List of Titles

K-complex detection using a hybrid-synergic machine learning method

- Vu, Huy Quan, Li, Gang, Sukhorukova, Nadezda, Beliakov, Gleb, Liu, Shaowu, Philippe, Carole, Amiel, Hélène, Ugon, Adrien

Authors: Vu, Huy Quan , Li, Gang , Sukhorukova, Nadezda , Beliakov, Gleb , Liu, Shaowu , Philippe, Carole , Amiel, Hélène , Ugon, Adrien
Date: 2012
Type: Text , Journal article
Relation: IEEE Transactions on Systems, Man and Cybernetics Part C : Applications and Reviews Vol. 42, no. 6 (2012), p. 1478-1490
Full Text: false
Reviewed:
Description: Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection. © 1998-2012 IEEE.
Description: 2003010569

IoT-powered deep learning brain network for assisting quadriplegic people

- Vinoj, P., Jacob, Sunil, Menon, Varun, Balasubramanian, Venki, Piran, Md Jalil

Authors: Vinoj, P. , Jacob, Sunil , Menon, Varun , Balasubramanian, Venki , Piran, Md Jalil
Date: 2021
Type: Text , Journal article
Relation: Computers and Electrical Engineering Vol. 92, no. (2021), p.
Full Text: false
Reviewed:
Description: Brain-Computer Interface (BCI) systems have recently emerged as a prominent technology for assisting paralyzed people. Recovery from paralysis in most patients using the existing BCI-based assistive devices is hindered due to the lack of training and proper supervision. The system's continuous usage results in mental fatigue, owing to a higher user concentration required to execute the mental commands. Moreover, the false-positive rate and lack of constant control of the BCI systems result in user frustration. The proposed framework integrates BCI with a deep learning network in an efficient manner to reduce mental fatigue and frustration. The Deep learning Brain Network (DBN) recognizes the patient's intention for upper limb movement by a deep learning model based on the features extracted during training. DBN correlates and maps the different Electroencephalogram (EEG) patterns of healthy subjects with the identified pattern's upper limb movement. The stroke-affected muscles of the paralyzed are then activated using the obtained superior pattern. The implemented DBN consisting of four healthy subjects and a quadriplegic patient achieved 94% accuracy for various patient movement intentions. The results show that DBN is an excellent tool for providing rehabilitation, and it delivers sustained assistance, even in the absence of caregivers. © 2021

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