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

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

doi:10.4081/ejtm.2016.6224

Cited by 6 publications

(2 citation statements)

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“…This is a clear advantage compared to methods that use only EMG or accelerometers for biofeedback of swallowing. In [3] we showed that BI and EMG patterns can be used to trigger FES in phase with voluntary swallow onset. This classification system was built on data of healthy subjects, for that reason it is not always sufficient for patients who have decreased EMG activity and a smaller change in BI.…”

Section: Introductionmentioning

confidence: 99%

“…This classification system was built on data of healthy subjects, for that reason it is not always sufficient for patients who have decreased EMG activity and a smaller change in BI. To achieve a suitable sensitivity of the classifier for a patient either additional sensor technology [3] must be employed or an adaptive classification system must be realized. The system proposed in this paper follows the second approach and is based on a set of classifiers with increasing sensitivity trained on data from healthy subjects.…”

Section: Introductionmentioning

confidence: 99%

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Self-adapting Classification System for Swallow Intention Detection in Dysphagia Therapy

Riebold

Nahrstaedt

Schauer

et al. 2019

Current Directions in Biomedical Engineering

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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. The height and speed of larynx elevation can be assessed by evaluating the BI during a swallow. For the triggering of an supporting FES and for biofeedback online detection of swallow onsets is required. Patients can practice by a gamified biofeedback to swallow harder, swallow in a timely manner or to maintain the larynx elevation for a longer time period (Mendelson maneuver). The success of the stimulation and biofeedback therapy as well as the acceptance by the patient strongly depends on the precise detection of swallow onsets. We have introduced a classification algorithm based on a random forest classifier to trigger FES in phase with voluntary swallowing based on EMG and BI. Although the classification is successful in healthy subjects, difficulties appear in the utilization on some patients. The reason for this can be found in a strongly varying residual swallow activity. Usually the activity of EMG and change in BI are smaller in patients compared to healthy subjects. Thus an adaption procedure is needed, that can be easily applied. In this paper we introduce an algorithm that is capable to find an optimal classifier for a patient in terms of sensitivity. The adaption algorithm uses a small number of recorded swallow onsets of a patient at the beginning of a therapy session to evaluate different classifiers and to pick the most suitable for the treatment. The set of random forest classifiers has been trained with data from healthy subjects by step wise shifting the class weights of swallows and non-swallows, yielding classifiers with different sensitivities. The evaluation is done using data from 41 patients. It showed that the sensitivity of the classification can be increased by 4 to 6 % in average compared to fixed classifiers, but up to 66 % for individual patients. Finally, we studied the effect this adaptive classifier in triggered stimulation therapy in a single dysphagia patient. Swallowing performance was measurements during one week of therapy consisting of eleven therapy session. An improvement of 40 and 63 % in larynx elevation and speed could be observed, respectively.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-adapting Classification System for Swallow Intention Detection in Dysphagia Therapy

Riebold

Nahrstaedt

Schauer

et al. 2019

Current Directions in Biomedical Engineering

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Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin

Schauer

2017

Annual Reviews in Control

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FES in Europe and beyond: Current Translational Research

et al. 2016

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Capacity of adult neural and muscle tissues to respond to external Electrical Stimulation (ES) is the biological basis for the development and implementation of mobility impairment physiotherapy protocols and of related assistive technologies, e.g, Functional Electrical Stimulation (FES). All body tissues, however, respond to electrical stimulation and, indeed, the most successful application of FES is electrical stimulation of the heart to revert or limit effects of arrhythmias (Pace-makers and Defibrillators). Here, we list and discuss results of FES current research activities, in particular those presented at 2016 Meetings: the PaduaMuscleDays, the Italian Institute of Myology Meeting, the 20th International Functional Electrical Stimulation Society (IFESS) conference held in Montpellier and the Vienna Workshop on FES. Several papers were recently e-published in the European Journal of Translational Myology as reports of meeting presentations. All the events and publications clearly show that FES research in Europe and beyond is alive and promisses translation of results into clinical management of a very large population of persons with deficiencies.

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

Cited by 6 publications

References 5 publications

Self-adapting Classification System for Swallow Intention Detection in Dysphagia Therapy

Self-adapting Classification System for Swallow Intention Detection in Dysphagia Therapy

Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin

FES in Europe and beyond: Current Translational Research

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