Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach
Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. Ho...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Biomedicines |
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| Online Access: | https://www.mdpi.com/2227-9059/10/8/1897 |
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| author | Francesco Di Gregorio Fabio La Porta Valeria Petrone Simone Battaglia Silvia Orlandi Giuseppe Ippolito Vincenzo Romei Roberto Piperno Giada Lullini |
| author_facet | Francesco Di Gregorio Fabio La Porta Valeria Petrone Simone Battaglia Silvia Orlandi Giuseppe Ippolito Vincenzo Romei Roberto Piperno Giada Lullini |
| author_sort | Francesco Di Gregorio |
| collection | DOAJ |
| description | Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC. |
| first_indexed | 2024-03-09T10:00:59Z |
| format | Article |
| id | doaj.art-f8b90c88ea9945b7b0e3dbce733d4a4d |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-09T10:00:59Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-f8b90c88ea9945b7b0e3dbce733d4a4d2023-12-01T23:27:45ZengMDPI AGBiomedicines2227-90592022-08-01108189710.3390/biomedicines10081897Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning ApproachFrancesco Di Gregorio0Fabio La Porta1Valeria Petrone2Simone Battaglia3Silvia Orlandi4Giuseppe Ippolito5Vincenzo Romei6Roberto Piperno7Giada Lullini8UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40133 Bologna, ItalyIRCCS Istituto delle Scienze Neurologiche di BolognaIRCCS Istituto delle Scienze Neurologiche di BolognaCentro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, ItalyDepartment of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Viale Risorgimento, 2, 40136 Bologna, ItalyCentro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, ItalyCentro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, Campus di Cesena, 47521 Cesena, ItalyIRCCS Istituto delle Scienze Neurologiche di BolognaIRCCS Istituto delle Scienze Neurologiche di BolognaAccurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.https://www.mdpi.com/2227-9059/10/8/1897disorders of consciousnesstraumatic brain injuryelectroencephalographybrain plasticity and connectivitypost-anoxic comasevere acquired brain injury |
| spellingShingle | Francesco Di Gregorio Fabio La Porta Valeria Petrone Simone Battaglia Silvia Orlandi Giuseppe Ippolito Vincenzo Romei Roberto Piperno Giada Lullini Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach Biomedicines disorders of consciousness traumatic brain injury electroencephalography brain plasticity and connectivity post-anoxic coma severe acquired brain injury |
| title | Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach |
| title_full | Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach |
| title_fullStr | Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach |
| title_full_unstemmed | Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach |
| title_short | Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach |
| title_sort | accuracy of eeg biomarkers in the detection of clinical outcome in disorders of consciousness after severe acquired brain injury preliminary results of a pilot study using a machine learning approach |
| topic | disorders of consciousness traumatic brain injury electroencephalography brain plasticity and connectivity post-anoxic coma severe acquired brain injury |
| url | https://www.mdpi.com/2227-9059/10/8/1897 |
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