Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

Lozano, Manuel; Fiz, J.A.; Jané, Raimon

doi:10.1016/j.sigpro.2015.09.005

Cited by 38 publications

(31 citation statements)

References 51 publications

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“…Meanwhile, Reyes et al (2008) analyzed the discontinuous adventitious sound using HHT. A continuous adventitious sound analysis using HHT was done in a paper by (Lozano et al, 2016). Overall, these studies used IMF1 to IMF3 to see the Hilbert-Huang Spectra (HHS) of the lung sounds.…”

Section: Related Workmentioning

confidence: 99%

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Rizal¹,

Hidayat²,

Nugroho³

2017

American Journal of Applied Sciences

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Lung sound is produced by the respiration process in the human respiratory tract. It contains information about the health of the respiratory organs. Lung sound is non-stationary signals and complex signals. One method for the analysis of non-stationary signals often used for the analysis of lung sounds is Empirical Mode Decomposition (EMD). EMD is used to view the Instantaneous Frequency (IF) of the lung sound to differentiate the types of lung sounds. Features extraction directly on Intrinsic Mode Function (IMF) of EMD result is rarely performed in the lung sound analysis. In this research, the EMD was used to obtain IMF of lung sounds. IMF from lung sounds was then analyzed using the Hjorth descriptors. As a classifier, we used Multilayer Perceptron (MLP) with a three-fold cross validation (3fold CV) for validation. From the test, it was found that activity parameter in the first 10 IMF yielded 98.8% accuracy on five classes of data tested. The proposed method showed the excellence of the measurement of the Hjorth descriptor on IMF for feature extraction in lung sound classification.

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Section: Related Workmentioning

confidence: 99%

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Rizal¹,

Hidayat²,

Nugroho³

2017

American Journal of Applied Sciences

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“…These nonlung sound signals included signals from the trachea [10], signals from a microphone placed on the side of the nose [11], and signals due to chest movements recorded with a smartphone camera [12]. For people with sleep apnea, sound signals from the lungs and the trachea have been recorded simultaneously [13], and airflow signals were obtained from a group of healthy people, a group with abnormal breathing [14], and a group with asthma [15]. These studies had to acquire multichannel data to detect respiratory cycles using the sounds from the lungs.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of the respiratory cycle from recorded, single-channel sounds from lungs

Aras¹,

Öztürk²,

Gangal³

2018

Turk J Elec Eng & Comp Sci

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Abstract:Listening to the sounds made by the lungs is a long-standing method that is still used to diagnose lung diseases. Many studies have been conducted on the automatic recognition of recorded sounds from the lungs. However, in these studies, respiratory cycles, i.e. inhalations followed by exhalations, were either monitored manually or by using multichannel signals of the sounds made by the lungs. In order to recognize sounds made by the lungs automatically, one must first use these sounds to determine the respiratory cycles. Our previous study was the first study presented in the literature in which the boundaries of respiratory cycles were determined based on single-channel sounds from the lungs.However, this method was not successful when ambient noise occurred and interfered with the sounds from the lungs.In the present study, a new method has been developed in which important changes were made to reduce or negate the effects of ambient noises. Our proposed new method includes a processing method that we developed to obtain respiratory cycles as smooth, repetitive patterns. Then the dynamic time-warping algorithm was used to determine the boundaries of the respiratory cycles, based on the similarity of the patterns. This new method was used to detect the seven sounds that are commonly made by the lung. As a result, the limits of the respiratory cycles were obtained with a mean absolute error of 56.51 ms. In addition, the method we developed has the potential to determine the boundaries of the patterns in signals that contain repetitive data, such as the sounds made by the lungs in this study.

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“…The specific procedure which deals with fine data implanted inside an adventitious sound must be broken down into Intrinsic Mode Function (IMF) which is a fundamental building block of EMD. The EMD and Ensemble EMD (EEMD) based Hilbert transform is a useful procedure to set up CAS more precisely than another customary time-frequency investigation [3]. The EMD and EEMD occupying top priority techniques among time-frequency investigation procedure contrast with STFT, wavelets and any type of Fourier Transformation methods.…”

Section: Introductionmentioning

confidence: 99%

Ensemble EMD based Time-Frequency Analysis of Continuous Adventitious Signal Processing

Shankar¹,

Jayadevappa²

2018

IJET

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The importance of lung sound analyses is increasing day by day very rapidly. In this paper, we present a new method for analysis of two classes of lung signals namely wheezes and crackles. The procedure used in this article is based on improved Empirical Mode Decomposition (EMD) called Ensemble Empirical Mode Decomposition (EEMD) to analyze and compare continuous and discontinuous adventitious sounds with EMD. These two proposed procedures decompose the lung signals into a set of instantaneous frequency components. Function (IMF). The continuous and discontinuous adventitious sounds are present in an asthmatic patient, produces a non-stationary and nonlinear signal pattern. The empirical mode decomposition (EMD) decomposes such characteristic signals. The instantaneous frequency and spectral analysis related to dual techniques specified above are utilized by IMF to investigate and present the outcome in the time-frequency distribution to investigate the qualities of inbuilt properties of lung sound waves. The Hilbert marginal spectrum has been used to represent total amplitude and energy contribution from every frequency value. Finally, the resultant EEMD analysis is better for wheezes that solves mode mixing issues and improvisation is seen over the EMD method.

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Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization

Cited by 38 publications

References 51 publications

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Lung Sound Classification Using Empirical Mode Decomposition and the Hjorth Descriptor

Automatic detection of the respiratory cycle from recorded, single-channel sounds from lungs

Ensemble EMD based Time-Frequency Analysis of Continuous Adventitious Signal Processing

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