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Way of Detecting Parkinson’s Early via Typing Patterns Being Tested and Refined

typing patterns

A type of computational analysis that works to analyze typing patterns may help in detecting motor signs of Parkinson’s disease at early stages, the researchers who developed the analysis report.

This new method, which appeared to allow researchers to discriminate between people with early Parkinson’s and those without the disease, may also speed data collection and analysis of disease states across neurodegenerative ills.

The study, “Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots,” was published in the IEEE/CAA Journal of Automatica Sinica.

Objective measures of Parkinson’s motor signs are crucial for diagnosing the disease early and correctly, as well as for monitoring progression and assessing treatment response. Early detection of Parkinson’s disease (PD) is particularly relevant, as people at early stages of the disease are more likely to benefit from neuroprotective treatments.

“Because a significant amount of the [midbrain’s] substantia nigra neurons have already been lost or impaired before the onset of motor features, people with PD may first start experiencing symptoms later in the course of the disease,” the researchers wrote.

Current methods to evaluate motor symptoms focus on a person’s movements and balance while walking, which requires a trained specialist and clinic visits. As such, they limit the frequency at which disease state and progression is likely to be assessed.

In addition, these methods involve the collection of data “during relatively long walking periods, causing discomfort to the participants or impracticability of performance in clinical settings,” the researchers wrote.

Increasing efforts are being made to develop easier and more accessible methods of detecting Parkinson’s motor signs, including the use of digital technologies.

A previous study showed that analyzing the time a person takes between pressing and releasing a key while typing (key hold time) could be used to detect motor problems in the early stages of Parkinson’s. The analysis involves a computational self-learning algorithm able to generate a Parkinson’s disease motor index based on key hold times.

This approach — which measures key hold times during the normal use of a computer without any change in hardware — was shown to efficiently distinguish people with and without Parkinson’s using data from either a controlled clinic setting or an uncontrolled at-home setting.

While it has the potential to be an objective and user-convenient tool to detect Parkinson’s, this approach “require the time series of length to be considerably long.”

Researchers at Linköping University (LiU), in Sweden, developed a new way of analyzing typing patterns based on a very short time series of data for machine learning (artificial neural networks that learn from data). It intends to avoid discomfort to participants in performing long physical tasks for data recording, and to effectively differentiate Parkinson’s patients from healthy people.

The team used the first short segments of the key hold time data from 43 healthy individuals and 42 Parkinson’s patients (average time since diagnosis, 3.9 years), part of a publicly available database. Of note, patients were on parkinsonian medication, but stopped their treatments for at least the 18 hours before the typing test.

Researchers first translated the data into a set of two-dimensional, grey-scale images of texture, called fuzzy recurrence plots, which were then used for machine learning with an algorithm named long short-term memory (LSTM)-based deep learning.

The use of fuzzy recurrence plots in machine learning allowed for distinguishing among people with and without Parkinson’s using less data than current methods.

According to a press release, researchers believe their findings are “encouraging,” and plan to further explore the use of fuzzy recurrence plots and improve the algorithm to better determine a patient’s disease state.

They also highlighted that this approach may be applied to other types of data, with a goal of improving machine learning and reducing the amount of data required to achieve good results for differentiating healthy people from those with disease.

The research team plans to evaluate this approach against walking and balance data collected from people with Parkinson’s and other neurodegenerative diseases, such as Huntington’s disease and amyotrophic lateral sclerosis (ALS).

The post Way of Detecting Parkinson’s Early via Typing Patterns Being Tested and Refined appeared first on Parkinson’s News Today.

New Software May Help Detect Early Parkinson’s Motor Signs At Home

Parkinson's software

Researchers have validated a software that evaluates typing patterns with keyboards to detect Parkinson’s disease-specific motor impairment. This approach, done in an at-home setting, may allow early detection of the disease, as well as monitor disease progression.

The study, “Detecting Motor Impairment in Early Parkinson’s Disease via Natural Typing Interaction With Keyboards: Validation of the neuroQWERTY Approach in an Uncontrolled At-Home Setting,” was published in the Journal of Medical Internet Research.

Early detection of Parkinson’s disease can be crucial to prevent disease progression. The current standard to evaluate motor signs is the Unified Parkinson’s Disease Rating Scale part III (UPDRS-III), which requires a trained specialist and attendance at the clinic. This limits the frequency at which disease state and progression can be assessed.

Recently, efforts have been made to develop more accessible methods to detect motor signs of Parkinson’s, including the use of digital technologies. Analysis of the time a person takes between pressing and releasing a key while typing (key hold time) was found to be a reliable method to detect impaired psycho-motor function.

A recent study showed that analysis of key hold times could detect motor signs in the early stages of Parkinson’s in a controlled typing task in the clinic. The work involved a new computational algorithm able to generate a Parkinson’s disease motor index based on key hold times, called “neuroQWERTY index” (nQi).

This approach measures the key hold times during the normal use of a computer without any change in hardware and converts it to a neuroQWERTY index. This has the potential to detect motor problems remotely, in a natural environment (like home),  which would allow data to be collected much more often than current standard of care.

Researchers evaluated the use of the neuroQWERTY approach in an uncontrolled at-home setting. This study analyzed the baseline data collected from participants who had less than five years of disease and were about to initiate dopaminergic therapy, in a six-month Parkinson’s clinical trial (NCT02522065).

At the beginning of the trial, 60 participants (30 early-diagnosed Parkison’s patients and 30 healthy controls) underwent clinical evaluation, that included the UPDRS-III method and the neuroQWERTY typing test in clinic (which takes approximately 15 minutes).

Participants who reported using the computer for at least 30 minutes a day had the platform and software installed in their personal laptops. Data was collected for seven days after the first log-in to the neuroQWERTY platform, and participants were encouraged to type an email or a document for at least 15 minutes per day.

At the end, only 52 participants had enough data for the final analysis — 25 Parkinson’s patients and 27 healthy individuals. Researchers compared that data with the one collected during the typing task in the clinic.

The neuroQWERTY approach at home was able to distinguish Parkinson’s patients from healthy individuals through the analysis of at-home typing patterns, and had a comparable performance to that performed in the clinic.

“These results prove that the data collected from subjects’ routine use of the computer also are valid to detect PD-related motor signs, getting us closer to our ultimate goal of providing an objective ambulatory tool to monitor PD progression,” researchers wrote.

The team now wants to develop a tool that can track Parkinson’s progression and therapeutic effectiveness. However, additional studies must be performed to validate the neuroQWERTY approach to monitor Parkinson’s disease progression over time.

The post New Software May Help Detect Early Parkinson’s Motor Signs At Home appeared first on Parkinson’s News Today.

Source: Parkinson's News Today