Analysis and Classification of Swallowing Sounds Using Reconstructed Phase Space Features

Aboofazeli, Mohammad; Moussavi, Zahra

doi:10.1109/icassp.2005.1416330

Cited by 18 publications

(13 citation statements)

References 12 publications

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“…These invariants have shown promise as useful tools in the analysis of respiratory condition via lung sounds [3,16]. With respect to other bioacoustic signals, classification of swallowing sounds between healthy and dysphagic subjects exhibited an accuracy of 83% when a geometrical state space invariant, the time delay, was used as a classification feature [2]. Reconstructed state spaces of speech sounds were useful in characterization of vowels, and a method of synthesizing these sounds was developed based on the Lyapunov exponent, which is a dynamical state space measure [6].…”

Section: Introductionmentioning

confidence: 98%

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: Introductionmentioning

confidence: 98%

Diagnostic potential in state space parameters of lung sounds

2007

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“…The results of this study along with our previous findings [1,8] pave the way for non-invasive and yet objective swallowing disorder detection by acoustical means. Using features with more discriminating ability between IDS and BTS and also between healthy subjects and patients may lead to the better performance of automated segmentation and classification of swallowing sounds.…”

Section: Discussionmentioning

confidence: 67%

“…However, the information obtained by this method depends on the skills of the examiner [5]. In recent years, digital signal processing of swallowing sounds has drawn more attention in which swallowing sounds are recorded by a microphone and/or accelerometer and processed by digital signal processing techniques [1,8,18].…”

Section: Introductionmentioning

confidence: 99%

Analysis of swallowing sounds using hidden Markov models

Aboofazeli

Moussavi

2007

Med Biol Eng Comput

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In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

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“…The hermite projection method, which decomposes the signal into hermite polynomials, shares clear similarities to the wavelet transform method, but does not have the same popularity in this field [79]. In addition, swallowing signals have been investigated using a phase space transformation [42], [62], [73], [121]. By applying the method of delays, it is possible to map the time domain swallowing signal onto a multi-dimensional phase portrait and generate a recurrence plot as shown in Figure 6 [42], [62], [73], [121].…”

Section: Signal Analysismentioning

confidence: 99%

“…In addition, swallowing signals have been investigated using a phase space transformation [42], [62], [73], [121]. By applying the method of delays, it is possible to map the time domain swallowing signal onto a multi-dimensional phase portrait and generate a recurrence plot as shown in Figure 6 [42], [62], [73], [121]. This plot can be used to analyze the trends and periodic nature of a given signal as it tracks every time the phase portrait overlaps itself.…”

Section: Signal Analysismentioning

confidence: 99%

Dysphagia Screening: Contributions of Cervical Auscultation Signals and Modern Signal-Processing Techniques

Dudik

Coyle

Sejdić

2015

IEEE Trans. Human-Mach. Syst.

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Cervical auscultation is the recording of sounds and vibrations caused by the human body from the throat during swallowing. While traditionally done by a trained clinician with a stethoscope, much work has been put towards developing more sensitive and clinically useful methods to characterize the data obtained with this technique. The eventual goal of the field is to improve the effectiveness of screening algorithms designed to predict the risk that swallowing disorders pose to individual patients’ health and safety. This paper provides an overview of these signal processing techniques and summarizes recent advances made with digital transducers in hopes of organizing the highly varied research on cervical auscultation. It investigates where on the body these transducers are placed in order to record a signal as well as the collection of analog and digital filtering techniques used to further improve the signal quality. It also presents the wide array of methods and features used to characterize these signals, ranging from simply counting the number of swallows that occur over a period of time to calculating various descriptive features in the time, frequency, and phase space domains. Finally, this paper presents the algorithms that have been used to classify this data into ‘normal’ and ‘abnormal’ categories. Both linear as well as non-linear techniques are presented in this regard.

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Analysis and Classification of Swallowing Sounds Using Reconstructed Phase Space Features

Cited by 18 publications

References 12 publications

Diagnostic potential in state space parameters of lung sounds

Diagnostic potential in state space parameters of lung sounds

Analysis of swallowing sounds using hidden Markov models

Dysphagia Screening: Contributions of Cervical Auscultation Signals and Modern Signal-Processing Techniques

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