An EMG-based system for continuous monitoring of clinical efficacy of Parkinson's disease treatments

A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes

Introduction

In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD.

Methods

This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days.

Results

The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants.

Conclusion

These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.

Wrist Rigidity Evaluation in Parkinson’s Disease: A Scoping Review

(1) Background: One of the main cardinal signs of Parkinson’s disease (PD) is rigidity, whose assessment is important for monitoring the patient’s recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability. (2) Objectives: To compile the main methods used to assess wrist rigidity in PD and to study their validity and reliability, a scope review was conducted. (3) Methods: PubMed, IEEE/IET Electronic Library, Web of Science, Scopus, Cochrane, Bireme, Google Scholar and Science Direct databases were used. (4) Results: Twenty-eight studies were included. The studies presented several methods for quantitative assessment of rigidity using instruments such as force and inertial sensors. (5) Conclusions: Such methods present good correlation with clinical scales and are useful for detecting and monitoring rigidity. However, the development of a standard quantitative method for assessing rigidity in clinical practice remains a challenge.

Biodegradable Polymer Composites for Electrophysiological Signal Sensing

Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited.

An update on adaptive deep brain stimulation in Parkinson's disease

Advancing conventional open‐loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed‐loop or adaptive DBS aims to overcome these limitations by real‐time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Intraoperative acceleration measurements to quantify improvement in tremor during deep brain stimulation surgery

Deep brain stimulation (DBS) surgery is extensively used in the treatment of movement disorders. Nevertheless, methods to evaluate the clinical response during intraoperative stimulation tests to identify the optimal position for the implantation of the chronic DBS lead remain subjective. In this paper, we describe a new, versatile method for quantitative intraoperative evaluation of improvement in tremor with an acceleration sensor that is mounted on the patient's wrist during surgery. At each anatomical test position, the improvement in tremor compared to the initial tremor is estimated on the basis of extracted outcome measures. This method was tested on 15 tremor patients undergoing DBS surgery in two centers. Data from 359 stimulation tests were acquired. Our results suggest that accelerometric evaluation detects tremor changes more sensitively than subjective visual ratings. The effective stimulation current amplitudes identified from the quantitative data (1.1 ± 0.8 mA) are lower than those identified by visual evaluation (1.7 ± 0.8 mA) for similar improvement in tremor. Additionally, if these data had been used to choose the chronic implant position of the DBS lead, 15 of the 26 choices would have been different. These results show that our method of accelerometric evaluation can potentially improve DBS targeting.

Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors

Medication non-adherence is a major concern in the healthcare industry and has led to increases in health risks and medical costs. For many neurological diseases, adherence to medication regimens can be assessed by observing movement patterns. However, physician observations are typically assessed based on visual inspection of movement and are limited to clinical testing procedures. Consequently, medication adherence is difficult to measure when patients are away from the clinical setting. The authors propose a data mining driven methodology that uses low cost, non-wearable multimodal sensors to model and predict patients' adherence to medication protocols, based on variations in their gait. The authors conduct a study involving Parkinson's disease patients that are "on" and "off" their medication in order to determine the statistical validity of the methodology. The data acquired can then be used to quantify patients' adherence while away from the clinic. Accordingly, this data-driven system may allow for early warnings regarding patient safety. Using whole-body movement data readings from the patients, the authors were able to discriminate between PD patients on and off medication, with accuracies greater than 97% for some patients using an individually customized model and accuracies of 78% for a generalized model containing multiple patient gait data. The proposed methodology and study demonstrate the potential and effectiveness of using low cost, non-wearable hardware and data mining models to monitor medication adherence outside of the traditional healthcare facility. These innovations may allow for cost effective, remote monitoring of treatment of neurological diseases.

Automatic recognition of Parkinson's disease using surface electromyography during standardized gait tests

Diagnosis and severity staging of Parkinsons disease (PD) relies mainly on subjective clinical examination. To better monitor disease progression and therapy success in PD patients, new objective and rater independent parameters are required. Surface electromyography (EMG) during dynamic movements is one possible modality. However, EMG signals are often difficult to understand and interpret clinically. In this study pattern recognition was applied to find suitable parameters to differentiate PD patients from healthy controls. EMG signals were recorded from 5 patients with PD and 5 younger healthy controls, while performing a series of standardized gait tests. Wireless surface electrodes were placed bilaterally on tibialis anterior and gastrocnemius medialis and lateralis. Accelerometers were positioned on both heels and used for step segmentation. Statistical and frequency features were extracted and used to train a Support Vector Machine classifier. Sensitivity and specificity were high at 0.90 using leave-one-subject-out cross-validation. Feature selection revealed kurtosis and mean frequency as best features, with a significant difference in kurtosis (p=0.013). Evaluated on a bigger population, this could lead to objective diagnostic and staging tools for PD.

Using a smart phone as a standalone platform for detection and monitoring of pathological tremors

Introduction: Smart phones are becoming ubiquitous and their computing capabilities are ever increasing. Consequently, more attention is geared toward their potential use in research and medical settings. For instance, their built-in hardware can provide quantitative data for different movements. Therefore, the goal of the current study was to evaluate the capabilities of a standalone smart phone platform to characterize tremor. Results: Algorithms for tremor recording and online analysis can be implemented within a smart phone. The smart phone provides reliable time- and frequency-domain tremor characteristics. The smart phone can also provide medically relevant tremor assessments. Discussion: Smart phones have the potential to provide researchers and clinicians with quantitative short- and long-term tremor assessments that are currently not easily available. Methods: A smart phone application for tremor quantification and online analysis was developed. Then, smart phone results were compared to those obtained simultaneously with a laboratory accelerometer. Finally, results from the smart phone were compared to clinical tremor assessments.