Emotional Cognition Analysis Enables Near-Perfect Parkinson's Detection

A joint research team from the University of Canberra and Kuwait College of Science and Technology has achieved groundbreaking detection of Parkinson's disease with near-perfect accuracy, simply by analyzing brain responses to emotional situations like watching video clips or images. The findings offer an objective way to diagnose the debilitating movement disorder, instead of relying on clinical expertise and patient self-assessments, potentially enhancing treatment options and overall well-being for those affected by Parkinson's disease. The study was published Oct. 17 in Intelligent Computing, a Science Partner Journal.

Their emotional brain analysis focuses on the difference in implicit emotional reactions between Parkinson's patients, who are generally believed to suffer from impairments in recognizing emotions, and healthy individuals. The team demonstrated they can identify patients and healthy individuals with an F1 score of 0.97 or higher, based solely on brain scan readings of emotional responses. This diagnostic performance edges very close to 100% accuracy from brainwave data alone. The F1 score is a metric that combines precision and recall, where 1 is the best possible value.

The results show that Parkinson's patients displayed specific emotional perception patterns, comprehending emotional arousal better than emotional valence, which means they are more attuned to the intensity of emotions rather than the pleasantness or unpleasantness of those emotions. The patients were also found to struggle most with recognizing fear, disgust and surprise, or to confuse emotions of opposite valences, such as mistaking sadness for happiness.

The researchers recorded electroencephalography - or EEG - data, measuring electrical brain activity in 20 Parkinson's patients and 20 healthy controls. Participants watched video clips and images designed to trigger emotional responses. After the recording of EEG data, multiple EEG descriptors were processed to extract key features and these were transformed into visual representations, which were then analyzed using machine learning frameworks such as convolutional neural networks, for automatic detection of distinct patterns in how the patients processed emotions compared to the healthy group. This processing enabled the highly accurate differentiation between patients and healthy controls.

Key EEG descriptors used include spectral power vectors and common spatial patterns. Spectral power vectors capture the power distribution across various frequency bands, which are known to correlate with emotional states. Common spatial patterns enhance interclass discriminability by maximizing variance for one class while minimizing it for another, allowing for better classification of EEG signals.

As the researchers continue refining EEG-based techniques, emotional brain monitoring has the potential to become a widespread clinical tool for Parkinson's diagnosis. The study demonstrates the promise of combining neurotechnology, AI and affective computing to provide objective neurological health assessments.

Ravikiran Parameshwara, Soujanya Narayana, Murugappan Murugappan, Ibrahim Radwan, Roland Goecke, Ramanathan Subramanian.
Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson's Disease.
Intell Comput. 2024;3:0084. doi: 10.34133/icomputing.0084

Most Popular Now

Open Medical Works with Moray's Dig…

Open Medical is working with the Digital Health & Care Innovation Centre’s Rural Centre of Excellence on a referral management plan, as part of a research and development scheme to...

Generative AI on Track to Shape the Futu…

Using advanced artificial intelligence (AI), researchers have developed a novel method to make drug development faster and more efficient. In a new paper, Xia Ning, lead author of the study and...

AI could Help Improve Early Detection of…

A new study led by investigators at the UCLA Health Jonsson Comprehensive Cancer Center suggests that artificial intelligence (AI) could help detect interval breast cancers - those that develop between...

Reorganisation, Consolidation, and Cuts:…

NHS England has been downsized and abolished. Integrated care boards have been told to change function, consolidate, and deliver savings. Trusts are planning big cuts. The Highland Marketing advisory board...

AI-Human Task-Sharing could Cut Mammogra…

The most effective way to harness the power of artificial intelligence (AI) when screening for breast cancer may be through collaboration with human radiologists - not by wholesale replacing them...

Siemens Healthineers infection Control S…

Klinikum Region Hannover (KRH) has commissioned Siemens Healthineers to install infection control system (ICS) at the Klinikum Siloah hospital. The ICS aims to effectively tackle nosocomial infections and increase patient...

AI Tool Uses Face Photos to Estimate Bio…

Eyes may be the window to the soul, but a person's biological age could be reflected in their facial characteristics. Investigators from Mass General Brigham developed a deep learning algorithm...

Philips Future Health Index 2025 Report …

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, today unveiled its 2025 Future Health Index U.S. report, "Building trust in healthcare AI," spotlighting the state of...

AI-Powered Precision: Unlocking the Futu…

A team of researchers from the Department of Diagnostic and Therapeutic Ultrasonography at the Tianjin Medical University Cancer Institute & Hospital, have published a review in Cancer Biology & Medicine...

AI Model Improves Delirium Prediction, L…

An artificial intelligence (AI) model improved outcomes in hospitalized patients by quadrupling the rate of detection and treatment of delirium. The model identifies patients at high risk for delirium and...

Building Trust in Artificial Intelligenc…

A new review, published in the peer-reviewed journal AI in Precision Oncology, explores the multifaceted reasons behind the skepticism surrounding artificial intelligence (AI) technologies in healthcare and advocates for approaches...

SALSA: A New AI Tool for the Automated a…

Investigators of the Vall d'Hebron Institute of Oncology's (VHIO) Radiomics Group, led by Raquel Perez-Lopez, have developed SALSA (System for Automatic Liver tumor Segmentation And detection), a fully automated deep...