Deep learning for neurological disease - overview
Deep learning for neurological disease
IBM scientists use advanced machine learning techniques to develop new methods for the personalised treatment of epilepsy and Alzheimer’s disease patients.
Using brain-inspired chips and deep learning for epilepsy treatment
Epilepsy is the most common, chronic noncommunicable disorder of the brain that affects people of all ages. Approximately 50 million individuals worldwide suffer of this disease. Around 70% of these patients respond to treatment, whereas 30% remain in the dark with no warning signs or prevention methods of an epileptic seizure. This could mean, for example, that they are not allowed to drive or are limited in their career choices.
IBM scientists are using machine learning algorithms and a neuromorphic chip called TrueNorth for the real-time analysis of electroencephalogram (EEG) data to develop new devices for epilepsy patients. The brain-inspired TrueNorth chip is powered by 1 million neurons and 256 million synapses made of silicon, yet it consumes less than 70 mW. This makes it ideal for applications in small and energy-efficient wearables with high compute capabilities. Our scientists envision an intelligent, wearable device which may identify signs of an upcoming seizure and alert a patient or automatically administer medication — seamlessly to the patient’s life. This could not only improve dramatically the lives of people with epilepsy, but also save lives.
Supporting the diagnosis and personalized prognosis for Alzheimer’s patients
The most common type of dementia is Alzheimer’s disease, which affects 70% of all dementia patients. The disease destroys brain cells and thus impairs the memory and thinking capabilities as well as the behaviour of a patient.