Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies.

A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.

Gene and protein expression profiles of JAK-STAT signalling pathway in the developing brain of the Ts1Cje down syndrome mouse model.

Aims: The JAK-STAT signalling pathway is one of the key regulators of pro-gliogenesis process during brain development. Down syndrome (DS) individuals, as well as DS mouse models, exhibit an increased number of astrocytes, suggesting an imbalance of neurogenic-to-gliogenic shift attributed to dysregulated JAK-STAT signalling pathway. The gene and protein expression profiles of JAK-STAT pathway members have not been characterised in the DS models. Therefore, we aimed to profile the expression of Jak1, Jak2, Stat1, Stat3 and Stat6 at different stages of brain development in the Ts1Cje mouse model of DS. Methods: Whole brain samples from Ts1Cje and wild-type mice at embryonic day (E)10.5, E15, postnatal day (P)1.5; and embryonic cortex-derived neurospheres were collected for gene and protein expression analysis. Gene expression profiles of three brain regions (cerebral cortex, cerebellum and hippocampus) from Ts1Cje and wild-type mice across four time-points (P1.5, P15, P30 and P84) were also analysed. Results: In the developing mouse brain, none of the Jak/Stat genes were differentially expressed in the Ts1Cje model compared to wild-type mice. However, Western blot analyses indicated that phosphorylated (p)-Jak2, p-Stat3 and p-Stat6 were downregulated in the Ts1Cje model. During the postnatal brain development, Jak/Stat genes showed complex expression patterns, as most of the members were downregulated at different selected time-points. Notably, embryonic cortex-derived neurospheres from Ts1Cje mouse brain expressed lower Stat3 and Stat6 protein compared to the wild-type group. Conclusion: The comprehensive expression profiling of Jak/Stat candidates provides insights on the potential role of the JAK-STAT signalling pathway during abnormal development of the Ts1Cje mouse brains.