Heart sound cancellation from lung sound recordings using recurrence time statistics and nonlinear prediction

Ahlström, Christer; Liljefeldt, O.; Hult, Peter; Ask, Per

doi:10.1109/lsp.2005.859528

Cited by 37 publications

(36 citation statements)

References 11 publications

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“…The percentages of total positive k i decreased for high flow lung sounds and stayed approximately the same for low and medium flows. The results of state space analysis of heart sounds using the recurrence statistic reported in [4] complement the finding of our study. The recurrence statistic is related to the distance between state space vectors, and it was found to be lower for regions of lung sound recordings outside of heart sounds [4].…”

Section: Discussionsupporting

confidence: 87%

Diagnostic potential in state space parameters of lung sounds

2007

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The goal of this study was to investigate state space parameters of the lung sounds of healthy subjects and subjects with symptoms of asthma under different respiratory conditions. Our main objective was to elucidate the diagnostic potential of these parameters, which included embedding dimension (m), time delay (tau) and Lyapunov exponents (lambda). Lung sounds were acquired over the right lower lobe from six healthy subjects, ages 10-26 years, and from eight children with symptoms of asthma recorded pre- and post-bronchial provocation via methacholine challenge (MCh) and post-bronchial dilation (BD). Inspiratory air flows during recordings were 7.5, 15, or 22.5 mL/s per kg (+/-20%). With increasing flow for sounds recorded from healthy subjects, mean values of tau decreased. Percent of breaths with positive lambda decreased when heart sounds were excluded. For the patients who exhibited bronchoconstriction, values of tau increased and percent of positive lambda decreased post-MCh, and returned to pre-MCh values post-BD. Thus, both tau and presence of positive lambda may prove valuable in developing a model that will predict changes in respiratory status using lung sounds.

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Section: Discussionsupporting

confidence: 87%

Diagnostic potential in state space parameters of lung sounds

2007

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“…If it is chosen too low, the hypersphere would be low on data, and if r is chosen too high, the hypersphere will contain misleading information from erroneous parts of the reconstructed state space. In this work, r is adaptive, and it becomes lower if there are not lobe detection corresponding to S1 and S2 in Ψ(r) [6].…”

Section: Recurrence Time Statistics (Rts)mentioning

confidence: 99%

“…Another approach to segment PCG is based on time-frequency analysis, implemented with wavelet transform or distributions belonging to the Cohen's quadratic class. That PCG signal exhibits nonlinear dynamics, this fact motivates the use of complexity measures [5], and RTS [6]. In our work, we combine the best of these methodologies with the aim of developing a reliable algorithm capable of segmenting the PCG signal with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effective phonocardiogram segmentation using nonlinear dynamic analysis and high-frequency decomposition

Quiceno

Delgado

Vallverd

et al. 2008

2008 Computers in Cardiology

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In this study, an effective methodology for segmenting the temporal trace of phonocardiographic signals (PCG) is presented. Initially, inter-beat segmentation is carried out using the DII lead of the ECG recording for locating the occurrence of the first heart sound (S1). Next, the intra-beat segmentation is achieved by using recurrence time statistics (RTS), which is IntroductionThe segmentation process of PCG signals is a very important task in order to perform murmur detection, and diagnosis of other cardiac pathologies using computer analysis. Thus, it is essential that different components of heart cycle can be timed and separated [1]. Segmentation is performed in two main steps: inter-beat segmentation and intra-beat segmentation. The first one refers to the identification and separation of the beats with the aim of performing an individual diagnostic in each one of them. The second step includes the separation of the beat into its four principal components: first cardiac sound (S1), systole, second cardiac sound (S2) and diastole [2].Cardiac murmurs are present in systole, diastole or both. In this way, it is crucial to precisely detect the boundaries in order to correctly locate the components of heart sounds (HS), and then, to perform an accurate diagnosis and classification of the murmur if it is present. The main problem in HS segmentation occurs when a murmur is present and produces high distortion in the temporal trace.A large variety of algorithms to perform PCG segmentation have been proposed in the literature. In [1] the PCG signal segmentation is based on synchronized electrocardiographic (ECG) signal acquired with PCG. On the other hand, the envelope of PCG can be used to perform the segmentation, in [3] the envelope is estimated using homomorphic filtering, while in [4] it is estimated by means of Shannon normalized average energy. Moreover, Shannon energy can be used with more complex methodologies: wavelet transform in order to enhance the spectral components of S1 and S2; or with Mel-scaled filter-banks and Hidden Markov Models. Another approach to segment PCG is based on time-frequency analysis, implemented with wavelet transform or distributions belonging to the Cohen's quadratic class. That PCG signal exhibits nonlinear dynamics, this fact motivates the use of complexity measures [5], and RTS [6]. In our work, we combine the best of these methodologies with the aim of developing a reliable algorithm capable of segmenting the PCG signal with high accuracy.In this paper, we propose a method for the boundary identification of S1 and S2, using the ECG signal as reference in order to separate each beat according to the methodology proposed in [7]; later, the end of S1, beginning of S2 and end of S2 are detected using RTS and threshold rules. The results obtained in the previous stage are validated using biomedical features; if the fiducial points are out of certain boundaries given by physiological considerations, an alternative segmentation method is used. It is based on wavelet dec...

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“…For lung sound analysis, signal processing strategies based on conventional time, frequency, or time-frequency signal representations have been proposed for heart sound cancelation. Representative strategies include entropy calculation (7) and recurrence time statistics (8) for heart sound detection-and-removal followed by lung sound prediction, adaptive filtering (e.g., (9; 10)), time-frequency spectrogram filtering (11), and time-frequency wavelet filtering (e.g., (12)(13)(14)). Subjective assessment, however, has suggested that due to the temporal and spectral overlap between heart and lung sounds, heart sound removal may result in noisy or possibly "non-recognizable" lung sounds (15).…”

Section: Introductionmentioning

confidence: 99%