Computational Deglutition: Using Signal- and Image-Processing Methods to Understand Swallowing and Associated Disorders [Life Sciences]

Sejdić, Ervin; Malandraki, Georgia A.; Coyle, James L.

doi:10.1109/msp.2018.2875863

Cited by 31 publications

(20 citation statements)

References 19 publications

(20 reference statements)

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“…In fact, one could argue that even the recent work of [ 74 ] (which is the only one in Table 3 that uses a deep-learning-based approach for localization) upholds this protocol as closely as the oldest non-MUSIC work reported here [ 50 ], albeit with much better reported performance. This observation is consistent with what has been reported in recent search-and-rescue drone competitions, such as the IEEE Signal Processing Cup 2019 Student Competition [ 73 ].…”

Section: Auditory Perception From a Uav (Land To Air)supporting

confidence: 92%

“…Deep learning via CNNs could also be exploited to process different sensor data captured by microphones, cameras, and IMUs. Hence, a current challenge is that of generating reliable datasets [ 46 , 73 ] that include audio, visual, inertial, and other types of sensor data with their corresponding labeling and for concrete tasks, including some initial benchmarks and metrics. This would open the door to investigating and designing CNN architectures that could extract useful features to effectively and efficiently perform auditory perception.…”

Section: Discussion: Next Steps Of Audio With Uavsmentioning

confidence: 99%

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A Review on Auditory Perception for Unmanned Aerial Vehicles

Martínez-Carranza

Rascón

2020

Sensors

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Although a significant amount of work has been carried out for visual perception in the context of unmanned aerial vehicles (UAVs), not so much has been done regarding auditory perception. The latter can complement the observation of the environment that surrounds a UAV by providing additional information that can be used to detect, classify, and localize audio sources of interest. Motivated by the usefulness of auditory perception for UAVs, we present a literature review that discusses the audio techniques and microphone configurations reported in the literature. A categorization of techniques is proposed based on the role a UAV plays in the auditory perception (is it the one being perceived or is it the perceiver?), as well as a set of objectives that are more popularly aimed to be accomplished in the current literature (detection, classification, and localization). This literature review aims to provide a concise landscape of the most relevant works on auditory perception in the context of UAVs to date and provides insights into future avenues of research as a guide to those who are beginning to work in this field.

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Section: Auditory Perception From a Uav (Land To Air)supporting

confidence: 92%

Section: Discussion: Next Steps Of Audio With Uavsmentioning

confidence: 99%

A Review on Auditory Perception for Unmanned Aerial Vehicles

Martínez-Carranza

Rascón

2020

Sensors

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“…Swallowing is essential for feeding and drinking, whereas breathing controls vital pulmonary gas exchange (oxygen uptake and excretion of carbon dioxide). Although these motor behaviours serve different biological functions, they both use a common oropharyngeal passage and thereby share a variety of upper airway muscles to control respiratory airflow (Bartlett Jr (1989), Dutschmann and Paton (2002b), Dutschmann and Paton (2002a), Dutschmann et al (2014)) or the transportation of food or fluids from the oral cavity to the oesophagus and stomach (Miller (1986), Sejdic et al (2018), Allen (2012), Jean (2001)). Thus, the motor activity underlying breathing and swallowing needs to be tightly coordinated (Hårdemark Cedborg et al (2009), Bolser et al (2013), Bautista and Dutschmann (2014), Huff et al (2020)).…”

Section: Introductionmentioning

confidence: 99%

Modelling of synaptic interactions between two brainstem half-centre oscillators that coordinate breathing and swallowing

Tolmachev

et al. 2021

Preprint

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Respiration and swallowing are vital orofacial motor behaviours that require the coordination of the activity of two brainstem central pattern generators (r-CPG, sw-CPG). Here, we use computational modelling to further elucidate the neural substrate for breathing-swallowing coordination. We progressively construct several computational models of the breathing-swallowing circuit, starting from two interacting half-centre oscillators for each CPG. The models are based exclusively on neuronal nodes with spike-frequency adaptation, having a parsimonious description of intrinsic properties. These basic models undergo a stepwise integration of synaptic connectivity between central sensory relay, sw- and r-CPG neuron populations to match experimental data obtained in a perfused brainstem preparation. In the model, stimulation of the superior laryngeal nerve (SLN, 10s) reliably triggers sequential swallowing with concomitant glottal closure and suppression of inspiratory activity, consistent with the motor pattern in experimental data. Short SLN stimulation (100ms) evokes single swallows and respiratory phase resetting yielding similar experimental and computational phase response curves. Subsequent phase space analysis of model dynamics provides further understanding of SLN-mediated respiratory phase resetting. Consistent with experiments, numerical circuit-busting simulations show that deletion of ponto-medullary synaptic interactions triggers apneusis and eliminates glottal closure during sequential swallowing. Additionally, systematic variations of the synaptic strengths of distinct network connections predict vulnerable network connections that can mediate clinically relevant breathing-swallowing disorders observed in the elderly and patients with neurodegenerative disease. Thus, the present model provides novel insights that can guide future experiments and the development of efficient treatments for prevalent breathing-swallowing disorders.Key pointsThe coordination of breathing and swallowing depends on synaptic interactions between two functionally distinct central pattern generators (CPGs) in the dorsal and ventral brainstem.We model both CPGs as half-centre oscillators with spike-frequency adaptation to identify the minimal connectivity sufficient to mediate physiologic breathing-swallowing interactions.The resultant computational model(s) can generate sequential swallowing patterns including concomitant glottal closure during simulated 10s stimulation of the superior laryngeal nerve (SLN) consistent with experimental data.In silico, short (100 ms) SLN stimulation triggers a single swallow which modulates the respiratory cycle duration consistent with experimental recordings.By varying the synaptic connectivity strengths between the two CPGs and the sensory relay neurons, and by inhibiting specific nodes of the network, the model predicts vulnerable network connections that may mediate clinically relevant breathing-swallowing disorders.

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“…To design such a system, one of the key issues is developing a processing method for the signal obtained from cervical auscultation [7].…”

Section: Introductionmentioning

confidence: 99%

Classifying Dysphagic Swallowing Sounds with Support Vector Machines

et al. 2020

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Swallowing sounds from cervical auscultation include information related to the swallowing function. Several studies have been conducted on the screening tests of dysphagia. The literature shows a significant difference between the characteristics of swallowing sounds obtained from different subjects (e.g., healthy and dysphagic subjects; young and old adults). These studies demonstrate the usefulness of swallowing sounds during dysphagic screening. However, the degree of classification for dysphagia based on swallowing sounds has not been thoroughly studied. In this study, we investigate the use of machine learning for classifying swallowing sounds into various types, such as normal swallowing or mild, moderate, and severe dysphagia. In particular, swallowing sounds were recorded from patients with dysphagia. Support vector machines (SVMs) were trained using some features extracted from the obtained swallowing sounds. Moreover, the accuracy of the classification of swallowing sounds using the trained SVMs was evaluated via cross-validation techniques. In the two-class scenario, wherein the swallowing sounds were divided into two categories (viz. normal and dysphagic subjects), the maximum F-measure was 78.9%. In the four-class scenario, where the swallowing sounds were divided into four categories (viz. normal subject, and mild, moderate, and severe dysphagic subjects), the F-measure values for the classes were 65.6%, 53.1%, 51.1%, and 37.1%, respectively.

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Computational Deglutition: Using Signal- and Image-Processing Methods to Understand Swallowing and Associated Disorders [Life Sciences]

Cited by 31 publications

References 19 publications

A Review on Auditory Perception for Unmanned Aerial Vehicles

A Review on Auditory Perception for Unmanned Aerial Vehicles

Modelling of synaptic interactions between two brainstem half-centre oscillators that coordinate breathing and swallowing

Classifying Dysphagic Swallowing Sounds with Support Vector Machines

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