mdh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Islam, Mir Riyanul, Doctoral StudentORCID iD iconorcid.org/0000-0003-0730-4405
Publications (2 of 2) Show all publications
Islam, M. R., Barua, S., Ahmed, M. U., Begum, S. & Flumeri, G. D. (2019). Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification. In: Communications in Computer and Information Science, Volume 1107: . Paper presented at The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy (pp. 121-135).
Open this publication in new window or tab >>Deep Learning for Automatic EEG Feature Extraction: An Application in Drivers' Mental Workload Classification
Show others...
2019 (English)In: Communications in Computer and Information Science, Volume 1107, 2019, p. 121-135Conference paper, Published paper (Refereed)
Abstract [en]

In the pursuit of reducing traffic accidents, drivers' mental workload (MWL) has been considered as one of the vital aspects. To measure MWL in different driving situations Electroencephalography (EEG) of the drivers has been studied intensely. However, in the literature, mostly, manual analytic methods are applied to extract and select features from the EEG signals to quantify drivers' MWL. Nevertheless, the amount of time and effort required to perform prevailing feature extraction techniques leverage the need for automated feature extraction techniques. This work investigates deep learning (DL) algorithm to extract and select features from the EEG signals during naturalistic driving situations. Here, to compare the DL based and traditional feature extraction techniques, a number of classifiers have been deployed. Results have shown that the highest value of area under the curve of the receiver operating characteristic (AUC-ROC) is 0.94, achieved using the features extracted by CNN-AE and support vector machine. Whereas, using the features extracted by the traditional method, the highest value of AUC-ROC is 0.78 with the multi-layer perceptron. Thus, the outcome of this study shows that the automatic feature extraction techniques based on CNN-AE can outperform the manual techniques in terms of classification accuracy.

Keywords
Autoencoder, Convolutional Neural Networks, Electroencephalography, Feature Extraction, Mental Workload
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45059 (URN)10.1007/978-3-030-32423-0_8 (DOI)2-s2.0-85075680380 (Scopus ID)9783030324223 (ISBN)
Conference
The 3rd International Symposium on Human Mental Workload: Models and Applications H-WORKLOAD 2019, 14 Nov 2019, Rome, Italy
Projects
BRAINSAFEDRIVE: A Technology to detect Mental States During Drive for improving the Safety of the road
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-12-16Bibliographically approved
Islam, M. R., Barua, S., Begum, S. & Ahmed, M. U. (2019). Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology. In: Workshop on CBR in the Health Science WS-HealthCBR: . Paper presented at Workshop on CBR in the Health Science WS-HealthCBR, 09 Sep 2019, Otzenhausen, Germany.
Open this publication in new window or tab >>Hypothyroid Disease Diagnosis with Causal Explanation using Case-based Reasoning and Domain-specific Ontology
2019 (English)In: Workshop on CBR in the Health Science WS-HealthCBR, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Explainability of intelligent systems in health-care domain is still in its initial state. Recently, more efforts are made to leverage machine learning in solving causal inference problems of disease diagnosis, prediction and treatments. This research work presents an ontology based causal inference model for hypothyroid disease diagnosis using case-based reasoning. The effectiveness of the proposed method is demonstrated with an example from hypothyroid disease domain. Here, the domain knowledge is mapped into an ontology and causal inference is performed based on this domain-specific ontology. The goal is to incorporate this causal inference model in traditional case-based reasoning cycle enabling explanation for each solved problem. Finally, a mechanism is defined to deduce explanation for a solution to a problem case from the combined causal statements of similar cases. The initial result shows that case-based reasoning can retrieve relevant cases with 95% accuracy.

Keywords
Case-based Reasoning, Causal Model, Explainability, Explainable Artificial Intelligence, Hypothyroid Diagnosis, Ontology
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45058 (URN)
Conference
Workshop on CBR in the Health Science WS-HealthCBR, 09 Sep 2019, Otzenhausen, Germany
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0730-4405

Search in DiVA

Show all publications