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Abnormal Respiration

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Shoichi Matsunaga – One of the best experts on this subject based on the ideXlab platform.

  • classification between Abnormal and normal Respiration through observation rate of heart sounds within lung sounds
    European Signal Processing Conference, 2018
    Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi Matsunaga

    Abstract:

    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.

  • EUSIPCO – Classification Between Abnormal and Normal Respiration Through Observation Rate of Heart Sounds Within Lung Sounds
    2018 26th European Signal Processing Conference (EUSIPCO), 2018
    Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi Matsunaga

    Abstract:

    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.

  • distinction between healthy individuals and patients with confident Abnormal Respiration
    Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2017
    Co-Authors: Masara Yamashita, Tasuku Miura, Shoichi Matsunaga

    Abstract:

    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.

Masaru Yamashita – One of the best experts on this subject based on the ideXlab platform.

  • classification between Abnormal and normal Respiration through observation rate of heart sounds within lung sounds
    European Signal Processing Conference, 2018
    Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi Matsunaga

    Abstract:

    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.

  • EUSIPCO – Classification Between Abnormal and Normal Respiration Through Observation Rate of Heart Sounds Within Lung Sounds
    2018 26th European Signal Processing Conference (EUSIPCO), 2018
    Co-Authors: Kimitake Ohkawa, Masaru Yamashita, Shoichi Matsunaga

    Abstract:

    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.

  • 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, 2016
    Co-Authors: Naoki Nakamura, Masaru Yamashita, Shoichi Matsunaga

    Abstract:

    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.

Agostino Accardo – One of the best experts on this subject based on the ideXlab platform.

  • CinC – Fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in OSAS patients
    , 2013
    Co-Authors: Giovanni D'addio, Agostino Accardo, Elisa Fornasa, Mario Cesarelli, Alberto De Felice

    Abstract:

    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

  • Fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in OSAS patients
    European Respiratory Journal, 2013
    Co-Authors: Alberto De Felice, Giovanni D'addio, Mario Cesarelli, Giovanni Balzano, Agostino Accardo

    Abstract:

    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

  • fractal behaviour of heart rate variability reflects Abnormal Respiration patterns in osas patients
    Computing in Cardiology Conference, 2013
    Co-Authors: Giovanni Daddio, Elisa Fornasa, Agostino Accardo, Mario Cesarelli, Alberto De Felice

    Abstract:

    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.