Seizure-related injuries in adults: A prospective case-controlled study on risk factors, seizure severity and quality of life

ECG-Based Semi-Supervised Anomaly Detection for Early Detection and Monitoring of Epileptic Seizures

Epilepsy is one of the most common brain diseases, characterized by frequent recurrent seizures or "ictal" states. A patient experiences uncontrollable muscular contractions, inducing loss of mobility and balance, which may result in injury or even death during these ictal states. Extensive investigation is vital to establish a systematic approach for predicting and informing patients about oncoming seizures ahead of time. Most methodologies developed are focused on the detection of abnormalities using mostly electroencephalogram (EEG) recordings. In this regard, research has indicated that certain pre-ictal alterations in the Autonomic Nervous System (ANS) can be detected in patient electrocardiogram (ECG) signals. The latter could potentially provide the basis for a robust seizure prediction approach. The recently proposed ECG-based seizure warning systems utilize machine learning models to classify a patient's condition. Such approaches require the incorporation of large, diverse, and thoroughly annotated ECG datasets, limiting their application potential. In this work, we investigate anomaly detection models in a patient-specific context with low supervision requirements. Specifically, we consider One-Class SVM (OCSVM), Minimum Covariance Determinant (MCD) Estimator, and Local Outlier Factor (LOF) models to quantify the novelty or abnormality of pre-ictal short-term (2-3 min) Heart Rate Variability (HRV) features of patients, trained on a reference interval considered to contain stable heart rate as the only form of supervision. Our models are evaluated against labels that were either hand-picked or automatically generated (weak labels) by a two-phase clustering procedure for samples of the "Post-Ictal Heart Rate Oscillations in Partial Epilepsy" (PIHROPE) dataset recorded by the Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, achieving detection in 9 out of 10 cases, with average AUCs of over 93% across all models and warning times ranging from 6 to 30 min prior to seizure. The proposed anomaly detection and monitoring approach can potentially pave the way for early detection and warning of seizure incidents based on body sensor inputs.

Quality of life of children with residual seizures after epileptic resection surgery

Objective

Epilepsy dramatically affects the quality of life (QoL) of children, and resection surgery can improve their QoL by reducing seizures or completely controlling them. Children who have postoperative seizures tend to show a poorer QoL. The aim of the present study was to investigate the QoL of children with seizures after resection surgery and its influencing factors.

Methods

In the present study, we retrospectively reviewed 151 consecutive children who underwent resection surgery. We then divided them into two groups, seizure and seizure-free groups, according to the seizure outcomes 1 year after surgery. Variables were categorized into a number of factor types such as preoperative factors, surgery-related factors, postoperative factors, and family factors. QoL and seizure outcomes more than 3 years after surgery were assessed according to the ILAE seizure outcome classification and the CHEQOL-25 scale.

Results

Forty-three (28.5%) of the 151 children had seizures 1 year after surgery, and two children died during the follow-up period. The mean CHEQOL-25 scale for children with seizures was 63.5 ± 18.2, and 20 (48.8%) patients had poor QoL. Surgery-related factors, such as surgical complications and surgical sequelae, were not statistically associated with QoL. Preoperative language development retardation or language dysfunction [odds ratio (OR) = 29.3, P = 0.012) and postoperative ILAE seizure outcome classification (OR = 1.9, P = 0.045)] were significantly associated with QoL.

Significance

Children with seizures after resection surgery had a relatively poor QoL. Surgery-related factors, such as surgical complications and surgical sequelae, cannot predict the QoL. Preoperative language development retardation or language dysfunction and postoperative ILAE seizure outcome classification were independent predictors of the quality of life (QoL). For children who could not achieve the expected freedom from seizure after surgery, a lower ILAE grade (ILAE 1-3) is also an acceptable outcome since it predicts a higher QoL.