Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital-based validation study.
Jesper JeppesenKatia LinHiago Murilo MeloJonatas PaveiJefferson Luiz Brum MarquesSándor BeniczkyRoger WalzPublished in: Epileptic disorders : international epilepsy journal with videotape (2024)
The study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.
Keyphrases
- machine learning
- heart rate variability
- end stage renal disease
- ejection fraction
- newly diagnosed
- temporal lobe epilepsy
- heart rate
- chronic kidney disease
- peritoneal dialysis
- palliative care
- label free
- loop mediated isothermal amplification
- patient reported outcomes
- real time pcr
- blood pressure
- artificial intelligence
- patient reported
- atrial fibrillation
- electronic health record