Neuropathic Tremor in Guillain-Barré Syndrome

Background

Neuropathic Tremor (NT) is a postural/kinetic tremor of the upper extremity, often encountered in patients with chronic neuropathies such as paraprotein-associated and hereditary neuropathies.

Objectives

To describe the clinical and electrophysiological features of NT in a previously underrecognized setting- during recovery from Guillain-Barré Syndrome (GBS).

Methods

Patients with a documented diagnosis of GBS in the past, presenting with tremor were identified from review of clinical records. Participants underwent structured, videotaped neurological examination, and electrophysiological analysis using tri-axial accelerometry-surface electromyography. Tremor severity was assessed using the Fahn-Tolosa-Marin Tremor Rating Scale.

Results

We describe the clinical and electrophysiological features of 5 patients with GBS associated NT. Our cohort had a fine, fast, and slightly jerky postural tremor of frequency ranging from 8 to 10 Hz. Dystonic posturing and overflow movements were noted in 4/5 patients. Tremor appeared 3 months-5 years after the onset of GBS, when patients had regained near normal muscle strength and deep tendon jerks were well elicitable. Electrophysiological analysis of tremor strongly suggested the presence of a central oscillator in all patients.

Conclusion

NT is not limited to chronic inflammatory or hereditary neuropathies and may occur in the recovery phase of GBS. The tremor is characterized by a high frequency, jerky postural tremor with dystonic posturing. Electrophysiological evaluation suggests the presence of a central oscillator, hypothetically the cerebellum driven by impaired sensorimotor feedback.

Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring

Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.

Real-time classification of movement patterns of tremor patients

The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson's Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.