Although both diseases damage the brain, they do so in distinct ways. AD primarily affects memory and spatial awareness, while FTD targets regions responsible for behavior, personality and language. Because their symptoms can overlap, it often leads to misdiagnosis. Distinguishing between them is not just a scientific challenge but a clinical necessity, as accurate diagnosis can profoundly affect treatment, care and quality of life.
MRI and PET scans are effective for diagnosing AD but are costly, time-consuming and require specialized equipment. Electroencephalography (EEG) offers a portable, non-invasive and affordable alternative by measuring brain activity with sensors across various frequency bands. However, signals are often noisy and vary between individuals, making analysis difficult. Even with machine learning applications to EEG data, results are inconsistent and differentiating AD from FTD remains difficult.
To tackle this issue, researchers from the College of Engineering and Computer Science at Florida Atlantic University have created a deep learning model that detects and evaluates AD and FTD. It boosts EEG accuracy and interpretability by analyzing both frequency- and time-based brain activity patterns linked to each disease.
The results of the study, published in the journal Biomedical Signal Processing and Control, found that slow delta brain waves were an important biomarker for both AD and FTD, mainly in the frontal and central regions of the brain. In AD, brain activity was more widely disrupted, also affecting other regions of the brain and frequency bands like beta, indicating more extensive brain damage. These differences help explain why AD is typically easier to detect than FTD.
The model achieved more than 90% accuracy in distinguishing individuals with dementia (AD or FTD) from cognitively normal participants. It also predicted disease severity with relative errors of less than 35% for AD and 15.5% for FTD.
Because AD and FTD share similar symptoms and brain activity, telling them apart was difficult. Using feature selection, the researchers boosted the model’s specificity – how well it identified people without the disease – from 26% to 65%. Their two-stage design – first detecting healthy individuals, then separating AD from FTD – achieved 84% accuracy, ranking among the best EEG-based methods so far.
The model merges convolutional neural networks and attention-based LSTMs to detect both the type and severity of dementia from EEG data. Grad-CAM shows which brain signals influenced the model, helping clinicians understand its decisions. This approach offers a new view of how brain activity evolves and which regions and frequencies drive diagnosis – something traditional tools rarely capture.
“What makes our study novel is how we used deep learning to extract both spatial and temporal information from EEG signals,” said Tuan Vo, first author and a doctoral student in the FAU Department of Electrical Engineering and Computer Science. “By doing this, we can detect subtle brainwave patterns linked to Alzheimer’s and frontotemporal dementia that would otherwise go unnoticed. Our model doesn’t just identify the disease – it also estimates how severe it is, offering a more complete picture of each patient’s condition.”
The findings also revealed that AD tends to be more severe, impacting a wider range of brain areas and leading to lower cognitive scores, while FTD’s effects are more localized to the frontal and temporal lobes. These insights align with previous neuroimaging studies but add new depth by showing how these patterns appear in EEG data – an inexpensive and noninvasive diagnostic tool.
“Our findings show that Alzheimer’s disease disrupts brain activity more broadly, especially in the frontal, parietal and temporal regions, while frontotemporal dementia mainly affects the frontal and central areas,” said Hanqi Zhuang, Ph.D., co-author and associate dean and professor, FAU Department of Electrical Engineering and Computer Science. “This difference explains why Alzheimer’s is often easier to detect. However, our work also shows that careful feature selection can significantly improve how well we distinguish FTD from Alzheimer’s.”
Overall, the study shows that deep learning can streamline dementia diagnosis by combining detection and severity assessment in one system, cutting down on lengthy evaluations and giving clinicians real-time tools to track disease progression.
“This work demonstrates how merging engineering, AI and neuroscience can transform how we confront major health challenges,” said Stella Batalama, Ph.D., dean of the College of Engineering and Computer Science. “With millions affected by Alzheimer’s and frontotemporal dementia, breakthroughs like this open the door to earlier detection, more personalized care, and interventions that can truly improve lives.”
Study co-authors are Ali K. Ibrahim, Ph.D., an assistant professor of teaching; and Chiron Bang, a doctoral student, both with the FAU Department of Electrical Engineering and Computer Science.
- FAU -
About FAU’s College of Engineering and Computer Science:
The FAU College of Engineering and Computer Science is internationally recognized for innovative research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, biomedical engineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Master of Science in AI (first in Florida), Master of Science and Bachelor in Data Science and Analytics, and the new Professional Master of Science and Ph.D. in computer science for working professionals. For more information about the College, please visit eng.fau.edu.
About Florida Atlantic University:
Florida Atlantic University serves more than 32,000 undergraduate and graduate students across six campuses along Florida’s Southeast coast. Recognized as one of only 21 institutions nationwide with dual designations from the Carnegie Classification - “R1: Very High Research Spending and Doctorate Production” and “Opportunity College and University” - FAU stands at the intersection of academic excellence and social mobility. Ranked among the Top 100 Public Universities by U.S. News & World Report, FAU is also nationally recognized as a Top 25 Best-In-Class College and cited by Washington Monthly as “one of the country’s most effective engines of upward mobility.” As a university of first choice for students across Florida and the nation, FAU welcomed its most academically competitive incoming class in university history in Fall 2025. To learn more, visit www.fau.edu.
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