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Global Field Time-Frequency Representation-Based Discriminative Similarity Analysis of Passive Auditory ERPs for Diagnosis of Disorders of Consciousness (2024)

Wang, X., Yang, Y., Laforge, G., Chen, X., Norton, L., Owen, A. M., He, J., & Cong, F. (2024). Global Field Time-Frequency Representation-Based Discriminative Similarity Analysis of Passive Auditory ERPs for Diagnosis of Disorders of Consciousness. IEEE Transactions on Biomedical Engineering, 71(6), 1820-1830. https://doi.org/10.1109/TBME.2024.3353110

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Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajatWang, Xiaoyu; Yang, Yi; Laforge, Geoffrey; Chen, Xueling; Norton, Loretta; Owen, Adrian M.; He, Jianghong; Cong, Fengyu

Lehti tai sarjaIEEE Transactions on Biomedical Engineering






Lehden numero6

Artikkelin sivunumerot1820-1830

KustantajaInstitute of Electrical and Electronics Engineers (IEEE)

JulkaisumaaYhdysvallat (USA)

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Julkaisun avoin saatavuusEi avoin

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Behavioural diagnosis of patients with disorders of consciousness (DOC) is challenging and prone to inaccuracies. Consequently, there have been increased efforts to develop bedside assessment based on EEG and event-related potentials (ERPs) that are more sensitive to the neural factors supporting conscious awareness. However, individual detection of residual consciousness using these techniques is less established. Here, we hypothesize that the cross-state similarity (defined as the similarity between healthy and impaired conscious states) of passive brain responses to auditory stimuli can index the level of awareness in individual DOC patients. To this end, we introduce the global field time-frequency representation-based discriminative similarity analysis (GFTFR-DSA). This method quantifies the average cross-state similarity index between an individual patient and our constructed healthy templates using the GFTFR as an EEG feature. We demonstrate that the proposed GFTFR feature exhibits superior within-group consistency in 34 healthy controls over traditional EEG features such as temporal waveforms. Second, we observed the GFTFR-based similarity index was significantly higher in patients with a minimally conscious state (MCS, 40 patients) than those with unresponsive wakefulness syndrome (UWS, 54 patients), supporting our hypothesis. Finally, applying a linear support vector machine classifier for individual MCS/UWS classification, the model achieved a balanced accuracy and F1 score of 0.77. Overall, our findings indicate that combining discriminative and interpretable markers, along with automatic machine learning algorithms, is effective for the differential diagnosis in patients with DOC. Importantly, this approach can, in principle, be transferred into any ERP of interest to better inform DOC diagnoses.

YSO-asiasanattajunnan tasotajuttomuusdiagnostiikkaaivotutkimusEEGsignaalianalyysikoneoppiminen

Vapaat asiasanatdisorders of consciousness; EEG/ERPs; global field time-frequency representation; machine learning; mismatch negativity

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Viimeisin päivitys 2024-03-07 klo 20:05