Automated Detection and Evaluation of Swallowing Using a Combined EMG/Bioimpedance Measurement System

Schultheiss, Corinna; Schauer, Thomas; Nahrstaedt, Holger; Seidl, Rainer

doi:10.1155/2014/405471

Cited by 25 publications

(17 citation statements)

References 17 publications

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“…Acoustic based swallowing detection systems suffer from environmental noise and presence of surrounding speech/talking. Another possibility is to use bio-impedance measurement (such as Electromyography (EMG) or Electroglottography (EGG)) at larynx level for detection of swallowing related to food intake [ 36 , 37 ]. A piezoelectric strain sensor placed against the throat is subjected to physical strain during muscle contraction and relaxation caused by swallows [ 38 , 39 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Wearable Device for Food Intake and Physical Activity Recognition

Farooq

Sazonov

2016

Sensors

117

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Presence of speech and motion artifacts has been shown to impact the performance of wearable sensor systems used for automatic detection of food intake. This work presents a novel wearable device which can detect food intake even when the user is physically active and/or talking. The device consists of a piezoelectric strain sensor placed on the temporalis muscle, an accelerometer, and a data acquisition module connected to the temple of eyeglasses. Data from 10 participants was collected while they performed activities including quiet sitting, talking, eating while sitting, eating while walking, and walking. Piezoelectric strain sensor and accelerometer signals were divided into non-overlapping epochs of 3 s; four features were computed for each signal. To differentiate between eating and not eating, as well as between sedentary postures and physical activity, two multiclass classification approaches are presented. The first approach used a single classifier with sensor fusion and the second approach used two-stage classification. The best results were achieved when two separate linear support vector machine (SVM) classifiers were trained for food intake and activity detection, and their results were combined using a decision tree (two-stage classification) to determine the final class. This approach resulted in an average F1-score of 99.85% and area under the curve (AUC) of 0.99 for multiclass classification. With its ability to differentiate between food intake and activity level, this device may potentially be used for tracking both energy intake and energy expenditure.

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Section: Introductionmentioning

confidence: 99%

A Novel Wearable Device for Food Intake and Physical Activity Recognition

Farooq

Sazonov

2016

Sensors

117

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“…In addition, Schultheiss et al . reported to be able to detect swallowing reliably (sensitivity: 96·1%, specificity: 97·1%) as well as automatically using a combination of electromyography and bioimpedance. Compared with these previous studies, the accuracy of swallowing event using our automated swallowing detection system is equal to or more than that of previous reports.…”

Section: Discussionmentioning

confidence: 99%

“…Schultheiss et al . used a combination of electromyography and bioimpedance. In this study, participants were given full description of the study and practiced to push a foot pedal at the time of swallowing before recording.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, the assessment using swallowing sound is popular as a method of screening examination. Recording swallowing sound by using microphone or accelerometer makes it possible to detect swallowing event non‐invasively . Furthermore, the wave analysis of swallowing sounds has also been receiving attention, as the recording process is easy and non‐invasive.…”

Section: Introductionmentioning

confidence: 99%

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A novel automated detection system for swallowing sounds during eating and speech under everyday conditions

Fukuike

Kodama

Manda

et al. 2014

J of Oral Rehabilitation

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The wave analysis of swallowing sounds has been receiving attention because the recording process is easy and non-invasive. However, up until now, an expert has been needed to visually examine the entire recorded wave to distinguish swallowing from other sounds. The purpose of this study was to establish a methodology to automatically distinguish the sound of swallowing from sound data recorded during a meal in the presence of everyday ambient sound. Seven healthy participants (mean age: 26·7 ± 1·3 years) participated in this study. A laryngeal microphone and a condenser microphone attached to the nostril were used for simultaneous recording. Recoding took place while participants were taking a meal and talking with a conversational partner. Participants were instructed to step on a foot pedal trigger switch when they swallowed, representing self-enumeration of swallowing, and also to achieve six additional noise-making tasks during the meal in a randomised manner. The automated analysis system correctly detected 342 out of the 352 self-enumerated swallowing events (sensitivity: 97·2%) and 479 out of the 503 semblable wave periods of swallowing (specificity: 95·2%). In this study, the automated detection system for swallowing sounds using a nostril microphone was able to detect the swallowing event with high sensitivity and specificity even under the conditions of daily life, thus showing potential utility in the diagnosis or screening of dysphagic patients in future studies.

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“…T he EMG and BI signals measured at the larynx show specific patterns during swallowing 2 . The intention to swallow results in an increased activity of the EMG.…”

mentioning

confidence: 99%

Multisensor classification system for triggering FES in order to support voluntary swallowing

et al. 2016

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In dysphagia the ability of elevating the larynx and hyoid is usually impaired. Electromyography (EMG) and Bioimpedance (BI) measurements at the neck can be used to trigger functional electrical stimulation (FES) of swallowing related muscles. Nahrstaedt et al.1 introduced an algorithm to trigger the stimulation in phase with the voluntary swallowing to improve the airway closure and elevation speed of the larynx and hyoid. However, due to non-swallow related movements like speaking, chewing or head turning, stimulations might be unintentionally triggered. So far a switch was used to enable the BI/EMG-triggering of FES when the subject was ready to swallow, which is inconvenient for practical use. In this contribution, a range image camera system is introduced to obtain data of head, mouth, and jaw movements. This data is used to apply a second classification step to reduce the number of false stimulations. In experiments with healthy subjects, the amount of potential false stimulations could be reduced by 47% while 83% of swallowing intentions would have been correctely supported by FES.

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Automated Detection and Evaluation of Swallowing Using a Combined EMG/Bioimpedance Measurement System

Cited by 25 publications

References 17 publications

A Novel Wearable Device for Food Intake and Physical Activity Recognition

A Novel Wearable Device for Food Intake and Physical Activity Recognition

A novel automated detection system for swallowing sounds during eating and speech under everyday conditions

Multisensor classification system for triggering FES in order to support voluntary swallowing

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