An FCN-LSTM model for neurological status detection from non-invasive multivariate sensor data

• Published in: Neural computing & applications Vol. 36; no. 1; pp. 77 - 93
• Main Authors: Masood, Sarfaraz, Khan, Rafiuddin, Abd El-Latif, Ahmed A., Ahmad, Musheer
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2024; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data acquisition; Data Mining and Knowledge Discovery; Datasets; Image Processing and Computer Vision; Multivariate analysis; Physiology; Probability and Statistics in Computer Science; S.I: Improving Healthcare outcomes using Multimedia Big Data Analytics; Special Issue on Improving Healthcare outcomes using Multimedia Big Data Analytics; Tracking systems; Wearable computers; Wearable technology; Wearable device; Recurrent neural network; Fully convolutional-long short-term memory (FCN-LSTM) network; Physiological signals; Neurological status
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07117-4

A continuous monitoring of neurological status can help in reporting the physical and mental health of a person. This can be capitalized for building a healthcare tracking system using a wearable device and a handheld mobile device. In this paper, we have used the non-EEG physiological biosignals dataset which gives practicability among subjects for acquiring data easily from wearable device sensors linearly and comfortably rather than the way of putting the subjects in a cumbersome setup laboratory. This paper proposes a custom fully convolutional-LSTM (FCN-LSTM) network to identify the neurological status of a subject using multivariate time series physiological sensor data. The proposed architecture uses parallel stacks of the convolutional layers and LSTM cells. This combination of different network types is significant for the selected problem as the fully convolutional section of the model extracts the local spatial features in the data, while the LSTM network handles the high-level features and temporal dependencies. The proposed FCN-LSTM model yielded a high accuracy of 98.6% and a precision of 98% on the non-EEG dataset from UT-Dallas. The average accuracy of single-subject results of the dataset using the proposed model was observed to be 99.26%. The results from the proposed model are significantly improved when compared with various state-of-the-art works on this problem. These results strongly suggest that this model, when put on a wearable device, can be effectively used to detect the neurological status or stress that the subject may be going through in real time.