Mini Review: linkages between essential tremor and Parkinson's disease?

A system for finger tremor quantification in patients with Parkinson's disease

The current diagnosis of Parkinson's disease (PD) is based on a subjective assessment by the specialist. The monitoring of the tremor that presents in the hand index fingers in a patient with Parkinson's is one of the most important parameters to diagnose the evolution of the disease in an objective manner. This research analyze the tremor in the hand index fingers of patients with PD with medication and without medication. A sensor based in a triaxial accelerometer was used to acquire the data produced by the acceleration changes of parkinsonian tremor in the case of three activities: postural tremor, action tremor and rest tremor. Acquired data were processed in Matlab; the data were filtered and the spectral power density (PSD) was estimated with the Burg periodogram. It has been verified that the system presented in this article can accurately detect the parkinsonian tremors of the patients evaluated, additionally has been found that with the medication the tremors do not disappear completely, these remained with the same frequencies of PD but with a very small amplitude.

Support vector machine classification of Parkinson's disease and essential tremor subjects based on temporal fluctuation

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the realization of treatment depends on specific medication. A novel feature is developed based on a hypothesis that tremor of PD subject has a larger fluctuation during resting than action task. Tremor signal is collected using a triaxial gyroscope sensor attached to subject's finger during kinetic and resting task. The angular velocity signal is analyzed by transforming a one-dimensional to two-dimensional signal using a relation of signal and its delay versions. Tremor fluctuation is defined as the area of 95% confidence ellipse covering the two-dimensional signal. The tremor fluctuation during kinetic and resting task is used as classification features. The support vector machine is used as a classifier and tested with 10-fold cross-validation. This novel feature provides a perfect PD/ET classification with 100% accuracy, sensitivity and specificity.

Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor

BACKGROUND

Parkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.

METHODS

35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.

RESULTS

The ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.

CONCLUSIONS

This study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.

Temporal fluctuation analysis of tremor signal in Parkinson's disease and Essential tremor subjects

Tremor is a common symptom shared in both Parkinson's disease (PD) and Essential tremor (ET) subjects. The differential diagnosis of PD and ET tremor is important since the treatment depends on specific medication. A novel feature was developed based on a hypothesis stating that the tremor of PD subject has a larger fluctuation while performing resting task than action task. Tremor signal was collected using a gyroscope sensor attached to subject's finger. The angular velocity signal was analyzed by transforming a one-dimensional to two-dimensional signal based on relation of different units of time-delay. The tremor fluctuation was defined as the area of 95% confidence ellipse covering the two-dimensional signal. Experimenting with 32 PD and 20 ET subjects, a ratio of fluctuation of resting to kinetic task can be a sensitive feature to discriminate PD from ET with 100% accuracy.

Ultrasonographic (TCS) and clinical findings in overlapping phenotype of essential tremor and Parkinson's disease (ET-PD)

BACKGROUND

Essential tremor (ET) and Parkinson's disease (PD) are considered distinct disorders. The aim of the study was to look for a link or any distinguishing features by transcranial sonography (TCS), together with the clinical examination findings in a group of patients with overlapping phenotype of ET and PD (ET-PD).

METHODS

A prospective observational case-control study was carried out from the 3rd January 2011 until 30th January 2013 at the Hospital of Lithuanian University of Health Sciences. The final study group consisted of 15 patients with ET-PD, 116 patients with ET-only and 141 patients with PD-only. The control group included 101 subjects. Clinical diagnosis was of a diagnostic standard.

RESULTS

The main ultrasonographic findings in the ET-PD group were similar to those of the PD-only: hyperechogenicity of the substantia nigra (66.7%, p < 0.001) and nuclei raphe interruptions/absence (38.5%, p < 0.001). The single distinguishing TCS finding in ET-PD group was a lentiform nucleus hyperechogenicity (26.7%), however this was only significant when compared to controls (p = 0.006). An asymmetrical onset of symptoms (73.3%) in ET-PD group was characteristic to PD-only. The ET-PD patients had the longest disease duration (median 6 years, p < 0.001), the most frequent rate of positive family history (53.3%, p = 0.005), rather low prevalence of cogwheel rigidity (26.7%, p < 0.001), and higher mean Hoehn & Yahr scores compared to PD-only (2.6 ± 0.8 vs. 1.8 ± 0.8, p = 0.012).

CONCLUSIONS

The main TCS findings of the present study in patients with overlapping ET-PD phenotype were similar to the PD-only group. The highest positive family history rate among ET-PD patients indicates a strong hereditary predisposition and needs genetic underpinnings. Some ET patients, who look like they may be developing co-morbid PD clinically, may have an alternative diagnosis for Parkinsonism, which could be delineated by TCS examination.