Neurological tremor: sensors, signal processing and emerging applications

Abstract

Neurological tremor is the most common movement disorder, affecting more than 4% of elderly people. Tremor is a non linear and non stationary phenomenon, which is increasingly recognized. The issue of selection of sensors is central in the characterization of tremor. This paper reviews the state-of-the-art instrumentation and methods of signal processing for tremor occurring in humans. We describe the advantages and disadvantages of the most commonly used sensors, as well as the emerging wearable sensors being developed to assess tremor instantaneously. We discuss the current limitations and the future applications such as the integration of tremor sensors in BCIs (brain-computer interfaces) and the need for sensor fusion approaches for wearable solutions.

Keywords: brain-computer interface (BCI); sensors; signal analysis; tremor.

Figure 1.

Superimposition of spirals drawn on a digitized tablet. Left panel: control subject, right panel: patient with tremor - the spirals are irregular with swerves.

Figure 2.

Brain MRI of a patient exhibiting a disabling neurological tremor in the upper limbs. Note the left inferior olivary hypertrophy (Guillain-Mollaret triangle; arrow in left panel, (A) and the lesion in putamen (arrow in right panel, (B). Axial T2-weighted images. R: right, L: left.

Figure 3.

Duration of EMG bursts in forearm muscles in various neurological disorders associated with tremor. Abbreviations: PD: Parkinson’s disease, ET: essential tremor, PN: peripheral neuropathy, OT: orthostatic tremor. Adapted from Grimaldi and Manto, 2008.

Figure 4.

Recordings of action tremor in a neurological patient. Note the oscillations in channel 2 (accelerometry, right side; accelerometer fixed on the extremity of the index finger) and the alternating pattern of EMG bursts in channels 5 (Flexor carpi radialis, right side – small arrows) and 6 (Extensor carpi radialis, right side – large arrows). Channel 1, channel 3 and channel 4 correspond to accelerometry and EMG recordings on the left side.

Figure 5.

Illustration of a textile with a matrix of surface EMG electrodes used to record upper limb tremor. The textile is designed to fit with comfort issues. Courtesy of Smartex, Italy.

Figure 6.

Example of tremor in a patient with multiple brain lesions. One single-axis accelerometer is affixed on the extremity of right index (Acc. R.) while the patient is performing the finger-to-finger test (the index fingers are maintained horizontally at a distance of about 1 cm in front of the patient). Surface EMG recordings of the flexor carpi radialis (FCR) and extensor carpi radialis (ECR) muscles are shown for both sides (L: left, R: right). Note the oscillations in the accelerometry and the regular bursts of EMG activities. Peak tremor frequency: 4.7 Hz.

Figure 7.

Example of change in alpha rhythm recorded with needle EEG on the scalp before a distal movement in contralateral upper limb. Preparation of movement is associated with a desynchronization of the EEG rhythm, with a decrease in the alpha/beta ratio. Upper panels correspond to power spectrum of EEG segments. Acc: accelerometer fixed on the hand; EMG: surface EMG in forearm muscle. Dotted vertical line: movement onset.

Figure 8.

Correlation between the clinical grade of postural tremor and crest factor obtained with accelerometry during a postural task in a group of eight patients presenting a neurological tremor. Accelerometer fixed on the extremity of the index finger. Sampling rate: 512 Hz, duration of acquisition: 30 seconds.

Figure 9.

Correlation between the crest factor (CF) during maintenance of a postural task and the score obtained using mechanical counters (repeated tapping between 2 counters separated by 30 cm during 30 seconds) in a group of six patients presenting a neurological tremor. Accelerometer fixed on the extremity of the index finger. Sampling rate: 512 Hz, duration of acquisition: 30 seconds.