A Novel Seizure Detection Method Based on the Feature Fusion of Multimodal Physiological Signals

Seizure detection is traditionally done using video/electroencephalography monitoring, but for out-of-hospital patients, this method is costly. In recent years, portable device to detect seizures gains attention. In this paper, multimodal signals collected by portable devices are studied, and a seizure detection algorithm is proposed based on adaptive multi-bit local differential ternary pattern (MLDTP). This algorithm is used for detecting seizure period and inter-seizure period. Traditional local binary pattern has certain limitations in describing one-dimensional time series signals. It can only describe two types of structures in signals: Rising structure and falling structure, making the signal patterns overly monotonous and not conducive to classification tasks. To address this issue, this paper introduces two additional structures, slowly rising structure and slowly falling structure, into the signal description using MLDTP method. This method constructs multi-bit neighboring relationships of the signals, and adaptively selects the optimal MLDTP parameters for different modalities using the Archimedes optimization algorithm (AOA). Additionally, this paper extensively discusses a multimodal signal fusion strategy, mapping features of different modal signals to the same feature space through the MLDTP algorithm to achieve information complementarity. Long-term recorded data from 18 patients were collected using the wearable device Biovital P1, with 13 cases from the Children's Hospital affiliated with Children's Hospital, Zhejiang University School of Medicine, and 5 cases from the fourth Affiliated Hospital of Anhui Medical University. The dataset underwent five-fold cross-validation, resulting in average accuracy, precision, sensitivity and F1 score of 96.81%, 98.55%, 95.24% and 96.87%, respectively.