Rapid tremor frequency assessment with the iPhone accelerometer

Abstract

The physician is often seeking more efficient ways of performing patient assessments. Currently, measuring tremor frequency requires expensive and bulky equipment. We propose the use of the in-built accelerometer of the iPhone via the iSeismo application for rapid measurement of tremor frequency. We use this device in a series of 7 different tremor cases, and show that the frequency measurements on the iSeismo graph closely match the more sophisticated EMG analysis during tremor. This is a preliminary confirmation of the usefulness of this device in the clinical setting for quick assessment of the dominant frequency component in a variety of tremors.

Introduction

Tremor, a ’rhythmical, involuntary oscillatory movement of a body part’ [1] is the most common movement disorder. Tremor can appear alone as in essential tremor (ET) or as part of other conditions, such as Parkinson’s disease, multiple sclerosis, or cerebellar damage.

Frequency as a property of tremor has been studied for quite some time; Holmes in 1904 identified that tremor frequencies were characteristic to certain conditions, and thought that this might be linked to their pathophysiology [2]. Tremor can be found in a wide variety of frequencies and amplitudes. For example, the frequency of classical rest tremor in Parkinson’s disease is between 4 and 7 Hz with large variations in tremor amplitude [1]. Essential tremor and dystonic tremor have a frequency between 4 and 12 Hz, multiple sclerosis between 2 and 10 Hz, and orthostatic tremor between 13 and 18 Hz [3].

Although clinical rating scales are quite useful for a rough assessment of tremor amplitude, assessing frequency by observation is by definition approximate and inaccurate. Clinical assessments then, are often combined with EMG measurements and accelerometry to determine tremor amplitude and frequency [4].

The ways of measuring tremor range dramatically. Innovative methods that have been used to measure tremor include an electromagnetic tracking device [5], a mechanical linkage device on the fingertip [6], lasers [7], and digitizing tablets [8], to name a few.

Although these various techniques are interesting and useful in their own right, they have demonstrated limited usability in a clinical setting, where flexibility, efficiency, and accuracy are paramount. The use of portable triaxial accelerometers is perhaps the easiest way to assess tremor [9], however this also involves purchase and transportation of necessary equipment from patient to patient. All in all, the existing methods for tremor assessment are quite cumbersome and expensive.

The challenge is finding a universally accessible, easily used device that can give a quick, accurate representation of tremor characteristics. Here, we present the use of the iPhone^® seismograph application (iSeismo by ObjectGraph LLC) in tremor detection and measurement. The freely available application uses the in-built accelerometer of the iPhone (or iPod Touch^®) to measure movement in the X, Y, and Z axes relative to the device. These data are then displayed graphically showing the three axes and their predominant frequency band.

Although originally developed as an earthquake detector, here we show the iSeismo application can act as an easily accessible way for clinicians who own this popular device to measure pathological tremor simply and accurately.

The local research ethics committee approved methods in this study, and written informed consent was obtained from all patients. A total of 7 patients were used in this study, with the following conditions: Essential tremor (2 cases, 1 with implanted deep brain stimulator) Parkinson’s tremor, multiple sclerosis tremor, post-stroke tremor, dystonic tremor, and orthostatic tremor. This allowed comparison of the iPhone with the EMG analysis at various tremor frequencies.

The iPhone was strapped securely to the tremorous limb (either forearm or lower leg) and tremor was recorded using the iSeismo application for 30 s concurrently with EMG recordings. On-screen spectral analysis of tremor in three dimensions was saved as a screen capture for later comparison with the EMG.

Electromyograms (EMG) were recorded from disposable adhesive Ag/AgCl electrodes (H 207 PT; Kendall, Tyco International, Germany) placed roughly 1 cm apart on the belly of the muscle utilising a bipolar configuration. The muscles studied included flexor carpi radialis and extensor carpi radialis for all patients except orthostatic tremor where tibialis anterior was used. Parkinson’s, post-stroke, and dystonic tremors were captured at rest, while essential tremor and multiple sclerosis tremors were with arms straight out (postural position), and orthostatic tremor was with the patient standing. Signals were amplified (×1,000) using isolated CED 1902 amplifiers (Cambridge Electronic Design, Cambridge, UK), filtered (0–500 Hz) and digitised at 16 bit resolution using CED 1401 mark II analogue-digital converter (Cambridge Electronic Design, Cambridge, UK) at a sample rate of 2.5 kHz. Recordings were displayed online on a personal computer using Spike 2 (Cambridge Electronic Design, Cambridge, UK) and saved to disk for off-line analysis.

Data were analysed off-line using custom written software in the MatLab (Mathworks Inc., Natick, MA, USA) numerical simulation environment. A 30 s data segment free from large movement artefact, recorded during tremor, was exported at a sample rate of 1 kHz. Prior to further analysis data was bandpass filtered between 2 and 300 Hz. The signal was then full-wave rectified and the power spectral density (PSD) calculated using Welch’s modified periodogram approach utilising a Hanning window over 2 s disjointed sections [10]. Each PSD generated was evaluated for the frequency of tremor by identifying the highest amplitude peak and comparing this to the iPhone recording, which generates a frequency peak on-screen. The axis which best corresponded to the direction of movement was selected for this analysis. We focused only on the predominant frequency, as iSeismo does not properly measure amplitude, which allowed easy comparison across tremors and methods.