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Coverage: 1967-2014 (Vol. 1, No. 1 - Vol. 48, No. 4)
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Subjects: Education, Social Sciences
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We are entering a new era in Parkinson’s disease management. Compound clinical scores are limited by the frequency of the measurements and the subjectivity of these assessments. Hence, there is an unmet medical need for quantitative and reliable tests that can complement clinical scales for various applications (e.g. drug response evaluation, at-risk cohorts identification, among others). Our envisioned approach could complement these standards by using the unconstrained use of digital devices in a setting representative of the daily routine (i.e. an ecologically valid environment) and allow for an objective and frequent evaluation of PD motor signs. In the experiments presented, our method shows promise and can accurately discriminate an early PD population with mild parkinsonian signs from a healthy control group.
Finding consistent patterns of early Parkinson’s from uncontrolled typing might appear an intractable problem, the style of typing varies greatly across subjects, an unpredictable number of pauses can be made (leading to sparse data), text typed varies greatly and so do the speed of typists. Further, the challenge was to identify a typing pattern that could be related to the pathophysiology of PD and that is not confounded by volitional action or other diseases that might impair motor function.
The problem of typing style heterogeneity was made tractable by adopting three strategies: using the Hold Time (HT) time series, automatically learning patterns from the data and considering each typing window locally. Additionally, the act of pressing and releasing a key is not influenced to a great extent by the text typed, typing speed, or typing style. We witnessed subjects who were “hunt and peck” typists having similar HTs as touch typists.
Regarding the pathophysiology of PD, typing (intended as the act of pressing and releasing keys) can be defined as a habit. Habits are greatly controlled by the basal ganglia and are more affected than goal-directed actions in PD. From the different keystroke dynamics, we selected HT: in addition to being independent of typing skills, it is largely not under conscious control. Each HT lasts on the order of 100 ms, a time so short to make implausible that subjects could consciously control it to within the 0–500 ms range where the vast majority of data lies. While a user could intentionally hold a key down for a long time, that is also not something that would happen for a prolonged period of typing. Therefore, we believe that the HT time series captures transient bradykinesia effects in typing that prevent PD subjects from lifting their fingers from keys in a consistent manner. This dynamic variance, i.e. heteroscedasticity, for motor measurements involving PD patients was already reported27,28. To our knowledge, our model is a first attempt to quantify this effect during a natural, uncontrolled task that did not involve visual or auditory stimuli. With nQi we strive to generate a straightforward numerical metric able to measure fine motor finger-based PD signs that could eventually be interpreted by physicians and patients alike.
We focused on early PD rather than more advanced disease stages, because it is with the early PD population where a low barrier to entry diagnostic tool could have particularly significant impact. For example, in developing treatments, particularly neuroprotective ones, clinical trial participants need to be recruited at the earliest stage possible. An easy to use diagnostic tool might also aid in lowering the number of undiagnosed PD subjects, thereby leading to adequate medical management8. Accordingly, we evaluated the discriminative power of our algorithm starting with a dataset that includes PD patients at the early stages when motor manifestation can be very subtle.
In our cohorts, the discriminative power of nQi scores was high even when adjusted for sex, age, education or typing skills. Still, we are far from having a fully validated diagnostic tool specific for PD motor signs. In methods like these, there is always a risk of overfitting the data such that the algorithm has limited generalizability to individuals whose PD signs are not represented in the dataset. The full external validity can be only demonstrated by a prospective study with a large sample size that encompasses the broad spectrum of motor and non-motor characteristics that are present in a sporadic PD population (such as cognition, depressive symptoms, apathy or anxiety).
Before undertaking such a study, several strategies can be used to increase confidence in the approach and mitigate the risk of overfitting. One set of strategies relates to how the training and test sets are handled. Here, we used a conservative cross-validation strategy where with two separate data sets, we train on one data set, and test with the other, and then repeat with the train and test datasets swapped. The resulting area under the ROC curve (AUC) for the combined datasets was 0.81 which is comparable or better than two other common quantitative motor performance tests to evaluate upper limbs: alternating finger tapping and single key tapping (see Fig. 3). As we and others accumulate more datasets, it will be possible to refine the nQi model with additional training data and compare it to other tests. To accelerate these lines of research we are making the datasets used in this paper available to the research community.
Computer use in the various age groups is an important factor to take into consideration for the applicability of our approach, a factor that is especially important given that older subjects are generally less likely to be computer users. Nonetheless, the United States Census Bureau estimates that the percentage of individuals owning a computer has grown to 71% in the 65 + age group as of 201329. As this figure grows, the adoption of an approach such as the one described in this paper increases in feasibility.
The use of the natural interaction with commodity digital devices as a data source brings significant advantages. Data can be captured at home with a frequency much higher of the current standard of care. Furthermore the data capturing platform can be deployed easily at large scale and at a low cost. Additionally, high frequency at-home data collection addresses the problem of the artificial circumstances created during a consultation with a physician: it is not uncommon for patients to have unrepresentative scores in motor tests, either because of the Hawthorne effect30 or because of the alteration of the timing of their medication to ensure that they arrive in good shape to the visit31. A data collection system that integrates with the normal use of a keyboard will also enable high compliance for all subjects who use digital devices for their job or as a pastime. The work described here provides a first step towards enabling that future, by showing in the limited context of a typing test done in a clinic for a small cohort, a good ability to discriminate between PD and control, and by doing so for the challenging case of an early PD population.
All the experimental protocols were approved by the Massachusetts Institute of Technology, USA (Committee on the Use of Humans as Experimental Subjects approval no. 1402006203 and no. 1412006804), Hospital 12 de Octubre, Spain (no. CEIC:14/090) and Hospital Clinico San Carlos, Spain (no. 14/136-E) and HM Hospitales, Spain (no. 15.05.796-GHM). Informed consent was obtained from all subjects involved in the study. All the experiments and recruitment were carried out in accordance with the relevant institutional guidelines.
This dataset consisted of 31 subjects, 18 early PD cases, i.e. patients without axial signs (Hoehn-Yahr stages I and II), without motor fluctuations and with a confirmed diagnosis for less than 5-years and 13 healthy spouses without any sign of parkinsonism as controls32 (one PD subject 6 years since diagnosis was included as early-PD because of very mild PD motor signs). Only participants who self-reported that they used a laptop or desktop computer for at least thirty minutes per day and completed all visits were considered eligible. We excluded participants with cognitive impairment or dementia, subjects with upper limb functional limitation, antipsychotics/sedative users and participants with sleep disorders. The subjects were recruited from two movement disorder units in Madrid (Spain). Table 3 shows demographic and clinical information of this cohort.
Each subject was asked to visit a movement disorder unit twice where the motor tests and clinical evaluation were performed, with 7 to 30 days between each visit. Patients taking levodopa, a symptomatic relief medication for PD, were asked to refrain from taking the medication for 18 hours before the visit.
This cohort was recruited as part of an on-going longitudinal PD study. It consisted of 54 subjects, 24 de-novo PD cases, i.e. newly diagnosed, drug naïve patients, and 30 healthy controls without any sign of parkinsonism. The PD subjects were recruited from 8 different health institutions in Madrid (Spain), the controls were mainly patient’s spouses or subjects connected to the PD community. Only participants who self-reported that they used a laptop or desktop computer for at least thirty minutes per day were considered eligible. We excluded participants with cognitive impairment or dementia, subjects with upper limb functional limitation, antipsychotics/sedative users and participants with sleep disorders. Table 3 shows demographic and clinical information of this cohort.
Each visit involved a clinical evaluation, finger tapping tests and our typing test. The clinical evaluation was undertaken by a movement disorder specialists who also filled-in the motor section in the Unified Parkinson’s Rating Scale (UPDRS-III)11. In the Early-PD dataset it was performed by two blinded specialists while in the De-novo dataset by a single one.
The finger tapping tests are a common way to quantify upper limbs dexterity in clinical studies. In the “single key tapping” test, subjects repeatedly pressed a single button for 60 seconds, as fast as possible, first with their dominant hand, then with the non-dominant hand. The final score was the average number of buttons pressed between the two hands. In the “alternating finger tapping” test, the subjects had to alternatively press two buttons, with a distance of approximately 25 cm between the two, with their index finger. The test was repeated for both hands and the final score was the average number of buttons pressed between the two hands. The “alternating finger tapping” test was introduced while the early-PD study was ongoing, because of this 5 PD subjects and 4 controls could only be measured with the “single key tapping” test.
In the typing test, the subjects transcribed a folk tale on standard word processor. The folk tale was randomly selected from a collection avoiding repetitions of the same text for each given subject. This was to limit the learning effect due to the content of the text. The subjects were instructed to type as they normally would do at home and they were left free to correct typing mistakes only if they wanted to. All subjects typed for an average of 14 minutes (2.9 std) with a standard word processor on a Lenovo G50-70 i3-4005U with 4MB of memory and a 15 inches screen running Manjaro Linux. In the background a custom piece of software recorded the timestamps of each key press and depress, stored it in memory and sent it to a remote database at the end of the writing task.
The timing resolution of the key acquisition software was evaluated by injecting a series of software generated key presses and releases into the operating system event queue. A stream of two consecutive events (key-press, key-release) was generated every 100 milliseconds for a total running time of 15 minutes. We measured a temporal resolution of 3/0.28 (mean/std) milliseconds. The versions for Windows and Macintosh can be downloaded at https://www.neuroqwerty.com.
PD patients and controls of the Early-PD dataset were tested twice, while the subjects in the De-novo dataset once. In order to make our experiments consistent, we averaged the clinical and motor tests scores across the two repetitions in the Early-PD dataset.