Contactless finger tapping detection at C band

The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice

An Evaluation of KELVIN, an Artificial Intelligence Platform, as an Objective Assessment of the MDS UPDRS Part III

BACKGROUND: Parkinson's disease severity is typically measured using the Movement Disorder Society Unified Parkinson's disease rating scale (MDS-UPDRS). While training for this scale exists, users may vary in how they score a patient with the consequence of intra-rater and inter-rater variability. OBJECTIVE: In this study we explored the consistency of an artificial intelligence platform compared with traditional clinical scoring in the assessment of motor severity in PD. METHODS: Twenty-two PD patients underwent simultaneous MDS-UPDRS scoring by two experienced MDS-UPDRS raters and the two sets of accompanying video footage were also scored by an artificial intelligence video analysis platform known as KELVIN. RESULTS: KELVIN was able to produce a summary score for 7 MDS-UPDRS part 3 items with good inter-rater reliability (Intraclass Correlation Coefficient (ICC) 0.80 in the OFF-medication state, ICC 0.73 in the ON-medication state). Clinician scores had exceptionally high levels of inter-rater reliability in both the OFF (0.99) and ON (0.94) medication conditions (possibly reflecting the highly experienced team). There was an ICC of 0.84 in the OFF-medication state and 0.31 in the ON-medication state between the mean Clinician and mean Kelvin scores for the equivalent 7 motor items, possibly due to dyskinesia impacting on the KELVIN scores. CONCLUSION: We conclude that KELVIN may prove useful in the capture and scoring of multiple items of MDS-UPDRS part 3 with levels of consistency not far short of that achieved by experienced MDS-UPDRS clinical raters, and is worthy of further investigation

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Interactions of motor and non-motor symptoms in Parkinson's disease

Parkinson’s disease (PD) is characterized by motor dysfunction and multiple non-motor symptoms. Though motor/non-motor interactions are common, the lines of research focusing on motor and non-motor symptoms mainly remain separate. The present studies assessed interactions between several motor aspects of PD (impaired gait, side of motor-symptom onset, tremor, motor-symptom severity) and non-motor symptoms (cognition, anxiety, self-perceived stigma) in non-demented individuals with idiopathic PD. Study 1 examined cognitive and motor performance during dual tasking, specifically executive function while walking. The impact of dual tasking on walking (speed, stride frequency) was greater for PD (N=19) than NC participants (N=13). The PD group had fewer set-shifts than NC on dual tasking, and demonstrated greater cognitive variability on dual tasking. Study 2 considered mechanisms of visuospatial dysfunction in PD (N=79) by assessing how side of motor-symptom onset (left versus right) and cognition (attention, executive function) affect spatial judgment on a dynamic line bisection task. In contrast to a rightward-biased parietal-neglect pattern, the PD group showed a leftward bias that occurred when attention was directed to the left side of space, regardless of side of onset. The extent and variability of bias correlated with frontally-mediated neuropsychological performance for PD but not NC (N=67). Both results suggested frontal-attentional rather than parietal-neglect mechanisms of spatial bias. Study 3 assessed how motor symptoms contribute to self-reported anxiety on the Beck Anxiety Inventory (BAI). Factor analysis identified a five-item PD motor factor, which correlated with motor-symptom severity and mediated the difference on BAI total scores between PD (N=100) and NC (N=74). Removal of the motor-factor items (e.g., “hands trembling”) significantly reduced BAI scores for PD relative to NC and reduced the size of the correlation between the BAI and motor-symptom severity. Study 4 examined the contributions of motor and non-motor symptoms to self-perceived stigma in PD (N=362). Contrary to expectations, perceived stigma was not predicted by motor symptoms but rather by depression and, for men only, by younger age. These studies provide insight into interactions that occur between motor and non-motor symptoms in PD in multiple aspects of daily function, highlighting potential avenues for future research and intervention

Neuropsychiatric and cognitive symptoms in Parkinson’s disease: the contribution to subtype classification, to differential diagnosis, their clinical and instrumental correlations

Il piano di ricerca è volto ad approfondire il contributo dei sintomi neuropsichiatrici e cognitivi nelle diverse fasi della Malattia di Parkinson (MP). In particolare, l’argomento di studio è focalizzato sull’analisi dei sintomi cognitivi e neuropsichiatrici nella MP, affrontando queste tematiche anche mediante l’utilizzo di tecniche di neuroimaging, in pazienti drug-naïve, in fase precoce di malattia ed in fase avanzata. Nei pazienti drug-naïve, la ricerca è stata finalizzata alla caratterizzazione dei sintomi neuropsichiatrici e cognitivi nei sottotipi motori (i.e., tremorigeni vs acinetico-rigidi) e rispetto alla lateralità di esordio degli stessi (i.e., lateralità destra vs lateralità sinistra). Nei pazienti in fase precoce di malattia, è stato indagato il contributo dei sintomi neuropsichiatrici e cognitivi nella diagnosi differenziale tra MP e Paralisi Sopranucleare Progressiva (PSP) in pazienti valutati entro i 24 mesi dall’esordio motorio, finestra temporale in cui spesso si assiste ad un overlapping dei sintomi motori. Nei pazienti in fase avanzata di malattia, la ricerca è stata finalizzata alla caratterizzazione, mediante i sintomi neuropsichiatrici e cognitivi, del Gioco D’Azzardo Patologico (gambling) rispetto agli altri tipi di Disturbi del controllo degli Impulsi (ICDs). Ancora nell’ambito dell’ICDs, è stato sviluppato uno studio di neuroimaging, volto ad identificare i correlati morfostrutturali (spessori corticali e volumi dei nuclei sottocorticali) di tali disturbi. Infine, si sono identificati i sintomi neuropsichiatrici e cognitivi che possono impedire l’esecuzione di un esame di Risonanza Magnetica (RM), al fine, in ambito clinico, di preparare adeguatamente all’esame i pazienti più a rischio di mancato svolgimento e con l’intento di indagare, in ambito di ricerca, la reale rappresentatività campionaria dei pazienti inseriti in studi di RM

Machine learning for large-scale wearable sensor data in Parkinson disease:concepts, promises, pitfalls, and futures

For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice

Protocol for PD SENSORS:Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcomes measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate