ML in Healthcare: Revolutionary to Alert and Predict Epilepsy-Seizure Patterns

ML in Healthcare: Revolutionary to Alert and Predict Epilepsy-Seizure Patterns

Machine learning helps to predict wearable device seizures. Patients can prepare for a case and minimize anxiety and side effects.

FREMONT, CA: Epilepsy surveillance is of significant concern to the medical community because of the sometimes debilitating nature of these seizures. Watch devices are accelerometer wristwatches and GPS in some cases. Such watch devices can sense repeated motions and warn someone by text, sound, or email from a smartphone. Some designs may also detect the place of the person by tracking GPS. Wearable technology is now widely used to monitor the activity of people, cycles of sleep-wake, and even heart rate. Those of us who care for individuals with epilepsy always like to look for ways to assist our patients by using technology.

Basic technology includes sensors that detect motion in easy pedometers. These motion detectors have become lower and compact in the advancement of this technology. Almost every smartphone can detect movement. Also, to boost the sensitivity, autonomous devices are produced that can be worn around the wrist or arm. It is then possible to connect any of these appliances to cellular or Wi-Fi technology. This can improve security and independence for individuals living with epilepsy.

Imagine whether a person wearing one of these devices has a widespread tonic-clonic seizure. The device can sense the wrist movement of the person. This move can then activate the device to signal a loved one or even call for assistance from emergency medical services. There is technology for transmitting the patient's particular place so that aid can be delivered rapidly. These devices can also maintain a record of the number of seizures and the times they happen, which can assist the doctor make adjustments to medicines.

More advanced modalities of sensing and AI are needed to combat chronic diseases like epilepsy. Recent advances in decreasing ML algorithms computational intensity have resulted in innovative techniques. Over time, wearables for biometric monitoring have gained popularity. Even component companies make health wearables as the appropriate algorithms for interpreting sensor information progress. The promise of a wearable device to monitor such seizures could extend to anticipating the occurrence of such seizures.