difficult. To overcome this difficulty, our strategy is to adopt a two-step classification approach based on the detection of "confident abnormal respiration." In the first step, hidden Markov models and bigram models are used for acoustic features and the occurrence of acoustic segments in each abnormal respiratory period, respectively, to calculate two kinds of stochastic likelihoods: the highest likelihood for a segment sequence to be abnormal respiration and the likelihood for normal respiration. In the second step, the patients are identified on the basis of the detection of confident abnormal respiration, which is when difference between these two likelihoods is larger than a predefined threshold. Our strategy achieved the highest classification rate of 88.7% between healthy subjects and patients among three basic classification strategies, which shows the validity of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946498 | ICASSP |
Keywords | Field | DocType |
acoustic signal processing,bioacoustics,diseases,hidden Markov models,lung,medical signal detection,medical signal processing,patient diagnosis,pneumodynamics,signal classification,stochastic processes,abnormal lung sounds,abnormal respiration likelihood,abnormal respiratory period,acoustic features,acoustic segment occurrence,adventitious lung sounds,auscultation,bigram models,confident abnormal respiration detection,hidden Markov models,pulmonary emphysema classification,pulmonary emphysema symptom,robust classification strategy,stochastic likelihoods,two step classification approach,acoustic model,adventitious sound,lung sound | Lung,Computer science,Bioacoustics,Bigram,Artificial intelligence,Audiology,Auscultation,Respiration,Pattern recognition,Speech recognition,Hidden Markov model,Classification rate,Acoustic model | Conference |
ISSN | Citations | PageRank |
1520-6149 | 5 | 0.68 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaru Yamashita | 1 | 28 | 6.46 |
| Shoichi Matsunaga | 2 | 164 | 36.02 |
| Sueharu Miyahara | 3 | 40 | 7.47 |