Background: Two clinical scoring systems, the status epilepticus severity score (STESS) and the epidemiology-based mortality score in status epilepticus (EMSE), are used to predict mortality in patients with status epilepticus (SE). The aim of this study was to compare the outcome-prediction function of the two scoring systems regarding in-hospital mortality using a multicenter large cohort of adult patients with SE. Moreover, we studied the potential role of these two scoring systems in predicting the functional outcome in patients with SE. Methods: The SE cohort consisted of patients from the epilepsy centers of eight academic tertiary medical centers in South Korea. The clinical and electroencephalography data for all adult patients with SE from January 2013 to December 2014 were derived from a prospective SE database. The primary outcome variable was defined as in-hospital death. The secondary outcome variable was defined as a poor functional outcome, i.e., a score of 1–3 on the Glasgow Outcome Scale, at discharge. Results: Among the 120 non-hypoxic patients with SE recruited into the study, 16 (13.3 %) died in the hospital and 64 (53.3 %) were discharged with a poor functional outcome. The receiver-operating characteristic (ROC) curve for prediction of in-hospital death based on the STESS had an area under the curve of 0.673 with an optimal cutoff value for discrimination (best match for both sensitivity (0.56) and specificity (0.70)) that was ≥4 points. The two combinations of elements of the EMSE system (EMSE-ALDEg and EMSE-ECLEg) predicted not only in-hospital mortality with the best match for sensitivity (more than 0.6) and specificity (more than 0.6), but also a poor functional outcome with the best match for both sensitivity (>0.7) and specificity (>0.6). STESS did not predict a poor functional outcome (area under the ROC, 0.581; P = 0.23). Conclusion: Although the EMSE is a clinical scoring system that focuses on individual mortality, we did not find differences between the EMSE and STESS in the prediction of in-hospital death. The EMSE was useful in predicting poor functional outcome, as it was significantly better than STESS.

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohen's kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohen's kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

This paper proposes a robust method to screen patients with sleep apnea syndrome (SAS) using a single-lead electrocardiogram (ECG). This method consists of minute-by-minute abnormal breathing detection and apnea-hypopnea index (AHI) estimation. Heartbeat interval and ECG-derived respiration (EDR) are calculated using the single-lead ECG and used to train the models, including ResNet18, ResNet34, and ResNet50. The proposed method, using data from 1232 subjects, was developed with two open datasets and experimental data and evaluated using two additional open datasets and data acquired from an abdomen-attached wearable device (in total, data from 189 subjects). ResNet18 showed the best results, having an average Cohen's kappa coefficient of 0.57, in the abnormal breathing detection. Moreover, SAS patient classification, with 15 as the AHI threshold, yielded an average Cohen's kappa coefficient of 0.71. The results of patient classification were biased toward data from the wearable patch-type device, which may be influenced by different ECG waveforms. The proposed method is tuned with a sample of the data from the device, and the performance result of Cohen's kappa increased from 0.54 to 0.91 for SAS patient classification. Our method, proposed in this paper, achieved equivalent performance results with data recorded using an abdomen-attached wearable device and two open datasets used in previous studies, although the method had not used those data during model training. The proposed method could reduce the development costs of commercial software, as it was developed using open datasets, has robust performance throughout all datasets.

Kim H, et al. J Clin Neurol. 2020 Oct;16(4):547-555. https://doi.org/10.3988/jcn.2020.16.4.547

Seo JG, et al. J Clin Neurol. 2020 Oct;16(4):556-561. https://doi.org/10.3988/jcn.2020.16.4.556

Study Objectives: Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies and eventually phenoconverts to overt neurodegenerative diseases including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). Associations of baseline resting-state electroencephalography (EEG) with phenoconversion have been reported. In this study, we aimed to develop machine learning models to predict phenoconversion time and subtype using baseline EEG features in patients with iRBD. Methods: At baseline, resting-state EEG and neurological assessments were performed on patients with iRBD. Calculated EEG features included spectral power, weighted phase lag index, and Shannon entropy. Three models were used for survival prediction, and four models were used for α-synucleinopathy subtype prediction. The models were externally validated using data from a different institution. Results: A total of 236 iRBD patients were followed up for up to 8 years (mean 3.5 years), and 31 patients converted to α-synucleinopathies (16 PD, 9 DLB, 6 MSA). The best model for survival prediction was the random survival forest model with an integrated Brier score of 0.114 and a concordance index of 0.775. The K-nearest neighbor model was the best model for subtype prediction with an area under the receiver operating characteristic curve of 0.901. Slowing of the EEG was an important feature for both models. Conclusions: Machine learning models using baseline EEG features can be used to predict phenoconversion time and its subtype in patients with iRBD. Further research including large sample data from many countries is needed to make a more robust model.