Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer

Abstract

Background: The differential diagnosis between patients with essential tremor (ET) and those with Parkinson's disease (PD) whose main manifestation is tremor may be difficult unless using complex neuroimaging techniques such as 123I-FP-CIT SPECT. We considered that using smartphone's accelerometer to stablish a diagnostic test based on time-frequency differences between PD an ET could support the clinical diagnosis.

Methods: The study was carried out in 17 patients with PD, 16 patients with ET, 12 healthy volunteers and 7 patients with tremor of undecided diagnosis (TUD), who were re-evaluated one year after the first visit to reach the definite diagnosis. The smartphone was placed over the hand dorsum to record epochs of 30 s at rest and 30 s during arm stretching. We generated frequency power spectra and calculated receiver operating characteristics curves (ROC) curves of total spectral power, to establish a threshold to separate subjects with and without tremor. In patients with PD and ET, we found that the ROC curve of relative energy was the feature discriminating better between the two groups. This threshold was then used to classify the TUD patients.

Results: We could correctly classify 49 out of 52 subjects in the category with/without tremor (97.96% sensitivity and 83.3% specificity) and 27 out of 32 patients in the category PD/ET (84.38% discrimination accuracy). Among TUD patients, 2 of 2 PD and 2 of 4 ET were correctly classified, and one patient having PD plus ET was classified as PD.

Conclusions: Based on the analysis of smartphone accelerometer recordings, we found several kinematic features in the analysis of tremor that distinguished first between healthy subjects and patients and, ultimately, between PD and ET patients. The proposed method can give immediate results for the clinician to gain valuable information for the diagnosis of tremor. This can be useful in environments where more sophisticated diagnostic techniques are unavailable.

Fig 1

1A, Recording tremor in rest position. 1B, Recording tremor in postural position.

Fig 2. Upper section, examples of the smartphone’s accelerometer signal morphology in Parkinson’s Disease (PD) and essential tremor (ET).

Lower section, examples of normalized power spectral density of tremor in PD and ET subjects.

Fig 3

3A, ROC curves for rest total power spectra (TPS A) and postural total power spectra (TPS B) comparing tremulous and healthy subjects. 3B, ROC curves for relative energy feature (RE) and Relative Power Contribution to the first harmonic feature (RPC). Blue and red crosses mark the highest discriminative threshold for each ROC curve. AUC, area under the curve. CI, confidence interval, followed by sensitivity and specificity for the given value.

Fig 4. Discrimination of Parkinson’s disease and essential tremor subjects using the threshold obtained from relative energy feature ROC curve.

15 of 17 Parkinson’s and 13 of 15 essential tremor patients were correctly classified. PD region, values in this region are classified as Parkinson’s disease. ET region, values in this region are classified as essential tremor.

Fig 5. Relative energy threshold was tested with patients with undecided diagnosis at the moment of the recording.

2 out of 2 Parkinson’s disease and 2 out of 4 essential tremor patients were correctly classified. One subject that presented Parkinson’s and essential tremor concomitance was classified as Parkinson’s disease by the test. PD, Parkinson’s disease ET, essential tremor. AP, atypical presentation.