Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study.

STUDY OBJECTIVES: A growing body of literature suggests that deep brain stimulation (DBS) to treat motor symptoms of Parkinson's disease (PD) may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of DBS on sleep macro-architecture during the first nights of stimulation. METHODS: Polysomnograms were recorded for three consecutive nights in 14 patients with advanced PD (10 male, 4 female; age: 53-74 years), with intermittent, unilateral subthalamic nucleus DBS on the final night or two. Sleep scoring was determined manually by a consensus of four experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset (WASO) and on each stage of sleep (REM and NREM stages N1, N2, N3). RESULTS: Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with PD. No statistically significant difference was observed due to stimulation. CONCLUSIONS: These objective measures suggest that one night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture. CLINICAL TRIAL REGISTRATION: Name: Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551; Identifier: NCT04620551.

Evaluation of consensus sleep stage scoring of dysregulated sleep in Parkinson's disease.

OBJECTIVE: Sleep dysregulation in Parkinson's disease (PD) has been hypothesized to occur, in part, from dysfunction in the basal ganglia-cortical circuit. Assessment of this relationship requires accurate sleep stage determination, a known challenge in this clinical population. Our objective was to optimize the consensus on the sleep staging process and reduce interrater variability in a cohort of advanced PD subjects. METHODS: Fifteen PD subjects were enrolled from three sites in a clinical trial that involved recordings from subthalamic nucleus (STN) deep brain stimulation (DBS) leads (NCT04620551). Video polysomnography (vPSG) data for a total of 45 nights were analyzed. Four experienced scorers independently scored data on initial review. Epochs with less than 75% consensus were flagged for secondary review. In secondary review of discordant epochs, two of the original scorers re-assessed epochs, from which the final consensus stage was derived. RESULTS: Sleep stage classification agreement averaged 83.10% across all sleep stages on initial scoring (IS), and on secondary consensus scoring (CS) review, agreement reached 96.58%. Greatest disagreement was noted in determination of awake epochs (33.6% of discordant epochs) and non-rapid-eye-movement stage 2 (N2) epochs (31.8% of discordant epochs). Scoring discrepancy was resolved with direct measurement of cortical frequency and amplitudes, physiologic context of the epoch, and video review. CONCLUSION: Our method of multi-level initial and then secondary consensus review scoring resulted in consensus scoring agreement superior to conventional standards. This work features a custom-engineered vPSG software and review platform for integration of consensus sleep stage scoring in a multi-site clinical trial.

Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study.

Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This paper proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation (0.42-0.75) between sEMG and IMU signals, thus proving the feasibility of such a system. Results also show that the developed system is robust as well as less susceptible to external interferences. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently.

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review.

Objective.Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities.Approach.For stroke applications, FT mainly includes the 'flexible/stretchable electronics', 'e-textile (electronic textile)' and 'soft robotics'. Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application.Main results.In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the 'biosignal acquisition unit', 'rehabilitation devices' and 'assistive systems'. In terms of biosignals acquisition, electroencephalography and electromyography are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation and robotics units (exoskeleton, orthosis, etc) have been explained.Significance.This is the first review article that compiles the different studies regarding FT based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.

Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: From designing to application.

Strokes are a growing cause of mortality and many stroke survivors suffer from motor impairment as well as other types of disabilities in their daily life activities. To treat these sequelae, motor imagery (MI) based brain-computer interface (BCI) systems have shown potential to serve as an effective neurorehabilitation tool for post-stroke rehabilitation therapy. In this review, different MI-BCI based strategies, including "Functional Electric Stimulation, Robotics Assistance and Hybrid Virtual Reality based Models," have been comprehensively reported for upper-limb neurorehabilitation. Each of these approaches have been presented to illustrate the in-depth advantages and challenges of the respective BCI systems. Additionally, the current state-of-the-art and main concerns regarding BCI based post-stroke neurorehabilitation devices have also been discussed. Finally, recommendations for future developments have been proposed while discussing the BCI neurorehabilitation systems.