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Tom Chau - One of the best experts on this subject based on the ideXlab platform.
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Automatic discrimination between cough and non-cough Accelerometry signal artefacts
Biomedical Signal Processing and Control, 2019Co-Authors: Helia Mohammadi, Catriona M Steele, Ali-akbar Samadani, Tom ChauAbstract:Abstract Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical Accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates Accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 ± 0.12% when discriminating between voluntary cough and rest Accelerometry signals. An accuracy of 90 ± 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 ± 3.6% and 80.3 ± 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer.
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Classification of Penetration--Aspiration Versus Healthy Swallows Using Dual-Axis Swallowing Accelerometry Signals in Dysphagic Subjects
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Ervin Sejdić, Catriona M Steele, Tom ChauAbstract:Swallowing Accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing Accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing Accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing Accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing Accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.
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Noninvasive Detection of Thin-Liquid Aspiration Using Dual-Axis Swallowing Accelerometry
Dysphagia, 2012Co-Authors: Catriona M Steele, Ervin Sejdic, Tom ChauAbstract:Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive Accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical Accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the Accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that Accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.
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Quantitative classification of pediatric swallowing through Accelerometry
Journal of Neuroengineering and Rehabilitation, 2012Co-Authors: Merey Celeste, Ervin Sejdic, Kushki Azadeh, Glenn Berall, Tom ChauAbstract:Background Dysphagia or swallowing disorder negatively impacts a child’s health and development. The gold standard of dysphagia detection is videofluoroscopy which exposes the child to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function. Swallowing Accelerometry is the non-invasive measurement of cervical vibrations during swallowing and may provide a portable and cost-effective bedside alternative. In particular, dual-axis swallowing Accelerometry has demonstrated screening potential in older persons with neurogenic dysphagia, but the technique has not been evaluated in the pediatric population.
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compressive sampling of swallowing Accelerometry signals using time frequency dictionaries based on modulated discrete prolate spheroidal sequences
EURASIP Journal on Advances in Signal Processing, 2012Co-Authors: Ervin Sejdic, Luis F Chaparro, Catriona M Steele, Tom ChauAbstract:Monitoring physiological functions such as swallowing often generates large volumes of samples to be stored and processed, which can introduce computational constraints especially if remote monitoring is desired. In this article, we propose a compressive sensing (CS) algorithm to alleviate some of these issues while acquiring dual-axis swallowing Accelerometry signals. The proposed CS approach uses a time-frequency dictionary where the members are modulated discrete prolate spheroidal sequences (MDPSS). These waveforms are obtained by modulation and variation of discrete prolate spheroidal sequences (DPSS) in order to reflect the time-varying nature of swallowing acclerometry signals. While the modulated bases permit one to represent the signal behavior accurately, the matching pursuit algorithm is adopted to iteratively decompose the signals into an expansion of the dictionary bases. To test the accuracy of the proposed scheme, we carried out several numerical experiments with synthetic test signals and dual-axis swallowing Accelerometry signals. In both cases, the proposed CS approach based on the MDPSS yields more accurate representations than the CS approach based on DPSS. Specifically, we show that dual-axis swallowing Accelerometry signals can be accurately reconstructed even when the sampling rate is reduced to half of the Nyquist rate. The results clearly indicate that the MDPSS are suitable bases for swallowing Accelerometry signals.
Nigel H. Lovell - One of the best experts on this subject based on the ideXlab platform.
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Spectral Analysis of Accelerometry Signals From a Directed-Routine for Falls-Risk Estimation
IEEE Transactions on Biomedical Engineering, 2011Co-Authors: Stephen J. Redmond, Ning Wang, Michael R. Narayanan, Fernando Blumenkron, Nigel H. LovellAbstract:Injurious falls are a prevalent and serious problem faced by a growing elderly population. Accurate assessment and long-term monitoring of falls-risk could prove useful in the prevention of falls, by identifying those at risk of falling early so targeted intervention may be prescribed. Previous studies have demonstrated the feasibility of using triaxial Accelerometry to estimate the risk of a person falling in the near future, by characterizing their movement as they execute a restricted sequence of predefined movements in an unsupervised environment, termed a directed routine. This study presents an improvement on this previously published system, which relied explicitly on time-domain features extracted from the Accelerometry signals. The proposed improvement incorporates features derived from spectral analysis of the same Accelerometry signals; in particular the harmonic ratios between signal harmonics and the fundamental frequency component are used. Employing these additional frequency-domain features, in combination with the previously reported time-domain features, an increase in the observed correlation with the clinical gold-standard risk of falling, from ρ = 0.81 to ρ = 0.96, was achieved when using manually annotated event segmentation markers; using an automated algorithm to segment the signals gave corresponding results of ρ = 0.73 and ρ = 0.99, before and after the inclusion of spectral features. The strong correlation with falls-risk observed in this preliminary study further supports the feasibility of using an unsupervised assessment of falls-risk in the home environment.
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Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Federico Bianchi, Stephen J. Redmond, Michael R. Narayanan, Sergio Cerutti, Nigel H. LovellAbstract:Falls and fall related injuries are a significant cause of morbidity, disability, and health care utilization, particularly among the age group of 65 years and over. The ability to detect falls events in an unsupervised manner would lead to improved prognoses for falls victims. Several wearable Accelerometry and gyroscope-based falls detection devices have been described in the literature; however, they all suffer from unacceptable false positive rates. This paper investigates the augmentation of such systems with a barometric pressure sensor, as a surrogate measure of altitude, to assist in discriminating real fall events from normal activities of daily living. The acceleration and air pressure data are recorded using a wearable device attached to the subject's waist and analyzed offline. The study incorporates several protocols including simulated falls onto a mattress and simulated activities of daily living, in a cohort of 20 young healthy volunteers (12 male and 8 female; age: 23.7 ±3.0 years). A heuristically trained decision tree classifier is used to label suspected falls. The proposed system demonstrated considerable improvements in comparison to an existing Accelerometry-based technique; showing an accuracy, sensitivity and specificity of 96.9%, 97.5%, and 96.5%, respectively, in the indoor environment, with no false positives generated during extended testing during activities of daily living. This is compared to 85.3%, 75%, and 91.5% for the same measures, respectively, when using Accelerometry alone. The increased specificity of this system may enhance the usage of falls detectors among the elderly population.
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Energy expenditure estimation using triaxial Accelerometry and barometric pressure measurement
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010Co-Authors: Matteo Voleno, Stephen J. Redmond, Sergio Cerutti, Nigel H. LovellAbstract:Energy expenditure (EE) is a parameter of great relevance in studies involving the assessment of physical activity. However, most reliable techniques for EE estimation are impractical for use in free-living environments, and those which are practically useful often poorly track EE when the subject is working to change their altitude, for example when ascending or descending stairs or slopes. The aim of this study is to evaluate the utility of adding barometric pressure related features, as a surrogate measure for altitude, to existing Accelerometry related features to estimate the subject's EE. The EE estimation system described is based on a triaxial accelerometer (triax) and a barometric pressure sensor. The device is wireless, with Bluetooth connectivity for data retrieval, and is mounted at the subject's waist. Using a number of features extracted from the triax and barometric pressure signals, a linear model is trained for EE estimation. This EE estimation model is compared to its counterpart, which solely utilizes Accelerometry signals. A protocol (comprising lying, sitting, standing, walking phases) was performed by 13 healthy volunteers (8 male and 5 female; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system. The model incorporating barometric pressure information estimated the oxygen uptake with the lowest mean square error of 4.5±1.7 (mlO2.min-1.kg-1)2, in comparison to 7.1±2.3 (mlO2.min-1.kg-1)2 using only Accelerometry-based features.
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An investigation of the impact of gait segmentation on Accelerometry-based inclined terrain classification
IET Irish Signals and Systems Conference (ISSC 2009), 2009Co-Authors: Ning Wang, Eliathamby Ambikairajah, Stephen J. Redmond, Branko G. Celler, Nigel H. LovellAbstract:Traditional methods of energy expenditure estimation, in the free-living environment, attempted using Accelerometry operate without knowledge of the slope of the terrain which is being traversed. The ability to recognise the gradient of the walking surface will most likely improve upon these simplistic energy estimates. This paper expands upon previous work in this area, and investigates the benefit of step-by-step segmentation of the Accelerometry signal in classifying the various gradients. Tri-axial Accelerometry signals from 12 subjects, performing 30 s of walking on 4 different gradients (up and down paved ramps with gradients of 4.8% and 17.2%), were collected. A feature subset selection search procedure was applied to find the optimal subset of 65 extracted features which maximise the classification accuracy, performed with a Gaussian Mixture Model (GMM) classifier, as estimated using six-fold cross-validation. An overall classification accuracy of 94.83% was achieved using 13 features, for the four-class problem. There was an improvement of 4.1% upon the same classification task, without knowledge of the start/end times of individual steps, indicating that segmentation of the Accelerometry signals at a step-by-step resolution is important for the automated classification of terrain gradient during walking.
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An investigation of the impact of gait segmentation on Accelerometry-based inclined terrain classification
IET Irish Signals and Systems Conference (ISSC 2009), 2009Co-Authors: Ning Wang, Eliathamby Ambikairajah, Stephen J. Redmond, Branko G. Celler, Nigel H. LovellAbstract:Traditional methods of energy expenditure estimation, in the free-living environment, attempted using Accelerometry operate without knowledge of the slope of the terrain which is being traversed. The ability to recognise the gradient of the walking surface will most likely improve upon these simplistic energy estimates. This paper expands upon previous work in this area, and investigates the benefit of step-by-step segmentation of the Accelerometry signal in classifying the various gradients. Tri-axial Accelerometry signals from 12 subjects, performing 30 s of walking on 4 different gradients (up and down paved ramps with gradients of 4.8% and 17.2%), were collected. A feature subset selection search procedure was applied to find the optimal subset of 65 extracted features which maximise the classification accuracy, performed with a Gaussian Mixture Model (GMM) classifier, as estimated using six-fold cross-validation. An overall classification accuracy of 94.83% was achieved using 13 features, for the four-class problem. There was an improvement of 4.1% upon the same classification task, without knowledge of the start/end times of individual steps, indicating that segmentation of the Accelerometry signals at a step-by-step resolution is important for the automated classification of terrain gradient during walking. (6 pages)
Catriona M Steele - One of the best experts on this subject based on the ideXlab platform.
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Automatic discrimination between cough and non-cough Accelerometry signal artefacts
Biomedical Signal Processing and Control, 2019Co-Authors: Helia Mohammadi, Catriona M Steele, Ali-akbar Samadani, Tom ChauAbstract:Abstract Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical Accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates Accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 ± 0.12% when discriminating between voluntary cough and rest Accelerometry signals. An accuracy of 90 ± 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 ± 3.6% and 80.3 ± 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer.
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Classification of Penetration--Aspiration Versus Healthy Swallows Using Dual-Axis Swallowing Accelerometry Signals in Dysphagic Subjects
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Ervin Sejdić, Catriona M Steele, Tom ChauAbstract:Swallowing Accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing Accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing Accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing Accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing Accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.
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Noninvasive Detection of Thin-Liquid Aspiration Using Dual-Axis Swallowing Accelerometry
Dysphagia, 2012Co-Authors: Catriona M Steele, Ervin Sejdic, Tom ChauAbstract:Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive Accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical Accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the Accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that Accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.
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compressive sampling of swallowing Accelerometry signals using time frequency dictionaries based on modulated discrete prolate spheroidal sequences
EURASIP Journal on Advances in Signal Processing, 2012Co-Authors: Ervin Sejdic, Luis F Chaparro, Catriona M Steele, Tom ChauAbstract:Monitoring physiological functions such as swallowing often generates large volumes of samples to be stored and processed, which can introduce computational constraints especially if remote monitoring is desired. In this article, we propose a compressive sensing (CS) algorithm to alleviate some of these issues while acquiring dual-axis swallowing Accelerometry signals. The proposed CS approach uses a time-frequency dictionary where the members are modulated discrete prolate spheroidal sequences (MDPSS). These waveforms are obtained by modulation and variation of discrete prolate spheroidal sequences (DPSS) in order to reflect the time-varying nature of swallowing acclerometry signals. While the modulated bases permit one to represent the signal behavior accurately, the matching pursuit algorithm is adopted to iteratively decompose the signals into an expansion of the dictionary bases. To test the accuracy of the proposed scheme, we carried out several numerical experiments with synthetic test signals and dual-axis swallowing Accelerometry signals. In both cases, the proposed CS approach based on the MDPSS yields more accurate representations than the CS approach based on DPSS. Specifically, we show that dual-axis swallowing Accelerometry signals can be accurately reconstructed even when the sampling rate is reduced to half of the Nyquist rate. The results clearly indicate that the MDPSS are suitable bases for swallowing Accelerometry signals.
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The effects of head movement on dual-axis cervical Accelerometry signals
BMC Research Notes, 2010Co-Authors: Ervin Sejdic, Catriona M Steele, Tom ChauAbstract:Background Head motions can severely affect dual-axis cervical acceloremetry signals. A complete understanding of the effects of head motion is required before a robust Accelerometry-based medical device can be developed. In this paper, we examine the spectral characteristics of dual-axis cervical Accelerometry signals in the absence of swallowing but in the presence of head motions.
Ervin Sejdic - One of the best experts on this subject based on the ideXlab platform.
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The effects of compressive sensing on extracted features from tri-axial swallowing Accelerometry signals
Proceedings of SPIE--the International Society for Optical Engineering, 2016Co-Authors: Ervin Sejdic, Faezeh Movahedi, Zhenwei Zhang, Atsuko Kurosu, James L. CoyleAbstract:Acquiring swallowing Accelerometry signals using a comprehensive sensing scheme may be a desirable approach for monitoring swallowing safety for longer periods of time. However, it needs to be insured that signal characteristics can be recovered accurately from compressed samples. In this paper, we considered this issue by examining the effects of the number of acquired compressed samples on the calculated swallowing Accelerometry signal features. We used tri-axial swallowing Accelerometry signals acquired from seventeen stroke patients (106 swallows in total). From acquired signals, we extracted typically considered signal features from time, frequency and time-frequency domains. Next, we compared these features from the original signals (sampled using traditional sampling schemes) and compressively sampled signals. Our results have shown we can obtain accurate estimates of signal features even by using only a third of original samples.
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Understanding the effects of pre-processing on extracted signal features from gait Accelerometry signals
Computers in Biology and Medicine, 2015Co-Authors: Alexandre Millecamps, Kristin A. Lowry, Jennifer S. Brach, Subashan Perera, Mark S. Redfern, Ervin SejdicAbstract:Gait Accelerometry is an important approach for gait assessment. Previous contributions have adopted various pre-processing approaches for gait Accelerometry signals, but none have thoroughly investigated the effects of such pre-processing operations on the obtained results. Therefore, this paper investigated the influence of pre-processing operations on signal features extracted from gait Accelerometry signals. These signals were collected from 35 participants aged over 65years: 14 of them were healthy controls (HC), 10 had Parkinson?s disease (PD) and 11 had peripheral neuropathy (PN). The participants walked on a treadmill at preferred speed. Signal features in time, frequency and time-frequency domains were computed for both raw and pre-processed signals. The pre-processing stage consisted of applying tilt correction and denoising operations to acquired signals. We first examined the effects of these operations separately, followed by the investigation of their joint effects. Several important observations were made based on the obtained results. First, the denoising operation alone had almost no effects in comparison to the trends observed in the raw data. Second, the tilt correction affected the reported results to a certain degree, which could lead to a better discrimination between groups. Third, the combination of the two pre-processing operations yielded similar trends as the tilt correction alone. These results indicated that while gait Accelerometry is a valuable approach for the gait assessment, one has to carefully adopt any pre-processing steps as they alter the observed findings. HighlightsGait Accelerometry is a popular approach for an instrumented walking analysis.Acquired recording typically need to be preprocessed to remove artifacts.The effects of preprocessing need to be understood.Wavelet denoising has no effects.Tilt correction can potentially enhance group discriminations.
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Noninvasive Detection of Thin-Liquid Aspiration Using Dual-Axis Swallowing Accelerometry
Dysphagia, 2012Co-Authors: Catriona M Steele, Ervin Sejdic, Tom ChauAbstract:Aspiration (the entry of foreign contents into the upper airway) is a serious concern for individuals with dysphagia and can lead to pneumonia. However, overt signs of aspiration, such as cough, are not always present, making noninstrumental diagnosis challenging. Valid, reliable tools for detecting aspiration during clinical screening and assessment are needed. In this study we investigated the validity of a noninvasive Accelerometry signal-processing classifier for detecting aspiration. Dual-axis cervical Accelerometry signals were collected from 40 adults on thin-liquid swallowing tasks during videofluoroscopic swallowing examinations. Signal-processing algorithms were used to remove known sources of artifact and a classifier was trained to identify signals associated with penetration-aspiration. Validity was measured in comparison to blinded ratings of penetration-aspiration from the concurrently recorded videofluoroscopies. On a bolus-by-bolus basis, the Accelerometry classifier had a 10 % false-negative rate (90 % sensitivity) and a 23 % false-positive rate (77 % specificity) for detecting penetration-aspiration. We conclude that Accelerometry can be used to support valid, reliable, and efficient detection of aspiration risk in patients with suspected dysphagia.
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Quantitative classification of pediatric swallowing through Accelerometry
Journal of Neuroengineering and Rehabilitation, 2012Co-Authors: Merey Celeste, Ervin Sejdic, Kushki Azadeh, Glenn Berall, Tom ChauAbstract:Background Dysphagia or swallowing disorder negatively impacts a child’s health and development. The gold standard of dysphagia detection is videofluoroscopy which exposes the child to ionizing radiation, and requires specialized clinical expertise and expensive institutionally-based equipment, precluding day-to-day and repeated assessment of fluctuating swallowing function. Swallowing Accelerometry is the non-invasive measurement of cervical vibrations during swallowing and may provide a portable and cost-effective bedside alternative. In particular, dual-axis swallowing Accelerometry has demonstrated screening potential in older persons with neurogenic dysphagia, but the technique has not been evaluated in the pediatric population.
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compressive sampling of swallowing Accelerometry signals using time frequency dictionaries based on modulated discrete prolate spheroidal sequences
EURASIP Journal on Advances in Signal Processing, 2012Co-Authors: Ervin Sejdic, Luis F Chaparro, Catriona M Steele, Tom ChauAbstract:Monitoring physiological functions such as swallowing often generates large volumes of samples to be stored and processed, which can introduce computational constraints especially if remote monitoring is desired. In this article, we propose a compressive sensing (CS) algorithm to alleviate some of these issues while acquiring dual-axis swallowing Accelerometry signals. The proposed CS approach uses a time-frequency dictionary where the members are modulated discrete prolate spheroidal sequences (MDPSS). These waveforms are obtained by modulation and variation of discrete prolate spheroidal sequences (DPSS) in order to reflect the time-varying nature of swallowing acclerometry signals. While the modulated bases permit one to represent the signal behavior accurately, the matching pursuit algorithm is adopted to iteratively decompose the signals into an expansion of the dictionary bases. To test the accuracy of the proposed scheme, we carried out several numerical experiments with synthetic test signals and dual-axis swallowing Accelerometry signals. In both cases, the proposed CS approach based on the MDPSS yields more accurate representations than the CS approach based on DPSS. Specifically, we show that dual-axis swallowing Accelerometry signals can be accurately reconstructed even when the sampling rate is reduced to half of the Nyquist rate. The results clearly indicate that the MDPSS are suitable bases for swallowing Accelerometry signals.
Ervin Sejdić - One of the best experts on this subject based on the ideXlab platform.
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Extraction of Stride Events From Gait Accelerometry During Treadmill Walking
IEEE Journal of Translational Engineering in Health and Medicine, 2016Co-Authors: Ervin Sejdić, Kristin A. Lowry, Subashan Perera, Mark S. Redfern, Jennica Bellanca, Jennifer S. BrachAbstract:Objective: evaluating stride events can be valuable for understanding the changes in walking due to aging and neurological diseases. However, creating the time series necessary for this analysis can be cumbersome. In particular, finding heel contact and toe-off events which define the gait cycles accurately are difficult. Method: we proposed a method to extract stride cycle events from tri-axial Accelerometry signals. We validated our method via data collected from 14 healthy controls, 10 participants with Parkinson's disease, and 11 participants with peripheral neuropathy. All participants walked at self-selected comfortable and reduced speeds on a computer-controlled treadmill. Gait Accelerometry signals were captured via a tri-axial accelerometer positioned over the L3 segment of the lumbar spine. Motion capture data were also collected and served as the comparison method. Results: our analysis of the Accelerometry data showed that the proposed methodology was able to accurately extract heel and toe-contact events from both feet. We used t-tests, analysis of variance (ANOVA) and mixed models to summarize results and make comparisons. Mean gait cycle intervals were the same as those derived from motion capture, and cycle-to-cycle variability measures were within 1.5%. Subject group differences could be similarly identified using measures with the two methods. Conclusions: a simple tri-axial acceleromter accompanied by a signal processing algorithm can be used to capture stride events. Clinical impact: the proposed algorithm enables the assessment of stride events during treadmill walking, and is the first step toward the assessment of stride events using tri-axial accelerometers in real-life settings.
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A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014Co-Authors: Ervin Sejdić, Kristin A. Lowry, Mark S. Redfern, Jennica Bellanca, Jennifer S. BrachAbstract:Gait Accelerometry is a promising tool to assess human walking and reveal deteriorating gait characteristics in patients and can be a rich source of clinically relevant information about functional declines in older adults. Therefore, in this paper, we present a comprehensive set of signal features that may be used to extract clinically valuable information from gait Accelerometry signals. To achieve our goal, we collected tri-axial gait Accelerometry signals from 35 adults 65 years of age and older. Fourteen subjects were healthy controls, 10 participants were diagnosed with Parkinson's disease, and 11 participants were diagnosed with peripheral neuropathy. The data were collected while the participants walked on a treadmill at a preferred walking speed. Accelerometer signal features in time, frequency and time-frequency domains were extracted. The results of our analysis showed that some of the extracted features were able to differentiate between healthy and clinical populations. Signal features in all three domains were able to emphasize variability among different groups, and also revealed valuable information about variability of the signals between anterior-posterior, mediolateral, and vertical directions within subjects. The current results imply that the proposed signal features can be valuable tools for the analysis of gait Accelerometry data and should be utilized in future studies.
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Classification of Penetration--Aspiration Versus Healthy Swallows Using Dual-Axis Swallowing Accelerometry Signals in Dysphagic Subjects
IEEE Transactions on Biomedical Engineering, 2013Co-Authors: Ervin Sejdić, Catriona M Steele, Tom ChauAbstract:Swallowing Accelerometry is a promising noninvasive approach for the detection of swallowing difficulties. In this paper, we propose an approach for classification of swallowing Accelerometry recordings containing either healthy swallows or penetration-aspiration (entry of material into the airway) in dysphagic patients. The proposed algorithm is based on the wavelet packet decomposition of swallowing Accelerometry signals in combination with linear discriminant analysis as a feature reduction method and Bayes classification. The proposed algorithm was tested using swallowing Accelerometry signals collected from 40 patients during the regularly scheduled videoflouroscopy exam. The participants were instructed to swallow several 5-mL sips of thin liquid barium in a head neutral position. The results of our numerical analysis showed that the proposed algorithm can differentiate healthy swallows from aspiration swallows with an accuracy greater than 90%. These results position swallowing Accelerometry as a valid approach for the detection of swallowing difficulties, particularly penetration-aspiration in patients suspected of dysphagia.
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A procedure for denoising dual-axis swallowing Accelerometry signals.
Physiological measurement, 2009Co-Authors: Ervin Sejdić, Catriona M Steele, Tom ChauAbstract:Dual-axis swallowing Accelerometry is an emerging tool for the assessment of dysphagia (swallowing difficulties). These signals however can be very noisy as a result of physiological and motion artifacts. In this note, we propose a novel scheme for denoising those signals, i.e. a computationally efficient search for the optimal denoising threshold within a reduced wavelet subspace. To determine a viable subspace, the algorithm relies on the minimum value of the estimated upper bound for the reconstruction error. A numerical analysis of the proposed scheme using synthetic test signals demonstrated that the proposed scheme is computationally more efficient than minimum noiseless description length (MNDL)-based denoising. It also yields smaller reconstruction errors than MNDL, SURE and Donoho denoising methods. When applied to dual-axis swallowing Accelerometry signals, the proposed scheme exhibits improved performance for dry, wet and wet chin tuck swallows. These results are important for the further development of medical devices based on dual-axis swallowing Accelerometry signals.
