Automated Extraction of Swallowing Sounds Using a Wavelet-Based Filter

Abstract

This paper presents an automated and objective method for extraction of swallowing sounds in a record of the tracheal breath and swallowing sounds. The proposed method takes advantage of the fact that swallowing sounds have more non-stationarity comparing with breath sounds and have large components in many wavelet scales whereas wavelet transform coefficients of breath sounds in higher wavelet scales are small. Therefore, a wavelet transform based filter was utilized in which a multiresolution decomposition-reconstruction process filters the signal. Swallowing sounds are detected in the filtered signal. The proposed method was applied to the tracheal sound recordings of 15 healthy and 11 dysphagic subjects. The results were validated manually by visual inspection using airflow measurement and spectrogram of the sounds and auditory means. Experimental results prove that the proposed method is more accurate, efficient, and objective than the methods proposed previously. Swallowing sound detection may be employed in a system for automated swallowing assessment and diagnosis of swallowing disorders (dysphagia) by acoustical means.

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

“…The signal/transducer combination used in a given project is often chosen at the whim of the experimenter. Several studies have not systematically evaluated the variations of their instrumentation and falsely assume that swallowing vibrations and sounds are equivalent [16], [17], [19], [30], [31]. Four papers have been published in the last two decades that have investigated the differences between accelerometers and microphones in swallowing applications.…”

“…First, several researchers have applied wavelet denoising techniques to swallowing signals with some success [21], [30], [47], [51], [65]–[67], [84]–[87]. Wavelet denoising involves performing the wavelet decomposition of a time domain signal and then eliminating any detail components with a magnitude below a specific threshold; the assumption being that these components consist predominantly of noise data [117].…”

“…However, the log of the resulting data is then mapped onto a mel scale, a scale that plots data against its frequency in octaves rather than the raw frequency in Hertz, so as to better relate the data to human perception [98], [99]. There has also been some work done with wavelets in the time-frequency domain, where the signal is decomposed into a sum of scaled and amplified pulses [21], [30], [39], [49], [51], [65], [77], [84], [86], [87], [89], [98], [99], [115], [122]. Similar to the Fourier transform, the signals are characterized by the amplitude of these components and the relative amount of energy in specific frequency bands can be calculated [30], [49], [122].…”

A comparison between swallowing sounds and vibrations in patients with dysphagia

“…Microphones and accelerometers are two common detectors that have been recently used to record the swallowing signals [1, 2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 19, 20, 21, 22, 23, 24]. These approaches are based on the transduction of vibrations and sounds recorded from the upper aerodigestive tract structure during the act of swallowing into a voltage signal [5, 7, 13].…”

“…The focus of these investigations can be categorized into several main topics, such as the physiological sources of the signals [4, 5, 13, 14, 15, 16, 17, 27], the best placement site of microphones and accelerometers on the neck [1, 7, 27, 28], the best preprocessing methods for signals [20, 27, 29, 30, 31], characterization of the recorded signals [1, 2, 3, 6, 8, 9, 12, 18, 21, 23, 27, 32], segmentation of the swallowing signals [20, 22, 27, 33, 34, 35, 36], and classification of an abnormal swallow from a normal swallow [10, 27, 37, 38, 39]. …”