METHODS: The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network.
RESULTS: Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset.
CONCLUSIONS: Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.
METHODS: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).
RESULTS: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.