Early Detection of Tonic-Clonic Seizure Through Multimodal Physiological Vital Signs Relations

We have developed a multimodal early tonic-clonic detection (METCSD) algorithm that combines the heart rate with a few physiological vital signs for early seizure detection. Seizures, originating from abnormal brain activity, affect vital signs, such as heart rate, blood pressure, body temperature, oxygen saturation levels, and so on. Traditionally, a seizure detection is done through clinical examinations, often utilizing electroencephalograms; the detection is further enhanced using heart rate monitoring, as the heart rate escalates suddenly before the onset of seizure. However, the sudden increase in heart rate may also be due to other factors, such as anxiety, stress, and intensive physical activity, potentially leading to false positive identifications. In this paper, we have addressed the issue by considering both the deviations of heart rate over time and against norm. In addition, we have considered two more physiological vital signs, such as blood pressure and body temperature, and derived a multi-model relation to improve the early detection accuracy while lowering false positives. Our developed METCSD algorithm consists of two phases: the heart rate assessment phase followed by the physiological vital signs evaluation phase. The algorithm monitors the heart rate variability over time, identifying sudden surges indicative of possible onset of seizures, thereby enabling early detection. With gradual rise in the heart rate, it proceeds to check the selected physiological vital signs in phase 2, distinguishing between its associations with seizures and those related to stress through continuous observation. We ran the algorithm through a dataset generated from medical consultations, demonstrating an early detection of the onset of seizures with 80% (approximately) reduced false positives compared to seizure detection relying solely on heart rate variation.