Clinical validation of a precision electromagnetic tremor measurement system in participants receiving deep brain stimulation for essential tremor

Using Remotely Supervised At-Home TES for Enhancing Mental Resilience

We are in the midst of a mental health crisis with major depressive disorder being the most prevalent among mental health disorders and up to 30% of patients not responding to first-line treatments. Noninvasive Brain Stimulation (NIBS) techniques have proven to be effective in treating depression. However, there is a fundamental problem of scale. Currently, any type of NIBS treatment requires patients to repeatedly visit a clinic to receive brain stimulation by trained personnel. This is an often-insurmountable barrier to both patients and healthcare providers in terms of time and cost. In this perspective, we assess to what extent Transcranial Electrical Stimulation (TES) might be administered with remote supervision in order to address this scaling problem and enable neuroenhancement of mental resilience at home. Social, ethical, and technical challenges relating to hardware- and software-based solutions are discussed alongside the risks of stimulation under- or over-use. Solutions to provide users with a safe and transparent ongoing assessment of aptitude, tolerability, compliance, and/or misuse are proposed, including standardized training, eligibility screening, as well as compliance and side effects monitoring. Looking into the future, such neuroenhancement could be linked to prevention systems which combine home-use TES with digital sensor and mental monitoring technology to index decline in mental wellbeing and avoid relapse. Despite the described social, ethical legal, and technical challenges, the combination of remotely supervised, at-home TES setups with dedicated artificial intelligence systems could be a powerful weapon to combat the mental health crisis by bringing personalized medicine into people’s homes.

Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson's disease

BACKGROUND

Resting tremor is an essential characteristic in patients suffering from Parkinson's disease (PD).

OBJECTIVE

Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring.

METHODS

Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson's Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis.

RESULTS

The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities.

CONCLUSION

The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.

A novel cost-effective postural tracking algorithm using marker-based video processing

Recently, many postural analysis techniques have been developed in order to reduce the risk of musculoskeletal problems. Methods such as rapid entire body assessment are capable of analyzing the most constant or awkward positions, but the selection of these postures is subjective. To make an objective postural analysis, devices such as electromagnetic trackers can be used continuously during the job task, but utilizing such devices is costly. Therefore, in this study a cost-effective marker-based video processing algorithm is developed for measuring three-dimensional (3D) information regarding both the location and the orientation of human posture. To investigate the precision of the measurements, an experiment was designed. With the average of 2.88 mm and 1.34° for location and orientation, respectively, the algorithm was able to measure six degrees of freedom information regarding 3D space. Furthermore, the precision of the algorithm is found to be significantly affected by the marker pattern.

Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks

Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.

Biomechanical System Versus Observational Rating Scale for Parkinson's Disease Tremor Assessment

There is a lack of evidence that either conventional observational rating scale or biomechanical system is a better tremor assessment tool. This work focuses on comparing a biomechanical system and the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale in terms of test-retest reliability. The Parkinson's disease tremors were quantified by biomechanical system in joint angular displacement and predicted rating, as well as assessed by three raters using observational ratings. Qualitative comparisons of the validity and function are made also. The observational rating captures the overall severity of body parts, whereas the biomechanical system provides motion- and joint-specific tremor severity. The tremor readings of the biomechanical system were previously validated against encoders' readings and doctors' ratings; the observational ratings were validated with previous ratings on assessing the disease and combined motor symptoms rather than on tremor specifically. Analyses show that the predicted rating is significantly more reliable than the average clinical ratings by three raters. The comparison work removes some of the inconsistent impressions of the tools and serves as guideline for selecting a tool that can improve tremor assessment. Nevertheless, further work is required to consider more variabilities that influence the overall judgement.

Objective evaluation of bradykinesia in Parkinson's disease using an inexpensive marker-less motion tracking system

OBJECTIVE

Quantification of bradykinesia (slowness of movement) is crucial for the treatment and monitoring of Parkinson's disease. Subjective observational techniques are the de-facto 'gold standard', but such clinical rating scales suffer from poor sensitivity and inter-rater variability. Although various technologies have been developed for assessing bradykinesia in recent years, most still require considerable expertise and effort to operate. Here we present a novel method to utilize an inexpensive off-the-shelf hand-tracker (Leap Motion) to quantify bradykinesia.

APPROACH

Eight participants with Parkinson's disease receiving benefit from deep brain stimulation were recruited for the study. Participants were assessed 'on' and 'off' stimulation, with the 'on' condition repeated to evaluate reliability. Participants performed wrist pronation/supination, hand open/close, and finger-tapping tasks during each condition. Tasks were simultaneously captured by our software and rated by three clinicians. A linear regression model was developed to predict clinical scores and its performance was assessed with leave-one-subject-out cross validation.

MAIN RESULTS

Aggregate bradykinesia scores predicted by our method were in strong agreement (R  =  0.86) with clinical scores. The model was able to differentiate therapeutic states and comparison between the test-retest conditions yielded no significant difference (p   =  0.50).

SIGNIFICANCE

These findings demonstrate that our method can objectively quantify bradykinesia in agreement with clinical observation and provide reliable measurements over time. The hardware is readily accessible, requiring only a modest computer and our software to perform assessments, thus making it suitable for both clinic- and home-based symptom tracking.

Validation of a precision tremor measurement system for multiple sclerosis

BACKGROUND

Tremor is a debilitating symptom of Multiple Sclerosis (MS). Little is known about its pathophysiology and treatments are limited. Clinical trials investigating new interventions often rely on subjective clinical rating scales to provide supporting evidence of efficacy.

NEW METHOD

We present a novel instrument (TREMBAL) which uses electromagnetic motion capture technology to quantify MS tremor. We aim to validate TREMBAL by comparison to clinical ratings using regression modelling with 310 samples of tremor captured from 13 MS participants who performed five different hand exercises during several follow-up visits. Minimum detectable change (MDC) and test-retest reliability were calculated and comparisons were made between MS tremor and data from 12 healthy volunteers.

RESULTS

Velocity of the index finger was most congruent with clinical observation. Regression modelling combining different features, sensor configurations, and labelling exercises did not improve results. TREMBAL MDC was 84% of its initial measurement compared to 91% for the clinical rating. Intra-class correlations for test-retest reliability were 0.781 for TREMBAL and 0.703 for clinical ratings. Tremor was lower (p =  0.002) in healthy subjects.

COMPARISON WITH EXISTING METHODS

Subjective scales have low sensitivity, suffer from ceiling effects, and mitigation against inter-rater variability is challenging. Inertial sensors are ubiquitous, however, their output is nonlinearly related to tremor frequency, compensation is required for gravitational artefacts, and their raw data cannot be intuitively comprehended.

CONCLUSIONS

TREMBAL, compared with clinical ratings, gave measures in agreement with clinical observation, had marginally lower MDC, and similar test-retest reliability.

Neurophysiological analysis of the clinical pull test

Postural reflexes are impaired in conditions such as Parkinson's disease, leading to difficulty walking and falls. In clinical practice, postural responses are assessed using the "pull test," where an examiner tugs the prewarned standing patient backward at the shoulders and grades the response. However, validity of the pull test is debated, with issues including scaling and variability in administration and interpretation. It is unclear whether to assess the first trial or only subsequent repeated trials. The ecological relevance of a forewarned backward challenge is also debated. We therefore developed an instrumented version of the pull test to characterize responses and clarify how the test should be performed and interpreted. In 33 healthy participants, "pulls" were manually administered and pull force measured. Trunk and step responses were assessed with motion tracking. We probed for the StartReact phenomenon (where preprepared responses are released early by a startling stimulus) by delivering concurrent normal or "startling" auditory stimuli. We found that the first pull triggers a different response, including a larger step size suggesting more destabilization. This is consistent with "first trial effects," reported by platform translation studies, where movement execution appears confounded by startle reflex-like activity. Thus, first pull test trials have clinical relevance and should not be discarded as practice. Supportive of ecological relevance, responses to repeated pulls exhibited StartReact, as previously reported with a variety of other postural challenges, including those delivered with unexpected timing and direction. Examiner pull force significantly affected the postural response, particularly the size of stepping. NEW & NOTEWORTHY We characterized postural responses elicited by the clinical "pull test" using instrumentation. The first pull triggers a different response, including a larger step size suggesting more destabilization. Thus, first trials likely have important clinical and ecological relevance and should not be discarded as practice. Responses to repeated pulls can be accelerated with a startling stimulus, as reported with a variety of other challenges. Examiner pull force was a significant factor influencing the postural response.