612 research outputs found
Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning
Full text linkThis study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations.We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests.The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment
The Δρομοδείχτης της Ελλάδος of 1824 and Athanasios Stageirites (Τίτλος περίληψης)
No full textσ. [281]-290Κείμενο στα ελληνικά με περίληψη στα αγγλικά με τον τίτλο: The Δρομοδείχτης της Ελλάδος of 1824 and Athanasios StageiritesThe article first examines the close relationship between the publication “Δρομοδείχτης της Ελλάδος” [1824] and the publication “Ηπειρωτικά” (1819) by Athanasios Stageirites and then suggests that Athanasios Stageirites is the likeliest author of the “Δρομοδείχτης της Ελλάδος”.Δωδώνη: Τεύχος Πρώτο: επιστημονική επετηρίδα του Τμήματος Ιστορίας και Αρχαιολογίας της Φιλοσοφικής Σχολής του Πανεπιστημίου Ιωαννίνων; Τόμ. 43-44 (2014-2015
Accurate telemonitoring of Parkinson's disease symptom severity using nonlinear speech signal processing and statistical machine learning
Full text linkThis study focuses on the development of an objective, automated method to extract clinically useful information from sustained vowel phonations in the context of Parkinson’s disease (PD). The aim is twofold: (a) differentiate PD subjects from healthy controls, and (b) replicate the Unified Parkinson’s Disease Rating Scale (UPDRS) metric which provides a clinical impression of PD symptom severity. This metric spans the range 0 to 176, where 0 denotes a healthy person and 176 total disability. Currently, UPDRS assessment requires the physical presence of the subject in the clinic, is subjective relying on the clinical rater’s expertise, and logistically costly for national health systems. Hence, the practical frequency of symptom tracking is typically confined to once every several months, hindering recruitment for large-scale clinical trials and under-representing the true time scale of PD fluctuations. We develop a comprehensive framework to analyze speech signals by: (1) extracting novel, distinctive signal features, (2) using robust feature selection techniques to obtain a parsimonious subset of those features, and (3a) differentiating PD subjects from healthy controls, or (3b) determining UPDRS using powerful statistical machine learning tools. Towards this aim, we also investigate 10 existing fundamental frequency (F_0) estimation algorithms to determine the most useful algorithm for this application, and propose a novel ensemble F_0 estimation algorithm which leads to a 10% improvement in accuracy over the best individual approach. Moreover, we propose novel feature selection schemes which are shown to be very competitive against widely-used schemes which are more complex. We demonstrate that we can successfully differentiate PD subjects from healthy controls with 98.5% overall accuracy, and also provide rapid, objective, and remote replication of UPDRS assessment with clinically useful accuracy (approximately 2 UPDRS points from the clinicians’ estimates), using only simple, self-administered, and non-invasive speech tests. The findings of this study strongly support the use of speech signal analysis as an objective basis for practical clinical decision support tools in the context of PD assessment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
“I Have to Do Something About It” - An Exploration of How Dashboards Invoke Self-Reflections in Chronic Obstructive Pulmonary Disease Patients
Full text linkChronic Obstructive Pulmonary Disease (COPD) patients need to track their symptoms for health professionals to adapt treatments in a timely manner in case of health deterioration. Clinicians typically analyzed the tracked data and recommended actions to patients who acted as mere data collectors. Consequently, patients have little agency and motivation to self-track. Two studies investigated how digital dashboards influenced patients’ motivation, agency, and reflections. Study 1 (one week) focused on how five patients used a paper diary to self-track and reflect on their symptoms. Additionally, the patients evaluated a tablet-based digital dashboard using four data visualisations. Study 2 looked at how five patients tracked and reflected on their data using a tablet-based dashboard for two weeks. By using reflective questions to prompt patients to compare and reflect on time series charts with data annotations, patients gained new knowledge about what factors might influence their symptoms and identified actions to improve their health (e.g. increase oxygen supplements). This strengthened their sense of agency and motivated them to participate more in the management of their condition.</p
Accurately Inferring Physical Activity Levels and Sleep From Wrist-Worn Actigraphy Recordings With Sample Rates as Low as 10 Hz
Full text linkInferring longitudinal Physical Activity (PA) levels and sleep timings from wrist-worn sensors may facilitate personalized insights into day-to-day profile assessments and can be used to monitor a range of physical- and mental-health outcomes, including towards symptom monitoring and rehabilitation. We used the publicly available CAPTURE-24 dataset, comprising 148 participants with ~24-hour concurrent three-dimensional wrist-worn accelerometer data and minute-by-minute labels used as ground truth: sleep, sedentary, light, moderate-vigorous PA. First, we down-sampled the raw accelerometry data to 10 Hz to ensure the generalizability of our methodology across longitudinal studies which typically use similarly low sample rates for actigraphy. Subsequently, we computed four complementary acceleration summary measures and 10 additional smoothened outputs for each acceleration summary measure to derive 44 features characterizing minute-by-minute PA. These features were presented into different classifiers casted as a 4-class classification problem. We trained the model using the first 98 participants and assessed model performance and generalization on the remaining 50 participants. Using a random forest classifier, we demonstrated accurately estimating PA levels and sleep with 87% overall accuracy (F1-score=0.80) including 98.6% correct sleep detection. These findings processing the down-sampled actigraphy data to 10 Hz match or exceed state-of-art results recently reported in the literature achieved using considerably more sophisticated and time-consuming methods (including deep learning) which required actigraphy data sampled at 100 Hz. Collectively, these findings support the deployment of longitudinal, large-scale actigraphy data with sample rates as low as 10 Hz, towards accurately estimating personalized day-to-day PA and sleep profiles in healthcare community studies
Patient Data Work with Consumer Self-tracking:Exploring Affective and Temporal Dimensions in Chronic Self-care
Full text linkEmerging studies are reporting on the implications of self-tracked data in patients’ everyday life and how it influences self-care activities in chronic care. The increased uptake of consumer wearable activity trackers in healthcare contexts and the wider application of advanced analytics is changing the temporal scope from ‘past-centric’ to ‘future-centric’ personal informatics. At the same time, a stream of research is making clear that experiences of emotion are constitutive of patient data work suggesting that the micro practices of engaging with personal data has an important affective dimension. We conducted an exploratory interview study with five chronic heart patients with an implanted cardiac device to conceptualize the data work, which is involved in making sense of self-tracked data from a consumer wearable activity tracker (Fitbit Alta HR). In this paper, we contribute to understanding patient data work as seven forms of micro practices: Verifying, Questioning, Motivating, Reacting, Accepting, Distancing, and Sharing. We discuss how these practices relate to temporal and affective dimensions of engaging with self-tracked data in chronic care and point to future research.</p
Heart Rate During Sleep Measured Using Finger-, Wrist-and Chest-Worn Devices: A Comparison Study
No full textISSN:1867-8211ISSN:1867-822XISSN:1867-822
Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment
Full text linkWrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate sample rate, (b) summarizing raw three-dimensional accelerometry data for further processing (accelerometry summary measures), and (c) accurately estimating PA levels and sleep towards understanding participants’ 24-hour profiles. We used the CAPTURE-24 dataset, where 148 participants concurrently wore a wrist-worn three-dimensional accelerometer and a wearable camera over approximately 24 h to obtain minute-by-minute labels: sleep; and sedentary light, moderate, and vigorous PA. We propose a new acceleration summary measure, the Rate of Change Acceleration Movement (ROCAM), and compare its performance against three established approaches summarizing three-dimensional acceleration data towards replicating the minute-by-minute labels. Moreover, we compare findings where the acceleration data was sampled at 10, 25, 50, and 100 Hz. We demonstrate the competitive advantage of ROCAM towards estimating the five labels (80.2% accuracy) and building 24-hour profiles where the sample rate of 10 Hz is fully sufficient. Collectively, these findings provide insights facilitating the deployment of large-scale longitudinal actigraphy data processing towards 24-hour PA and sleep-profile assessment
New insights into Parkinson’s disease through statistical analysis of standard clinical scales quantifying symptom severity
Full text linkClinical research studies in Parkinson’s Disease (PD) focusing on symptom assessment often rely on thorough time-consuming physical examinations quantified on clinical scales such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Although widely used in clinical research, realistic time constraints preclude its use in daily clinical practice. The Hoehn and Yahr (H&Y) staging is an alternative scale which is easier to administer and provides a succinct descriptor of overall PD severity. There is no universal agreement amongst neurologists on the specific PD symptoms they need to be assessing in order to prescribe treatments and optimize symptom management for their patients, and practically there are no clinical scales which are recorded in daily clinical practice. In this study, we systematically evaluate diverse symptoms (as expressed in 44 UPDRS items) and aim to provide a statistical association with UPDRS and H&Y using rank correlation and mutual information metrics. Moreover, we investigate the projection of a UPDRS item subset on a 2D plot to map onto H&Y. We report some statistically strong correlations of PD symptoms against UPDRS and H&Y (|푹|≥ퟎ.ퟑ), and provide an intuitively appealing visualization mapping onto H&Y. These findings may be useful to neurologists as practical guidance in their daily clinical routine
A quantitative comparison of manual vs. automated facial coding using real life observations of fathers
No full textThis work explores the application of an automated facial recognition software “FaceReader” [1] to videos of fathers (n = 36), taken using headcams worn by their infants during interactions in the home. We evaluate the use of FaceReader as an alternative method to manual coding – which is both time and labour intensive – and advance understanding of the usability of this software in naturalistic interactions. Using video data taken from the Avon Longitudinal Study of Parents and Children (ALSPAC), we first manually coded fathers’ facial expressions according to an existing coding scheme, and then processed the videos using FaceReader. We used contingency tables and multivariate logistic regression models to compare the manual and automated outputs. Our results indicated low levels of facial recognition by FaceReader in naturalistic interactions (approximately 25.17% compared to manual coding), and we discuss potential causes for this (e.g., problems with lighting, the headcams themselves, and speed of infant movement). However, our logistic regression models showed that when the face was found, FaceReader predicted manually coded expressions with a mean accuracy of M = 0.84 (range = 0.67–0.94), sensitivity of M = 0.64 (range = 0.27–0.97), and specificity of M = 0.81 (range = 0.51–0.97).</p
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