Ian Butterworth

Computer keyboard interaction as an indicator of early Parkinson's disease.

Parkinson’s disease (PD) is a slowly progressing neurodegenerative disease with early manifestation of motor signs. Objective measurements of motor signs are of vital importance for diagnosing, monitoring and developing disease modifying therapies, particularly for the early stages of the disease when putative neuroprotective treatments could stop neurodegeneration. Current medical practice has limited tools to routinely monitor PD motor signs with enough frequency and without undue burden for patients and the healthcare system. In this paper, we present data indicating that the routine interaction with computer keyboards can be used to detect motor signs in the early stages of PD. We explore a solution that measures the key hold times (the time required to press and release a key) during the normal use of a computer without any change in hardware and converts it to a PD motor index. This is achieved by the automatic discovery of patterns in the time series of key hold times using an ensemble regression algorithm. This new approach discriminated early PD groups from controls with an AUC = 0.81 (n = 42/43; mean age = 59.0/60.1; women = 43%/60%;PD/controls). The performance was comparable or better than two other quantitative motor performance tests used clinically: alternating finger tapping (AUC = 0.75) and single key tapping (AUC = 0.61).

Psychomotor Impairment Detection via Finger Interactions with a Computer Keyboard During Natural Typing

Modern digital devices and appliances are capable of monitoring the timing of button presses, or finger interactions in general, with a sub-millisecond accuracy. However, the massive amount of high resolution temporal information that these devices could collect is currently being discarded. Multiple studies have shown that the act of pressing a button triggers well defined brain areas which are known to be affected by motor-compromised conditions. In this study, we demonstrate that the daily interaction with a computer keyboard can be employed as means to observe and potentially quantify psychomotor impairment. We induced a psychomotor impairment via a sleep inertia paradigm in 14 healthy subjects, which is detected by our classifier with an Area Under the ROC Curve (AUC) of 0.93/0.91. The detection relies on novel features derived from key-hold times acquired on standard computer keyboards during an uncontrolled typing task. These features correlate with the progression to psychomotor impairment (p < 0.001) regardless of the content and language of the text typed, and perform consistently with different keyboards. The ability to acquire longitudinal measurements of subtle motor changes from a digital device without altering its functionality may allow for early screening and follow-up of motor-compromised neurodegenerative conditions, psychological disorders or intoxication at a negligible cost in the general population.

Analysis of white blood cell dynamics in nailfold capillaries.

Based on video data acquired with low-cost, portable microscopy equipment, we introduce a semi-automatic method to count visual gaps in the blood flow as a proxy for white blood cells (WBC) passing through nailfold capillaries. Following minimal user interaction and a pre-processing stage, our method consists in the spatio-temporal segmentation and analysis of capillary profiles. Besides the mere count information, it also estimates the speed associated with every WBC event. The accuracy of our algorithm is validated through the analysis of two capillaries acquired from one healthy subject. Results are compared with manual counts from four human raters and confronted with related physiological data reported in literature.

Non-invasive detection of severe neutropenia in chemotherapy patients by optical imaging of nailfold microcirculation

White-blood-cell (WBC) assessment is employed for innumerable clinical procedures as one indicator of immune status. Currently, WBC determinations are obtained by clinical laboratory analysis of whole blood samples. Both the extraction of blood and its analysis limit the accessibility and frequency of the measurement. In this study, we demonstrate the feasibility of a non-invasive device to perform point-of-care WBC analysis without the need for blood draws, focusing on a chemotherapy setting where patients’ neutrophils—the most common type of WBC—become very low. In particular, we built a portable optical prototype, and used it to collect 22 microcirculatory-video datasets from 11 chemotherapy patients. Based on these videos, we identified moving optical absorption gaps in the flow of red cells, using them as proxies to WBC movement through nailfold capillaries. We then showed that counting these gaps allows discriminating cases of severe neutropenia (<500 neutrophils per µL), associated with increased risks of life-threatening infections, from non-neutropenic cases (>1,500 neutrophils per µL). This result suggests that the integration of optical imaging, consumer electronics, and data analysis can make non-invasive screening for severe neutropenia accessible to patients. More generally, this work provides a first step towards a long-term objective of non-invasive WBC counting.

A wearable physiological hydration monitoring wristband through multi-path non-contact dielectric spectroscopy in the microwave range

Maintaining good physiological hydration is critical and can have marked effects on health and performance. Yet no reliable, simple, and widely-accepted method for hydration assessment exists. Even in clinical settings current methods are based on a series of empirical symptomatic observations, and though various candidate quantitative methods have been trialed, none has proven reliable or simple enough to become widely-accepted for either medical or lifestyle applications. A new type of wearable wristband device is detailed, simulated, and tested. This new monitoring mechanism tracks minute changes in the hydration of the wrist, which is used as a representative peripheral anatomical volume for tracking changes in hydration status. Measurement is made through a multi-path non-contact dielectric spectroscopy approach in the 2-6 GHz range. Numerical measurement methods based on multi-path broad spectral attenuation averaging, and principal component analysis are explored. A prototype of the design is constructed and programmed, and preliminary measurements from this device are shown to achieve a good correlation to cross section of the forearm, while being insensitive to alignment variations that are typical of a wearable wristband.

Author Correction: Computer keyboard interaction as an indicator of early Parkinson’s disease

A correction has been published and is appended to both the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Minimal Clinically Important Difference for UPDRS-III in Daily Practice: UPDRS' Important Difference in Routine Practice

Alvaro S anchez-Ferro, MD,* Michele Matarazzo, MD, Pablo Mart ınez-Mart ın, MD, Jose Carlos Mart ınezAvila, PhD, Agust ın G omez de la C amara, MD, Luca Giancardo, PhD, Teresa Arroyo Gallego, MSc, Paloma Montero, MD, Ver onica Puertas-Mart ın, PhD, Ignacio Obeso, PhD, Ian Butterworth, MSc, Carlos S. Mendoza, PhD, Maria Jos e Catal an, MD, Jos e Antonio Molina, MD, F elix Bermejo-Pareja, MD, Juan Carlos Mart ınez-Castrillo, MD, Lydia L opez-Manzanares, MD, Araceli Alonso-C anovas, MD, Jaime Herreros Rodr ıguez, MD, and Martha Gray, PhD

Measurement of very low concentration of microparticles in fluid by single particle detection using acoustic radiation force induced particle motion

Analysis and characterization of microparticles suspended in fluid have important applications in various industries. In this work, a new ultrasound-based method is proposed that enables the measurement of very low concentrations of particles in fluid. By using single particle detection, the method provides particle counts and acquires backscatter signals from individual particles. The detection method utilizes the motion of the particle induced by the acoustic radiation force and acoustic streaming. The identification of an individual particle is enabled by detecting its trajectory using a line-by-line cross-correlation and modified Hough transform. The proposed method has the potential to be used as a laboratory or a clinical device for analyzing fluid samples and studying single-cell characteristics.