A Method for Removal of Low Frequency Components Associated with Head Movements from Dual-Axis Swallowing Accelerometry Signals

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

A comparative analysis of swallowing accelerometry and sounds during saliva swallows

Background: Accelerometry (the measurement of vibrations) and auscultation (the measurement of sounds) are both non-invasive techniques that have been explored for their potential to detect abnormalities in swallowing. The differences between these techniques and the information they capture about swallowing have not previously been explored in a direct comparison. Methods: In this study, we investigated the differences between dual-axis swallowing accelerometry and swallowing sounds by recording data from adult participants and calculating a number of time and frequency domain features. During the experiment, 55 participants (ages 18-65) were asked to complete five saliva swallows in a neutral head position. The resulting data was processed using previously designed techniques including wavelet denoising, spline filtering, and fuzzy means segmentation. The pre-processed signals were then used to calculate 9 time, frequency, and time-frequency domain features for each independent signal. Wilcoxon signed-rank and Wilcoxon rank-sum tests were utilized to compare feature values across transducers and patient demographics, respectively. Results: In addition to finding a number of features that varied between male and female participants, our statistical analysis determined that the majority of our chosen features were statistically significantly different across the two sensor methods and that the dependence on within-subject factors varied with the transducer type. However, a regression analysis showed that age accounted for an insignificant amount of variation in our signals. Conclusions: We conclude that swallowing accelerometry and swallowing sounds provide different information about deglutition despite utilizing similar transduction methods. This contradicts past assumptions in the field and necessitates the development of separate analysis and processing techniques for swallowing sounds and vibrations

The effects of head movements and fluids with increasing viscosity on swallowing sounds

Cervical auscultation (CA) is an affordable, non-invasive technique for diagnosis of dysphagia (swallowing difficulties). CA involves swallowing characterization either via accelerometers or microphones. Though characteristics of the swallowing sound are well known, there is also need for a complete understanding of the baseline characteristics of the device, as well as any in influence of the head motion, age and gender. Also, the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. In order to examine these parameters, data was collected from 56 healthy participants. At first, they performed six different tasks with absence of swallowing, than they would complete five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted. Statistical test for baseline characteristic of swallowing sound showed that only the skewness and peak frequency did not possess statistical difference for all tasks. This results of the peak frequency indicates that head movement does not significantly affect the swallowing sound, and there is no need for removing those components. However, there is no observed gender, but age dependence was found in the swallowing sound. Nevertheless, participant's age should be considered in the future studies about swallowing sound. The same test was used for investigating influence dependence, and it demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of differences were found between water and fluids with higher viscosity. Almost no significant difference were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher regularity and predictability as demonstrated by the information theoretic features, and a lower frequency content as demonstrated by features in the frequency domain. Therefore, viscosity of fluids should be considered in future investigations involving swallowing sounds

Cervical Auscultation for the Identification of Swallowing Difficulties

Swallowing difficulties, commonly referred to as dysphagia, affect thousands of Americans every year. They have a multitude of causes, but in general they are known to increase the risk of aspiration when swallowing in addition to other physiological effects. Cervical auscultation has been recently applied to detect such difficulties non-invasively and various techniques for analysis and processing of the recorded signals have been proposed. We attempted to further this research in three key areas. First, we characterized swallows with regards to a multitude of time, frequency, and time-frequency features while paying special attention to the differences between swallows from healthy adults and safe dysphagic swallows as well as safe and unsafe dysphagic swallows. Second, we attempted to utilize deep belief networks in order to classify these states automatically and without the aid of a concurrent videofluoroscopic examination. Finally, we sought to improve some of the signal processing techniques used in this field. We both implemented the DBSCAN algorithm to better segment our physiological signals as well as applied the matched complex wavelet transform to cervical auscultation data in order to improve its quality for mathematical analysis

The effects of increased fluid viscosity on swallowing sounds in healthy adults

Background: Cervical auscultation (CA) is an affordable, non-invasive technique used to observe sounds occurring during swallowing. CA involves swallowing characterization via stethoscopes or microphones, while accelerometers can detect other vibratory signals. While the effects of fluid viscosity on swallowing accelerometry signals is well understood, there are still open questions about these effects on swallowing sounds. Therefore, this study investigated the influence of fluids with increasing thickness on swallowing sound characteristics.Method: We collected swallowing sounds and swallowing accelerometry signals from 56 healthy participants. Each participant completed five water swallows, five swallows of nectar-thick apple juice, and five swallows of honey-thick apple juice. These swallows were completed in neutral head and chin-tuck head positions. After pre-processing of collected signals, a number of features in time, frequency and time-frequency domains were extracted.Results: Our numerical analysis demonstrated that significant influence of viscosity was found in most of the features. In general, features extracted from swallows in the neutral head position were affected more than swallows from the chin-tuck position. Furthermore, most of the differences were found between water and fluids with higher viscosity. Almost no significant differences were found between swallows involving nectar-thick and honey-thick apple juices. Our results also showed that thicker fluids had higher acoustic regularity and predictability as demonstrated by the information-theoretic features, and a lower frequency content as demonstrated by features in the frequency domain.Conclusions: According to these results, we can conclude that viscosity of fluids should be considered in future investigations involving swallowing sounds. © 2013 Jestrovićet al.; licensee BioMed Central Ltd

Quantitative and Portable Instrumentation for the Screening and Assessment of Pharyngeal Dysphagia

Dysphagia, the dysfunction of swallowing, is a common complication of neurological conditions, and presents increased risk of morbidity, mortality, and may critically reduce the subject's quality of life. The early detection of dysphagia is essential to maintaining the subject's health, while accurate diagnosis of the physiological source of dysphagia is essential for successful treatment. 'Silent' dysphagia, where there are no outward symptoms, is a particular concern, as many screening processes rely on patients self-reporting difficulties. A gap exists in available instrumentation, between simple techniques, which are subjective and require experience to employ, and highly sophisticated instruments, which are invasive to the patient and resource intensive. This thesis addresses this by exploring the possibility of developing instrumentation techniques which present the potential for portable, non-invasive solutions, which are relatively inexpensive and require dramatically less expertise to employ, enabling more effective dysphagia screening procedures to be introduced to clinical practice. This thesis develops the means for measuring laryngeal motion by the use of a non-invasive throat-mounted sensor in four stages: Firstly, a mathematical and a physical model of the larynx are constructed to develop our understanding of the relationship between laryngeal motion and sensor signals; secondly, swallowing sensor data was captured from 23 healthy participants; thirdly, the data from the participants was analysed to evaluate alternative data processing techniques, and to develop an understanding of practical factors deriving from inter-personal variations in physiology; finally, a prototype instrument was constructed, based on specifications evolved from our analysis. Initial testing of the prototype instrument has demonstrated the validity of the concepts employed in its design: it is straight-forward to use, compact, portable, non-invasive, and can be used to quantitatively measure laryngeal elevation in a repeatable fashion

Rehabilitation Engineering in Parkinson's disease

Impairment of postural control is a common consequence of Parkinson's disease (PD) that becomes more and more critical with the progression of the disease, in spite of the available medications. Postural instability is one of the most disabling features of PD and induces difficulties with postural transitions, initiation of movements, gait disorders, inability to live independently at home, and is the major cause of falls. Falls are frequent (with over 38% falling each year) and may induce adverse consequences like soft tissue injuries, hip fractures, and immobility due to fear of falling. As the disease progresses, both postural instability and fear of falling worsen, which leads patients with PD to become increasingly immobilized. The main aims of this dissertation are to: 1) detect and assess, in a quantitative way, impairments of postural control in PD subjects, investigate the central mechanisms that control such motor performance, and how these mechanism are affected by levodopa; 2) develop and validate a protocol, using wearable inertial sensors, to measure postural sway and postural transitions prior to step initiation; 3) find quantitative measures sensitive to impairments of postural control in early stages of PD and quantitative biomarkers of disease progression; and 4) test the feasibility and effects of a recently-developed audio-biofeedback system in maintaining balance in subjects with PD. In the first set of studies, we showed how PD reduces functional limits of stability as well as the magnitude and velocity of postural preparation during voluntary, forward and backward leaning while standing. Levodopa improves the limits of stability but not the postural strategies used to achieve the leaning. Further, we found a strong relationship between backward voluntary limits of stability and size of automatic postural response to backward perturbations in control subjects and in PD subjects ON medication. Such relation might suggest that the central nervous system presets postural response parameters based on perceived maximum limits and this presetting is absent in PD patients OFF medication but restored with levodopa replacement. Furthermore, we investigated how the size of preparatory postural adjustments (APAs) prior to step initiation depend on initial stance width. We found that patients with PD did not scale up the size of their APA with stance width as much as control subjects so they had much more difficulty initiating a step from a wide stance than from a narrow stance. This results supports the hypothesis that subjects with PD maintain a narrow stance as a compensation for their inability to sufficiently increase the size of their lateral APA to allow speedy step initiation in wide stance. In the second set of studies, we demonstrated that it is possible to use wearable accelerometers to quantify postural performance during quiet stance and step initiation balance tasks in healthy subjects. We used a model to predict center of pressure displacements associated with accelerations at the upper and lower back and thigh. This approach allows the measurement of balance control without the use of a force platform outside the laboratory environment. We used wearable accelerometers on a population of early, untreated PD patients, and found that postural control in stance and postural preparation prior to a step are impaired early in the disease when the typical balance and gait intiation symptoms are not yet clearly manifested. These novel results suggest that technological measures of postural control can be more sensitive than clinical measures. Furthermore, we assessed spontaneous sway and step initiation longitudinally across 1 year in patients with early, untreated PD. We found that changes in trunk sway, and especially movement smoothness, measured as Jerk, could be used as an objective measure of PD and its progression. In the third set of studies, we studied the feasibility of adapting an existing audio-biofeedback device to improve balance control in patients with PD. Preliminary results showed that PD subjects found the system easy-to-use and helpful, and they were able to correctly follow the audio information when available. Audiobiofeedback improved the properties of trunk sway during quiet stance. Our results have many implications for i) the understanding the central mechanisms that control postural motor performance, and how these mechanisms are affected by levodopa; ii) the design of innovative protocols for measuring and remote monitoring of motor performance in the elderly or subjects with PD; and iii) the development of technologies for improving balance, mobility, and consequently quality of life in patients with balance disorders, such as PD patients with augmented biofeedback paradigms

Noninvasive Dynamic Characterization of Swallowing Kinematics and Impairments in High Resolution Cervical Auscultation via Deep Learning

Swallowing is a complex sensorimotor activity by which food and liquids are transferred from the oral cavity to the stomach. Swallowing requires the coordination between multiple subsystems which makes it subject to impairment secondary to a variety of medical or surgically related conditions. Dysphagia refers to any swallowing disorder and is common in patients with head and neck cancer and neurological conditions such as stroke. Dysphagia affects nearly 9 million adults and causes death for more than 60,000 yearly in the US. In this research, we utilize advanced signal processing techniques with sensor technology and deep learning methods to develop a noninvasive and widely available tool for the evaluation and diagnosis of swallowing problems. We investigate the use of modern spectral estimation methods in addition to convolutional recurrent neural networks to demarcate and localize the important swallowing physiological events that contribute to airway protection solely based on signals collected from non-invasive sensors attached to the anterior neck. These events include the full swallowing activity, upper esophageal sphincter opening duration and maximal opening diameter, and aspiration. We believe that combining sensor technology and state of the art deep learning architectures specialized in time series analysis, will help achieve great advances for dysphagia detection and management in terms of non-invasiveness, portability, and availability. Like never before, such advances will enable patients to get continuous feedback about their swallowing out of standard clinical care setting which will extremely facilitate their daily activities and enhance the quality of their lives

Gait analysis in neurological populations: Progression in the use of wearables

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies, and provide possible future directions. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature