Classifying Harmful Brain Activity
Rank: 217th out of 2767
Classifying seizures and other harmful brain patterns by using electrical brain activity data
About the competition
What happens inside the mind of someone in a coma, or someone who suffers from a seizure? This is what neurologists analyze using electroencephalography (EEG). It is commonly used to diagnose various neurological disorders such as epilepsy and helps to identify abnormalities in brain waves. This technology serves as a window into the brain’s inner workings. When the neurons in your brain communicate, they create small electrical signals that show up in a similar way as an audio recording. When someone suffers from a seizure, a big electrical storm occurs. These patterns are unusual and detectable through EEG. In this competition, we will develop an AI that can help a neurologist with analyzing the EEG data.
Harvard Medical School
The competition is hosted by Harvard Medical School, the graduate medical school of Harvard University. They are joined by the Sunstella Foundation, Persyst, Jazz Pharmaceuticals, and the Clinical Data Animation Center (CDAC), whose research aims to help people preserve and enhance brain health.
Relevance
Neurologist and expert in EEG, Robert van den Berg, taught us the importance of using AI to help analyze EEG data. He affirmed that using this will support decision-making. Additionally, it can serve as an early warning system when a patient is being monitored long-term. Essentially, it will reduce workload and result in better care. He confirmed that research has been done on using AI for EEG analyses. However, the use of AI in health care still lacks trust which slows down the process of implementation. Many patients worry that algorithms can’t meet their medical needs well enough. To change this, we need AI’s that show expert-level accuracy or even outperform experts. By providing accuracy and reliability our models can gain trust for use in settings like healthcare.
Technical details
In the data provided for this competition, Medical expert annotators reviewed 50-second-long EEG samples combined with extracted frequency spectrograms. They focused on spotting harmful brain activity within each sample. Interestingly, even these experts found it tough to agree on the right labels when examining the data. For example, it is a common occurrence that half of the experts vote for a Seizure, and the other half vote for Other harmful behaviour. Therefore, our models should match the expert’s uncertainty and label the data identically. That will result in the highest score. The team researches and develops state-of-the-art recurrent neural networks which can pick up on the rhythmic patterns of harmful events in EEG data.
UN Sustainable Development Goals
This competition contributes to the UN Sustainable Development Goal #3: Good Health and Well-being. Our AI model will help neurologists in treating their patients faster and more accurately. It will also help justify the research and development in using AI in the healthcare field.
