Heart rate variability predicts epileptic seizure
News Apr 01, 2016
The neurological disorder epilepsy affects 1% of the global population and causes seizures of many different types. Recent research from Japan has found that epileptic seizures can be more easily predicted by using an electrocardiogram to measure fluctuations in the heart rate than by measuring brain activity, because the monitoring device is easier to wear. By making more accurate predictions, it is possible to prevent injury or accident that may result from an epileptic seizure. This is a significant contribution toward the realization of a society where epileptic patients can live without worrying about sustaining injury from an unexpected seizure. This finding comes from the combined research of Kumamoto University, Kyoto University and Tokyo Medical and Dental University.
Seventy percent of all epileptic patients are able to go through their daily lives without any problems due to the suppression of seizures through anti-epileptic drugs. However, some patients have drug resistant epilepsy where their seizures cannot be controlled by medication, and they live in a constant state of fear that an epileptic seizure may occur at any moment. For this reason, development of a method to predict epileptic seizures has been strongly desired.
Previous attempts at predicting epileptic seizures by heart rate did not have very high accuracy. It was difficult to determine the difference between the normal heart rate and the heart rate just before the onset of an attack. Additionally, differences among individuals was large, and there were many false positives. Practical application was therefore considered to be difficult.
"We analyzed heart rate fluctuations in the electrocardiographic data of 14 patients who had been hospitalized for long-term EEG video monitoring using a novel technique," said Dr. Toshitaka Yamakawa, Assistant Professor at Kumamoto University.
The researchers used a multivariate statistical process control (MSPC) to analyze the heart rate variability. The results produced accurate predictions (91%) for epileptic seizures. Furthermore, predictions could be made an average of 8 minutes before seizure onset. The difference between normal and preictal (before-seizure) heart rates was made very clear, and there were few false-positives (0.7 times/hour). These result shows that it is possible to make accurate predictions of epileptic seizures.
"The next step is to develop a wearable seizure prediction device," said Dr. Yamakawa. "With that kind of device, patients would be able to ensure their safety before a seizure occurs and since the envisioned device would be attached to the chest, where it's invisible externally, they would be able to have normal daily lives while wearing it. They wouldn't need to be afraid of sustaining injury due to an unexpected seizure."
Clinical studies of the wearable epileptic seizure prediction devices began this January in some Japanese medical institutions.
This research was posted in the medical engineering journal, IEEE Transactions on Biomedical Engineering and was introduced in the Editors' Choice section of Science Translational Medicine.
Note: Material may have been edited for length and content. For further information, please contact the cited source.
Fujiwara K et al. Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features. IEEE Transactions on Biomedical Engineering, Published Online December 24 2015. doi: 10.1109/TBME.2015.2512276
Researchers Find a Way to Separate Side Effects of Opioid Drugs Reducing RiskNews
Scientists have discovered a way to separate these two effects -- pain relief and breathing, opening a window of opportunity to make effective pain medications without the risk of respiratory failure.READ MORE
Biological Mechanism of a Leading Cause of Childhood Blindness RevealedNews
Scientists have revealed the pathology of cells and structures stricken by optic nerve hypoplasia, a leading cause of childhood blindness in developed nations.READ MORE
Machine Learning: Helping Determine How a Drug Affects the BrainNews
Machine learning could improve our ability to determine whether a new drug works in the brain, potentially enabling researchers to detect drug effects that would be missed entirely by conventional statistical tests, finds a new UCL study published today in Brain.READ MORE