Healthcare
Case StudyGraphnet

Graphnet detects changes indicative of a seizure in epileptic patients using wearable tracker and machine learning and notifies the patient and carer via app

Graphnet''s wearable tracker is constantly monitoring vitals such as heart rate, skin conductance, gyroscope, and accelerometer as well as information related to mood, stress levels, alcohol intake, diet and medication compliance. The machine learning system can identify patterns that lead to high risk of seizures to notify patient and care provider in time.

Context

"Epilepsy is a condition that affects the brain and can cause repeated seizures. Around 600,000 people in the UK are diagnosed with epilepsy and patients often have to visit hospital many times for tests and consultant appointments to discuss and amend their treatment."

The Project

"Graphnet aims to improve the treatment and quality of lives of those with epilepsy by combining the latest in wearable technologies, integrated care records, machine learning and data analysis tools By analyzing user body functions such as heart rate, skin conductance, gyroscope, and accelerometer as well information related to mood, stress levels, alcohol intake, diet and medication compliance system can identify patterns and predict seizures and even lead to customized treatment options. The app captures data such as sleep patterns, exercise, heart rate, temperature and galvanic skin response."

Data

"Body functions such as heart rate, skin conductance, gyroscope, and accelerometer as well information related to mood, stress levels, alcohol intake, diet and medication compliance system can identify patterns and predict seizures and even lead to customized treatment options." The app captures data such as sleep patterns, exercise, heart rate, temperature and galvanic skin response.

Results

Improve quality of life of participants and reduced hospital admissions

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