Fabrication and validation of reference structures for the localization of subdural standard- and micro-electrodes in MRI.

OBJECTIVE: Report simple reference structure fabrication and validate the precise localization of subdural micro- and standard electrodes in magnetic resonance imaging (MRI) in phantom experiments. APPROACH: Electrode contacts with diameters of 0.3 mm and 4 mm are localized in 1.5 T MRI using reference structures made of silicone and iron oxide nanoparticle doping. The precision of the localization procedure was assessed for several standard MRI sequences and implant orientations in phantom experiments and compared to common clinical localization procedures. MAIN RESULTS: A localization precision of 0.41 ± 0.20 mm could be achieved for both electrode diameters compared to 1.46 ± 0.69 mm that was achieved for 4 mm standard electrode contacts localized using a common clinical standard method. The new reference structures are intrinsically bio-compatible, and they can be detected with currently available feature detection software so that a clinical implementation of this technology should be feasible. SIGNIFICANCE: Neuropathologies are increasingly diagnosed and treated with subdural electrodes, where the exact localization of the electrode contacts with respect to the patient's cortical anatomy is a prerequisite for the procedure. Post-implantation electrode localization using MRI may be advantageous compared to the common alternative of CT-MRI image co-registration, as it avoids systematic localization errors associated with the co-registration itself, as well as brain shift and implant movement. Additionally, MRI provides superior soft tissue contrast for the identification of brain lesions without exposing the patient to ionizing radiation. Recent studies show that smaller electrodes and high-density electrode grids are ideal for clinical and research purposes, but the localization of these devices in MRI has not been demonstrated.

Perturbation Treadmill Training Improves Clinical Characteristics of Gait and Balance in Parkinson's Disease.

BACKGROUND: Impaired gait and postural stability are cardinal motor symptoms in Parkinson's disease (PD). Treadmill training improves gait characteristics in PD. OBJECTIVE: This study investigates if postural perturbations during treadmill training improve motor performance and particularly gait and postural stability in PD. METHODS: This work presents secondary outcome measures of a pilot randomized controlled trial. PD patients (n = 43) recruited at the University Hospital Erlangen were randomly allocated to the experimental (perturbation treadmill training, PTT, n = 21) or control group (conventional treadmill training, CTT, n = 22). Outcome measures were collected at baseline, after 8 weeks of intervention, and 3 months follow-up. Motor impairment was assessed by the Unified Parkinson Disease Rating Scale part-III (UPDRS-III), Postural Instability and Gait Difficulty score (PIGD), and subitems 'Gait' and 'Postural stability' by an observer blinded to the randomization. Intervention effects were additionally compared to progression rates of a matched PD cohort (n = 20) receiving best medical treatment (BMT). RESULTS: Treadmill training significantly improved UPDRS-III motor symptoms in both groups with larger effect sizes for PTT (-38%) compared to CTT (-20%). In the PTT group solely, PIGD -34%, and items 'Gait' -50%, and 'Postural stability' -40% improved significantly in comparison to CTT (PIGD -24%, 'Gait' -22%, 'Postural stability' -33%). Positive effects persisted in PTT after 3 months and appeared to be beneficial compared to BMT. CONCLUSIONS: Eight weeks of PTT showed superior improvements of motor symptoms, particularly gait and postural stability. Sustainable effects indicate that PTT may be an additive therapy option for gait and balance deficits in PD.

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.

BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions. METHODS: This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed. RESULTS: As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration. CONCLUSION: It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.

Mobile Stride Length Estimation With Deep Convolutional Neural Networks.

OBJECTIVE: Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. METHODS: We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. RESULTS: Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . CONCLUSION: Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. SIGNIFICANCE: With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.

Acute Neuromuscular Adaptations in the Postural Control of Patients with Parkinson's Disease after Perturbed Walking.

Patients suffering from Parkinson's disease (PD) present motor impairments reflected in the dynamics of the center of pressure (CoP) adjustments during quiet standing. One method to study the dynamics of CoP adjustments is the entropic half-life (EnHL), which measures the short-term correlations of a time series at different time scales. Changes in the EnHL of CoP time series suggest neuromuscular adaptations in the control of posture. In this study, we sought to investigate the immediate changes in the EnHL of CoP adjustments of patients with PD during one session of perturbed (experimental group) and unperturbed treadmill walking (control group). A total of 39 patients with PD participated in this study. The experimental group (n = 19) walked on a treadmill providing small tilting of the treadmill platform. The control group (n = 20) walked without perturbations. Each participant performed 5-min practice followed by three 5-min training blocks of walking with or without perturbation (with 3-min resting in between). Quiet standing CoP data was collected for 30 s at pre-training, after each training block, immediately post-training, and after 10 min retention. The EnHL was computed on the original and surrogates (phase-randomized) CoP signals in the medio-lateral (ML) and anterior-posterior (AP) directions. Data was analyzed using four-way mixed ANOVA. Increased EnHL values were observed for both groups (Time effect, p < 0.001) as the intervention progressed, suggesting neuromuscular adaptations in the control of posture. The EnHL of surrogate signals were significantly lower than for original signals (p < 0.001), confirming that these adaptations come from non-random control processes. There was no Group effect (p = 0.622), however by analyzing the significant Group by Direction by Time interaction (p < 0.05), a more pronounced effect in the ML direction of the perturbed group was observed. Altogether, our findings show that treadmill walking decreases the complexity of CoP adjustments, suggesting neuromuscular adaptations in balance control during a short training period. Further investigations are required to assess these adaptations during longer training intervals.

Sensor-Based Gait Parameter Extraction With Deep Convolutional Neural Networks.

Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double-integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of eight spatio-temporal stride parameters. To this end, two modeling approaches are compared: a combined network estimating all parameters of interest and an ensemble approach that spawns less complex networks for each parameter individually. The ensemble approach is outperforming the combined modeling in the current application. On a clinically relevant and publicly available benchmark dataset, we estimate stride length, width and medio-lateral change in foot angle up to -0.15 ± 6.09 cm, -0.09 ± 4.22 cm and 0.13 ± 3.78° respectively. Stride, swing and stance time as well as heel and toe contact times are estimated up to ±0.07, ±0.05, ±0.07, ±0.07 and ±0.12 s respectively. This is comparable to and in parts outperforming or defining state of the art. Our results further indicate that the proposed change in the methodology could substitute assumption-driven double-integration methods and enable mobile assessment of spatio-temporal stride parameters in clinically critical situations as, e.g., in the case of spastic gait impairments.

Combined accelerometer and EMG analysis to differentiate essential tremor from Parkinson's disease.

In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space. Classification was performed using support vector machines. We evaluated the proposed method using leave one out cross validation and we report overall accuracy of the classification. With this method, it was possible to discriminate 12/13 PD patients from 8/11 patients with ET with an overall accuracy of 83%. In order to individualize this finding for clinical application we generated a posterior probability for the test result of each patient and compared the misclassified patients, or low probability scores to available clinical follow up information for individual cases. This non-standardized post hoc analysis revealed that not only the technical accuracy but also the clinical accuracy limited the overall classification rate. We show that, in addition to the successful isolation of diagnostic features, longitudinal and larger sized validation is needed in order to prove clinical applicability.

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things.

Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinson's disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the Internet of Things (IoT). Real-time motion metrics in PD could be obtained virtually in any scenario by placing lightweight wearable sensors in the patient's clothes and connecting them to a medical database through mobile devices such as cell phones or tablets. Technologies exist to collect huge amounts of patient data not only during regular medical visits but also at home during activities of daily life. These data could be fed into intelligent algorithms to first discriminate relevant threatening conditions, adjust medications based on online obtained physical deficits, and facilitate strategies to modify disease progression. A major impact of this approach lies in its efficiency, by maximizing resources and drastically improving the patient experience. The patient participates actively in disease management via combined objective device- and self-assessment and by sharing information within both medical and peer groups. Here, we review and discuss the existing wearable technologies and the Internet-of-Things concept applied to PD, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.

Pull Test estimation in Parkinson's disease patients using wearable sensor technology.

Postural instability is one of the main motor impairment of Parkinson's disease (PD). The Pull Test is the most common clinical examination to assess postural instability in PD. However, the subjectivity and low discriminative power of this test presents as a major drawback. In this paper we propose a novel methodology to estimate the Pull Test scores from patients with PD. We capture the relationship between the Pull Test outcomes and patients' foot motion patterns, using wearable sensors mounted on their shoes. 139 idiopathic Parkinson's disease patients performed four motor function tests, including walking and repetitive foot motions, while acceleration and orientation data was recorded. A total of 684 features were extracted from the acceleration and orientation signals. Feature selection and classification algorithms were utilized to estimate the Pull Test score for each participant. Further, we estimate which motor function test would better predict the Pull Test score, depending on the patient's phenotype (i.e. bradykinetic, tremor-dominant or equivalent). When combining all phenotypes and all tests, the mean of the classification probability distribution achieved was 0.75 (CI: [0.69-0.82]). Foot circling was the best predictive test for the equivalent patients (mean = 0.79, CI: [0.69-0.87]) and the bradykinetic patients (mean: 0.75, CI: [0.64-0.85]), while 2×10 m. walk with stop-and-go proved superior for the tremor-dominant patients (mean: 0.75, CI: [0.64-0.85]). Overall, these results suggest that inertial data from patient's foot motion can be used to estimate postural instability in PD patients.

Modulation of grasping force in prosthetic hands using neural network-based predictive control.

This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.

Influence of nerve supply on hand electromyography coherence during a three-digit task.

Intermuscular coupling has been investigated to understand neural inputs to coordinate muscles in a motor performance. However, little is known on the role of nerve innervation on intermuscular coupling. The purpose of this study was to investigate how the anatomy of nerve distribution affected intermuscular coupling in the hand during static grip. Electromyographic (EMG) signals were recorded from intrinsic and extrinsic muscles while subjects performed a static grip. Coherence was computed for muscle pairs innervated by either the same or different nerves. The results did not support the hypothesis that muscles sharing the same nerve exhibit greater coupling than muscles innervated by different nerves. In general, extrinsic muscle pairs displayed higher coherence than intrinsic pairs. The results suggest that intermuscular coupling in a voluntary motor task is likely modulated in a functional manner and that different nerves might transport common neural inputs to functionally coupled muscles.

Evaluation of a neural network-based control strategy for a cost-effective externally-powered prosthesis.

This paper presents a control strategy that compensates for the nonlinearity in the inexpensive sensors and hardware of a cost effective prosthetic hand. The control strategy uses neural network-based force control and sensory feedback to detect disturbance induced by slippage. The neural network approach is chosen over other nonlinear models because it is easy to implement and it offered the additional advantage of having its parameters easily adjusted over the life span of the device. The proposed strategy was evaluated on a functional multi-digit underactuated prosthetic hand. The initial and incremental forces exerted from each finger were adjusted to balance the amount of disturbance and the deformation of the objects. Experiments were conducted to test the performance of the protocol in situations encountered in activities of daily living. The displacement of each object under three grasping configurations was measured as a performance criterion while the object's mass was changed. The results showed that with the adjusted parameters for each grasping configuration, the control strategy was able to detect the dynamic changes in mass of the object and was also able to successfully adjust the grasping force before the object drops from the hand.

Nearest hyperplane distance neighbor clustering algorithm applied to gene co-expression analysis in Alzheimer's disease.

Microarray analysis can contribute considerably to the understanding of biologically significant cellular mechanisms that yield novel information regarding co-regulated sets of gene patterns. Clustering is one of the most popular tools for analyzing DNA microarray data. In this paper, we present an unsupervised clustering algorithm based on the K-local hyperplane distance nearest-neighbor classifier (HKNN). We adapted the well-known nearest neighbor clustering algorithm for use with hyperplane distance. The result is a simple and computationally inexpensive unsupervised clustering algorithm that can be applied to high-dimensional data. It has been reported that the NFkB1 gene is progressively over-expressed in moderate-to-severe Alzheimer's disease (AD) cases, and that the NF-kB complex plays a key role in neuroinflammatory responses in AD pathogenesis. In this study, we apply the proposed clustering algorithm to identify co-expression patterns with the NFkB1 in gene expression data from hippocampal tissue samples. Finally, we validate our experiments with biomedical literature search.