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

Abstract

BackgroundThe 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.MethodsThe 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.ResultsWe 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.ConclusionsBased 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.

Computer vision of smartphone video has potential to detect functional tremor

“…In this early report, we demonstrate that entrainment, a highly specific feature of FT [5], can be detected by measurement of pixel movement in standard smartphone video. Existing objective methods such as accelerometry can aid distinction of different tremor types [4].…”

“…Although laboratory accelerometery can distinguish functional tremor from other tremors [4], it is a limited resource. Smartphone accelerometers can measure tremor frequency and discriminate tremor type [5], but a clinical test whereby patients hold a smartphone is not one that has entered routine practice. An alternative ubiquitous item of hardware that could be used to assess tremor is the camera (present in smartphones, personal computers, CCTV).…”

Mobile-based Monitoring of Parkinson's Disease

“…Smartphones contain a wide variety of inbuilt sensors useful for PD observation. The severity of the patients' tremor has been evaluated using an iPhone attached to a glove [11] and to classifying essential tremor and PD tremor [3,16]. The mPower study collected both PD and non-PD participants' data via health surveys and physical activities for 14 days every three months [1,2].…”

Predicting motor, cognitive & functional impairment in Parkinson's

“…A multi‐device study is being planned, that uses smartphones to capture questionnaire data and to store tremor data recorded by smartwatches, alongside tablet‐based assessments, with the aim of differentiating Parkinson's from Essential Tremor . However so far, studies using smartphones alone have focused on equipping the clinician; in distinguishing individuals with and without Parkinson's and working to derive smartphone scales with which to measure disease severity . Our aim was to use smartphone data to predict outcomes of direct clinical relevance to people with Parkinson's and clinicians, alike.…”