Limited receptive area neural classifier for recognition of swallowing sounds using short-time Fourier transform

Makeyev, Oleksandr; Sazonov, Edward; Schuckers, Stephanie; Melanson, Edward L.; Neuman, Michael R.

doi:10.1109/ijcnn.2007.4371197

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…One method that has received significant attention is the neural network technique. The signal is windowed and then multiple time-varying features are calculated before being fed into the neural network [24, 25, 26, 27]. After sufficient training this network should be able to differentiate between periods of time where swallowing activity is present or absent based on the values of the inputs [24, 25, 26, 27].…”

Section: Introductionmentioning

confidence: 99%

“…The signal is windowed and then multiple time-varying features are calculated before being fed into the neural network [24, 25, 26, 27]. After sufficient training this network should be able to differentiate between periods of time where swallowing activity is present or absent based on the values of the inputs [24, 25, 26, 27]. However, it is very computationally intensive and researchers are still debating what features are valid inputs to use for the purposes of segmentation [24, 25, 26, 27].…”

Section: Introductionmentioning

confidence: 99%

“…After sufficient training this network should be able to differentiate between periods of time where swallowing activity is present or absent based on the values of the inputs [24, 25, 26, 27]. However, it is very computationally intensive and researchers are still debating what features are valid inputs to use for the purposes of segmentation [24, 25, 26, 27]. More computationally simple techniques have also been utilized, such as thresholding the time domain signal, but their accuracy has been questionable [28, 29, 30, 24, 31].…”

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals

Dudik¹,

Kurosu²,

Coyle³

et al. 2015

Computers in Biology and Medicine

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Background Cervical auscultation with high resolution sensors is currently under consideration as a method of automatically screening for specific swallowing abnormalities. To be clinically useful without human involvement, any devices based on cervical auscultation should be able to detect specified swallowing events in an automatic manner. Methods In this paper, we comparatively analyze the density-based spatial clustering of applications with noise algorithm (DBSCAN), a k-means based algorithm, and an algorithm based on quadratic variation as methods of differentiating periods of swallowing activity from periods of time without swallows. These algorithms utilized swallowing vibration data exclusively and compared the results to a gold standard measure of swallowing duration. Data was collected from 23 subjects that were actively suffering from swallowing difficulties. Results Comparing the performance of the DBSCAN algorithm with a proven segmentation algorithm that utilizes k-means clustering demonstrated that the DBSCAN algorithm had a higher sensitivity and correctly segmented more swallows. Comparing its performance with a threshold-based algorithm that utilized the quadratic variation of the signal showed that the DBSCAN algorithm offered no direct increase in performance. However, it offered several other benefits including a faster run time and more consistent performance between patients. All algorithms showed noticeable differen-tiation from the endpoints provided by a videofluoroscopy examination as well as reduced sensitivity. Conclusions In summary, we showed that the DBSCAN algorithm is a viable method for detecting the occurrence of a swallowing event using cervical auscultation signals, but significant work must be done to improve its performance before it can be implemented in an unsupervised manner.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals

Dudik¹,

Kurosu²,

Coyle³

et al. 2015

Computers in Biology and Medicine

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“…While some place it as low as 0.1 Hz in order to maintain a “pure” signal [21], [49], [51], [65], [66], [83], [86], [90], others place it as high as 30 Hz or more in order to eliminate motion artifacts and other low frequency noise [39], [42], [44], [60]–[62], [69]. Since similar bandlimits have yet to be identified for swallowing sounds, studies which use a microphone simply limit the recorded signal to either the human audible range [21], [32], [33], [37], [40], [46], [48], [67], [76]–[78], [86], [89], [95], [97]–[100], [102], [103], [110], [112]–[116] or the range of common stethoscopes used in bedside assessments [13], [22], [28], [39], [56], [69], [73], [88], [91], [92]. Typically, this bare minimum amount of signal processing has traditionally been done for one of two reasons.…”

Section: Signal Conditioningmentioning

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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“…The basis of this research involves sound classification techniques. Several techniques have been proposed to solve the problem of sound classification [17–25]. Most of these sound classification methods consist of two processing steps: feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

Single‐signal entity approach for sung word recognition with artificial neural network and time–frequency audio features

Khunarsa

2017

J. eng.

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Limited receptive area neural classifier for recognition of swallowing sounds using short-time Fourier transform

Cited by 11 publications

References 16 publications

A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals

A comparative analysis of DBSCAN, K-means, and quadratic variation algorithms for automatic identification of swallows from swallowing accelerometry signals

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

Single‐signal entity approach for sung word recognition with artificial neural network and time–frequency audio features

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