Research from Beacon Biosignals Presented at Alzheimer's Association International Conference

Company's machine learning technology uses standard EEG data to detect electrical disturbances in the brain, enabling identification of patients with rapid-progressing Alzheimer's disease

BOSTON, Aug. 8, 2022 /PRNewswire/ -- Beacon Biosignals, a startup that applies AI to EEG to unlock precision medicine for neurological and psychiatric disorders, presented research at the Alzheimer's Association International Conference, which took place July 31–Aug. 4, 2022, in San Diego. Medical Director Jay Pathmanathan, MD, PhD, discussed how the company has applied its machine learning platform to standard electroencephalography (EEG) data to detect electrical disturbances in the brain that may predict cognitive decline and explain variability in disease course for patients with Alzheimer's disease.

Pathmanathan's presentation focused on research he co-authored that confirmed the technology's ability to detect epileptiform activity with widely available clinical EEG, enabling the identification of patients with a more aggressive form of Alzheimer's disease.

Recent research has demonstrated that proteins such as tau and beta amyloid accumulate to not only cause neuronal cell death, but also create abnormal electrical activity in the brain. As neurons begin to fire abnormally, they initiate a cascade of increasing electrical activity that causes cell death due to electrical excitation. This electrical activity, which is measurable by EEG as epileptiform discharges, may generate a vicious cycle leading to accelerated cell death and deterioration for some patients.

Beacon Biosignals developed a machine learning algorithm trained to detect such epileptiform activity in patients with Alzheimer's. To perform the study, the researchers examined 90 subjects with an Alzheimer's diagnosis and 39 with mild cognitive impairment who underwent routine EEG.

All EEGs were analyzed by a machine learning-enabled interictal epileptiform discharge (IED) detection algorithm via the Beacon Platform. These EEGs were also visually analyzed for IEDs by board-certified human reviewers, with a third reviewer breaking dissenting opinions.

The researchers concluded that automated analysis of EEG data is a promising way to stratify patients and track disease progression, while enabling better patient selection for clinical trials.

"Alzheimer's disease patients face daily challenges in remaining independent and performing routine tasks, and until now we haven't had a reliable way to identify patients that may be at risk for more accelerated disease progression," said Pathmanathan. "This research demonstrates that machine-learning algorithms represent a reliable means of efficiently, reproducibly, and exhaustively detecting electrical activity in the EEGs of patients with Alzheimer's, offering a valuable source of data to identify patients whose condition may deteriorate more quickly. This can inform the development of future Alzheimer's treatments to improve the lives of these patients and their caregivers. In the future, this electrical pathology may also be the target of specific treatments."

Beacon Biosignals' platform provides an architectural foundation for discovery of robust quantitative neurobiomarkers that subsequently can be deployed for patient stratification or automated safety or efficacy monitoring in clinical trials. The powerful and validated algorithms developed by Beacon's machine learning teams can replicate the consensus interpretation of multiple trained epileptologists while exceeding human capabilities over many hours or days of recording. The Beacon Platform is currently being used across therapeutic areas such as neurodegenerative disorders, epilepsy, sleep disorders and mental illness. 

"Our study represents an important advance for medical and life sciences research in Alzheimer's disease," said Jacob Donoghue, MD, PhD, co-founder and chief executive officer of Beacon Biosignals. "Applying machine learning to EEG data will enable clinicians and researchers to better understand disease characteristics of patients and will inform treatment options and the development of therapeutics for diseases affecting the brain. We're proud to contribute to a better understanding of neurological disease and we see great potential for our platform to improve the lives of patients."

For more information about how Beacon Biosignals' technology can help advance Alzheimer's disease research, click here.

Beacon's machine learning platform for EEG enables and accelerates new treatments that transform the lives of patients with neurological, psychiatric or sleep disorders. Through novel machine learning algorithms, large clinical datasets, and advances in software engineering, Beacon Biosignals empowers biopharma companies with unparalleled tools for efficacy monitoring, patient stratification, and clinical trial endpoints from brain data. For more information, visit https://beacon.bio/. For careers, visit https://beacon.bio/careers; for partnership inquiries, visit https://beacon.bio/contact. Follow us on Twitter (@Biosignals) or LinkedIn (https://www.linkedin.com/company/beacon-biosignals).

MEDIA CONTACT
Megan Moriarty
Amendola Communications for Beacon Biosignals
913.515.7530
[email protected]

SOURCE Beacon Biosignals