Signal analysis of medical acoustic sounds with applications to chest medicine

Marshall, Alison; Boussakta, S.

doi:10.1016/j.jfranklin.2006.08.003

Cited by 41 publications

(20 citation statements)

References 13 publications

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“…Several studies have been conducted on acoustic analysis of respiratory sounds for the detection of specific adventitious sounds [1][2][3][4]. These studies were not, however, aimed at developing devices to identify respiratory illness at home; instead, they were aimed at assisting doctors in hospitals to make diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration

Yamashita

Matsunaga

Miyahara

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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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.

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Section: Introductionmentioning

confidence: 99%

Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration

Yamashita

Matsunaga

Miyahara

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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show abstract

“…Among all the filter banks, the cosine-modulated filter bank (CMFB) is one of the most frequently used filter banks in audio coding [28][29][30], adaptive signal processing [31,32], and ECG signal processing [33][34][35][36], because of its matching properties of the analysis filter bank to the characteristics of the input signal. In ECG signal processing, CMFB filter banks are used in several applications such as beat detection, signal enhancement, beat classification, and compression.…”

Section: Application To Sub-band Codingmentioning

confidence: 99%

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Kumar

Singh

Anand

2011

Journal of the Franklin Institute

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“…Several acoustic analyses of respiratory sounds for the detection of specific adventitious sounds have been conducted [2][3][4][5]. These analyses were performed to assist doctors in making diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Discrimination between healthy subjects and patients using lung sounds from multiple auscultation points

Matsutake

Yamashita

Matsunaga

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we propose a robust classification method to distinguish between a healthy subject and a patient with pulmonary emphysema using lung sound samples recorded from multiple auscultation points. Although the symptom of pulmonary emphysema can be determined from lung sounds that frequently include abnormal (i.e., adventitious) sounds, these are not observed in every auscultation point. Furthermore, noise pollution during auscultation makes high-accuracy detection difficult. To overcome these difficulties, our proposed method took into account lung sound samples from multiple auscultation points in diagnosing a patient. After the calculation of the acoustic likelihood for each respiratory phase based on the maximum likelihood approach using hidden Markov models and a segmental bigram, patient diagnosis was carried out based on the comparison of the average likelihood of all auscultation points between a patient and a healthy subject. Our classification method significantly increased the classification performance to 90.5% (using samples from four auscultation points) from the 82.7% classification performance of the conventional method (using a sample from one auscultation point), validating the usefulness of our proposed method.

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Signal analysis of medical acoustic sounds with applications to chest medicine

Cited by 41 publications

References 13 publications

Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration

Discrimination between healthy subjects and patients with pulmonary emphysema by detection of abnormal respiration

A simple design method for the cosine-modulated filter banks using weighted constrained least square technique

Discrimination between healthy subjects and patients using lung sounds from multiple auscultation points

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