Abstract
Head movements can greatly affect swallowing accelerometry signals. In this paper, we implement a spline-based approach to remove low frequency components associated with these motions. Our approach was tested using both synthetic and real data. Synthetic signals were used to perform a comparative analysis of the spline-based approach with other similar techniques. Real data, obtained data from 408 healthy participants during various swallowing tasks, was used to analyze the processing accuracy with and without the spline-based head motions removal scheme. Specifically, we analyzed the segmentation accuracy and the effects of the scheme on statistical properties of these signals, as measured by the scaling analysis. The results of the numerical analysis showed that the spline-based technique achieves a superior performance in comparison to other existing techniques. Additionally, when applied to real data, we improved the accuracy of the segmentation process by achieving a 27% drop in the number of false negatives and a 30% drop in the number of false positives. Furthermore, the anthropometric trends in the statistical properties of these signals remained unaltered as shown by the scaling analysis, but the strength of statistical persistence was significantly reduced. These results clearly indicate that any future medical devices based on swallowing accelerometry signals should remove head motions from these signals in order to increase segmentation accuracy.
Highlights
Patients living with the effects of stroke or neurodegenerative conditions commonly encounter swallowing difficulties [1]
In ‘‘Splines as a tool for removal signal components associated with head movements’’ we describe how splines can be used for removal of low frequency components associated with head movements
As described in Section ‘‘Splines as a tool for removal signal components associated with head movements’’, it is rather clear that we can find an approximate value of the lower sampling frequency that will be consistent in terms of the error
Summary
Patients living with the effects of stroke or neurodegenerative conditions commonly encounter swallowing difficulties (dysphagia) [1]. Dysphagia occurs for various reasons in these patients (e.g., damage to the cranial nerves associated with the swallowing neural control centers) [2]–[4]. These patients have an increased risk for aspiration (the entry of material into the airway below the true vocal folds), which may cause asphyxiation and other severe consequences [5], [6]. The videofluoroscopic swallowing study (VFSS) is the current gold standard for detection and management of dysphagia [7]. Due to the presence of two-dimensional movement of the hyoid and the larynx during swallowing [10], [11], dual-axis accelerometers provide more accurate results [12], [13] than single-axis accelerometers [14]–[17]
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