The Experts below are selected from a list of 2283 Experts worldwide ranked by ideXlab platform
Shoichi Matsunaga - One of the best experts on this subject based on the ideXlab platform.
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classification between Abnormal and normal Respiration through observation rate of heart sounds within lung sounds
European Signal Processing Conference, 2018Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi MatsunagaAbstract:This paper proposes an effective classification method to differentiate between normal and Abnormal lung sounds, which takes into account the detection level of heart sounds. Abnormal lung sounds frequently contain adventitious sounds; however, misclassification between heart sounds and adventitious sounds makes it difficult to achieve a high level of accuracy. Furthermore, the classification performance of conventional methods, which use the detection function of heart sounds, becomes worse for those lung sounds which contain a low level of heart sounds. To address this problem, our proposed method changes the classification method according to the detection rate of heart sounds, whereby if the rate was high, the heart-sound models in the HMM -based classification method were used. In addition to spectral information, temporal information of heart sounds and adventitious sounds were also used to obtain the rate more precisely. When using lung sounds from three auscultation points, the proposed method achieved a higher classification performance of 89.90% (between normal and Abnormal Respiration) compared to 88.7% for the conventional method, which used the detection function of heart sounds. Our approach to the classification of healthy and unhealthy subjects also achieved a higher classification rate of 86.6%, compared to 83.1 % when using the conventional method having the detection function of heart sounds.
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EUSIPCO - Classification Between Abnormal and Normal Respiration Through Observation Rate of Heart Sounds Within Lung Sounds
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi MatsunagaAbstract:This paper proposes an effective classification method to differentiate between normal and Abnormal lung sounds, which takes into account the detection level of heart sounds. Abnormal lung sounds frequently contain adventitious sounds; however, misclassification between heart sounds and adventitious sounds makes it difficult to achieve a high level of accuracy. Furthermore, the classification performance of conventional methods, which use the detection function of heart sounds, becomes worse for those lung sounds which contain a low level of heart sounds. To address this problem, our proposed method changes the classification method according to the detection rate of heart sounds, whereby if the rate was high, the heart-sound models in the HMM -based classification method were used. In addition to spectral information, temporal information of heart sounds and adventitious sounds were also used to obtain the rate more precisely. When using lung sounds from three auscultation points, the proposed method achieved a higher classification performance of 89.90% (between normal and Abnormal Respiration) compared to 88.7% for the conventional method, which used the detection function of heart sounds. Our approach to the classification of healthy and unhealthy subjects also achieved a higher classification rate of 86.6%, compared to 83.1 % when using the conventional method having the detection function of heart sounds.
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distinction between healthy individuals and patients with confident Abnormal Respiration
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2017Co-Authors: Masara Yamashita, Tasuku Miura, Shoichi MatsunagaAbstract:To adequately distinguish between healthy individuals and patients with respiratory disorders, we propose a new classification method combining two conventional methods. The first method entails determining the presence of a "confident Abnormal Respiration" period (used to describe individuals for whom the likelihood of an Abnormal respiratory candidate was much higher than for that of a normal candidate, and for which patients could be determined with high accuracy). The second method entails comparing the two total likelihoods (through a series of inspiration and expiration periods) of normal and Abnormal candidates of each respiratory period in a test sample. In our new method, if one or more confident Abnormal Respiration phases are detected in a test Respiration sample, the first method is used; otherwise, the second method is used for the classification. Our proposed method achieved significantly higher performance (88.6%) at the 5% level (p=0.027) than does each conventional classification method alone (80.6% and 84.9%). This validates our newly proposed classification method.
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APSIPA - Distinction between healthy individuals and patients with confident Abnormal Respiration
2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 2017Co-Authors: Masara Yamashita, Tasuku Miura, Shoichi MatsunagaAbstract:To adequately distinguish between healthy individuals and patients with respiratory disorders, we propose a new classification method combining two conventional methods. The first method entails determining the presence of a "confident Abnormal Respiration" period (used to describe individuals for whom the likelihood of an Abnormal respiratory candidate was much higher than for that of a normal candidate, and for which patients could be determined with high accuracy). The second method entails comparing the two total likelihoods (through a series of inspiration and expiration periods) of normal and Abnormal candidates of each respiratory period in a test sample. In our new method, if one or more confident Abnormal Respiration phases are detected in a test Respiration sample, the first method is used; otherwise, the second method is used for the classification. Our proposed method achieved significantly higher performance (88.6%) at the 5% level (p=0.027) than does each conventional classification method alone (80.6% and 84.9%). This validates our newly proposed classification method.
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EMBC - Detection of patients considering observation frequency of continuous and discontinuous adventitious sounds in lung sounds
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2016Co-Authors: Naoki Nakamura, Masaru Yamashita, Shoichi MatsunagaAbstract:We propose an improved approach for distinguishing between healthy subjects and patients with pulmonary emphysema by the use of one stochastic acoustic model for continuous adventitious sounds and another for discontinuous adventitious sounds. These models are able to represent the spectral features of the adventitious sounds for the detection of Abnormal Respiration. However, Abnormal respiratory sounds with unclassifiable spectral features are present among the continuous and discontinuous adventitious sounds and mixing noises. These sounds cause difficulties in achieving a highly accurate classification. In this study, the difference in occurrence frequencies between two types of adventitious sounds for each considered auscultation point and inspiration/expiration was considered. This difference, in combination with the confusion tendency of the classifier, was formulated as the validity score of each respiratory sound. The conventional spectral likelihood and the newly formulated validity score were combined to perform detection of Abnormal Respiration and patients. In the classification of healthy subjects and patients, the proposed approach achieved a higher classification rate (87.7%) than the conventional method (85.2%), demonstrating the statistical superiority (5% level) of the former.
Masaru Yamashita - One of the best experts on this subject based on the ideXlab platform.
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classification between Abnormal and normal Respiration through observation rate of heart sounds within lung sounds
European Signal Processing Conference, 2018Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi MatsunagaAbstract:This paper proposes an effective classification method to differentiate between normal and Abnormal lung sounds, which takes into account the detection level of heart sounds. Abnormal lung sounds frequently contain adventitious sounds; however, misclassification between heart sounds and adventitious sounds makes it difficult to achieve a high level of accuracy. Furthermore, the classification performance of conventional methods, which use the detection function of heart sounds, becomes worse for those lung sounds which contain a low level of heart sounds. To address this problem, our proposed method changes the classification method according to the detection rate of heart sounds, whereby if the rate was high, the heart-sound models in the HMM -based classification method were used. In addition to spectral information, temporal information of heart sounds and adventitious sounds were also used to obtain the rate more precisely. When using lung sounds from three auscultation points, the proposed method achieved a higher classification performance of 89.90% (between normal and Abnormal Respiration) compared to 88.7% for the conventional method, which used the detection function of heart sounds. Our approach to the classification of healthy and unhealthy subjects also achieved a higher classification rate of 86.6%, compared to 83.1 % when using the conventional method having the detection function of heart sounds.
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EUSIPCO - Classification Between Abnormal and Normal Respiration Through Observation Rate of Heart Sounds Within Lung Sounds
2018 26th European Signal Processing Conference (EUSIPCO), 2018Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi MatsunagaAbstract:This paper proposes an effective classification method to differentiate between normal and Abnormal lung sounds, which takes into account the detection level of heart sounds. Abnormal lung sounds frequently contain adventitious sounds; however, misclassification between heart sounds and adventitious sounds makes it difficult to achieve a high level of accuracy. Furthermore, the classification performance of conventional methods, which use the detection function of heart sounds, becomes worse for those lung sounds which contain a low level of heart sounds. To address this problem, our proposed method changes the classification method according to the detection rate of heart sounds, whereby if the rate was high, the heart-sound models in the HMM -based classification method were used. In addition to spectral information, temporal information of heart sounds and adventitious sounds were also used to obtain the rate more precisely. When using lung sounds from three auscultation points, the proposed method achieved a higher classification performance of 89.90% (between normal and Abnormal Respiration) compared to 88.7% for the conventional method, which used the detection function of heart sounds. Our approach to the classification of healthy and unhealthy subjects also achieved a higher classification rate of 86.6%, compared to 83.1 % when using the conventional method having the detection function of heart sounds.
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EMBC - Detection of patients considering observation frequency of continuous and discontinuous adventitious sounds in lung sounds
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2016Co-Authors: Naoki Nakamura, Masaru Yamashita, Shoichi MatsunagaAbstract:We propose an improved approach for distinguishing between healthy subjects and patients with pulmonary emphysema by the use of one stochastic acoustic model for continuous adventitious sounds and another for discontinuous adventitious sounds. These models are able to represent the spectral features of the adventitious sounds for the detection of Abnormal Respiration. However, Abnormal respiratory sounds with unclassifiable spectral features are present among the continuous and discontinuous adventitious sounds and mixing noises. These sounds cause difficulties in achieving a highly accurate classification. In this study, the difference in occurrence frequencies between two types of adventitious sounds for each considered auscultation point and inspiration/expiration was considered. This difference, in combination with the confusion tendency of the classifier, was formulated as the validity score of each respiratory sound. The conventional spectral likelihood and the newly formulated validity score were combined to perform detection of Abnormal Respiration and patients. In the classification of healthy subjects and patients, the proposed approach achieved a higher classification rate (87.7%) than the conventional method (85.2%), demonstrating the statistical superiority (5% level) of the former.
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Abnormal-Respiration detection by considering correlation of observation of adventitious sounds
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Shohei Matsutake, Masaru Yamashita, Shoichi MatsunagaAbstract:We propose a classification method to distinguish between normal and Abnormal Respiration by considering the correlation of the observation frequencies of adventitious sounds between auscultation points. This method is based on the fact that adventitious sounds are frequently observed in lung sounds from multiple points. We use the product of the correlation score and the Abnormality score, which indicates the likelihood that a candidate is Abnormal, of lung sounds from different points. When using lung sounds from eight points, the proposed method achieved a higher classification performance of 92.0% between normal and Abnormal Respiration compared with the baseline method not considering the other lung sounds, which achieved a performance of 84.1%. Our approach to the classification of healthy subjects and patients also achieved a higher classification rate of 90.8%.
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EUSIPCO - Abnormal-Respiration detection by considering correlation of observation of adventitious sounds
2015 23rd European Signal Processing Conference (EUSIPCO), 2015Co-Authors: Shohei Matsutake, Masaru Yamashita, Shoichi MatsunagaAbstract:We propose a classification method to distinguish between normal and Abnormal Respiration by considering the correlation of the observation frequencies of adventitious sounds between auscultation points. This method is based on the fact that adventitious sounds are frequently observed in lung sounds from multiple points. We use the product of the correlation score and the Abnormality score, which indicates the likelihood that a candidate is Abnormal, of lung sounds from different points. When using lung sounds from eight points, the proposed method achieved a higher classification performance of 92.0% between normal and Abnormal Respiration compared with the baseline method not considering the other lung sounds, which achieved a performance of 84.1%. Our approach to the classification of healthy subjects and patients also achieved a higher classification rate of 90.8%.
Agostino Accardo - One of the best experts on this subject based on the ideXlab platform.
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CinC - Fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in OSAS patients
2013Co-Authors: Giovanni D'addio, Agostino Accardo, Elisa Fornasa, Mario Cesarelli, Alberto De FeliceAbstract:Although heart rate variability (HRV) decreasing has been usually described in obstructive sleep apnea syndrome (OSAS), some studies have recently questioned the validity of spectral HRV analysis in presence of respiratory and arrhythmic disorders. Fractal analysis of HRV is an emerging nonlinear technique overcoming these limitations and allowing short term HRV assessment during hypo/apnea phases. The aim of this study is to analyse the Fractal features in sleep apnea in order to find as these characteristics could change during Abnormal Respiration patterns in OSAS. We studied 30 polysomnographic recordings of severe OSAS (AHI≥30) pts. (age 55±9) and 10 PR of normal subjects (age 46±4). Hypo/apnea phases and related beat-to-beat time series have been detected and classified by automated algorithms and manually verified by expert technicians. Fractal analysis was performed by the Higuchi algorithm (FD). Results showed that while FD does not significantly differ between Normals (1.61±0.09) and normal breath epochs in OSAS, it significantly (p
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Fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in OSAS patients
European Respiratory Journal, 2013Co-Authors: Alberto De Felice, Giovanni D'addio, Mario Cesarelli, Giovanni Balzano, Agostino AccardoAbstract:Although heart rate variability (HRV) decreasing has been usually described in obstructive sleep apnea syndrome (OSAS), some studies have recently questioned the validity of spectral HRV analysis in presence of respiratory and arrhythmic disorders. Fractal analysis (F) of HRV is an emerging nonlinear technique overcoming these limitations and allowing short term HRV assessment during hypo/apnea phases. This is one of the first studies on F-features in sleep apnea and its aim is to find out whether and to which extend F-HRV reflects Abnormal Respiration patterns in OSAS. We studied 30 polysomnographic recordings (PR) of severe OSAS (AHI≥30) pts. (age 55±9) and 10 PR of normal subjects (age 46±4). Hypo/apnea phases and related beat-to-beat time series have been detected and classified by automated algorithms and manually verified by expert technicians. F-analysis has been performed by Huiguchi fractal dimension alghoritm (FD), separately for normal breath (NB), hypopneas (HY), obstructive (OS) and mixed (MX) apneas epochs. Results showed that while FD does not significantly (Mann Whitney test P>0.05) differ between Normals (1.61±0.09) and NB epochs in OSAS pts., it significantly (Friedman test p
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fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in osas patients
Computing in Cardiology Conference, 2013Co-Authors: Giovanni Daddio, Agostino Accardo, Elisa Fornasa, Mario Cesarelli, Alberto De FeliceAbstract:Although heart rate variability (HRV) decreasing has been usually described in obstructive sleep apnea syndrome (OSAS), some studies have recently questioned the validity of spectral HRV analysis in presence of respiratory and arrhythmic disorders. Fractal analysis of HRV is an emerging nonlinear technique overcoming these limitations and allowing short term HRV assessment during hypo/apnea phases. The aim of this study is to analyse the Fractal features in sleep apnea in order to find as these characteristics could change during Abnormal Respiration patterns in OSAS. We studied 30 polysomnographic recordings of severe OSAS (AHI≥30) pts. (age 55±9) and 10 PR of normal subjects (age 46±4). Hypo/apnea phases and related beat-to-beat time series have been detected and classified by automated algorithms and manually verified by expert technicians. Fractal analysis was performed by the Higuchi algorithm (FD). Results showed that while FD does not significantly differ between Normals (1.61±0.09) and normal breath epochs in OSAS, it significantly (p<;0.005) tends to a less fractal structure from normal breath (1.60±0.15) to hypopneas (1.52±0.13), obstructive (1.50±0.12) and mixed apneas (1.48±0.11) epochs, with a significant Dunn's multiple comparisons post test only between normal breath vs. obstructive and mixed apneas.
Walter T. Mcnicholas - One of the best experts on this subject based on the ideXlab platform.
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Clinical prediction of the sleep apnea syndrome.
Sleep medicine reviews, 1997Co-Authors: W. Ward Flemons, Walter T. McnicholasAbstract:Polysomnography, the standard diagnostic test for people suspected of having sleep apnea, is a limited resource due to its expense. Decisions about which patients to refer to a sleep center and which require polysomnography can be made based on an estimate of the probability that they have sleep apnea. Clinical features that are associated with the severity of sleep apnea, as judged by the apnea-hypopnea index, can be combined together using statistical modeling into a clinical prediction rule, whose diagnostic performance can be summarized by its sensitivity and specificity or by likelihood ratios. To date, at least seven different sleep apnea clinical prediction rules have been developed, most incorporate anthropomorphic variables such as the body mass index, waist circumference, and/or neck circumference, and some type of Abnormal Respiration during sleep (snoring, apneas, choking and/or gasping) witnessed by a bed partner. In general these rules have reasonably high sensitivities but only intermediate specificities, thus they can be useful in excluding the diagnosis but do not usually raise the probability of sleep apnea high enough to warrant initiating therapy without at least some type of additional testing to confirm the diagnosis. In isolation the apnea-hypopnea index is not an optimal indicator of disease severity, thus ultimately clinical decisions about the need for polysomnography and/or the need for treatment must take into account other important clinical information such as symptom severity, quality of life, and the presence or absence of comorbid illness.
Sueharu Miyahara - One of the best experts on this subject based on the ideXlab platform.
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discrimination between healthy subjects and patients with pulmonary emphysema by detection of Abnormal Respiration
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Masaru Yamashita, Shoichi Matsunaga, Sueharu MiyaharaAbstract: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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ICASSP - Discrimination between healthy subjects and patients with pulmonary emphysema by detection of Abnormal Respiration
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Masaru Yamashita, Shoichi Matsunaga, Sueharu MiyaharaAbstract: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.
