EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods

Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a complet...
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Autor*in:	Corsi, Leonardo [verfasserIn] Liuzzi, Piergiuseppe [verfasserIn] Ballanti, Sara [verfasserIn] Scarpino, Maenia [verfasserIn] Maiorelli, Antonio [verfasserIn] Sterpu, Raisa [verfasserIn] Macchi, Claudio [verfasserIn] Cecchi, Francesca [verfasserIn] Hakiki, Bahia [verfasserIn] Grippo, Antonello [verfasserIn] Lanatà, Antonio [verfasserIn] Carrozza, Maria Chiara [verfasserIn] Bocchi, Leonardo [verfasserIn] Mannini, Andrea [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry

Übergeordnetes Werk:	Enthalten in: Biomedical signal processing and control - Amsterdam [u.a.] : Elsevier, 2006, 79
Übergeordnetes Werk:	volume:79

DOI / URN:	10.1016/j.bspc.2022.104260

Katalog-ID:	ELV009709363

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100	1		\|a Corsi, Leonardo \|e verfasserin \|0 (orcid)0000-0003-2937-3858 \|4 aut
245	1	0	\|a EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
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520			\|a Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.
650		4	\|a Disorders of consciousness
650		4	\|a Electroencephalography
650		4	\|a Diagnostic models
650		4	\|a Machine learning
650		4	\|a qEEG
650		4	\|a Brain symmetry
700	1		\|a Liuzzi, Piergiuseppe \|e verfasserin \|0 (orcid)0000-0002-6067-474X \|4 aut
700	1		\|a Ballanti, Sara \|e verfasserin \|0 (orcid)0000-0002-0323-8036 \|4 aut
700	1		\|a Scarpino, Maenia \|e verfasserin \|4 aut
700	1		\|a Maiorelli, Antonio \|e verfasserin \|4 aut
700	1		\|a Sterpu, Raisa \|e verfasserin \|4 aut
700	1		\|a Macchi, Claudio \|e verfasserin \|4 aut
700	1		\|a Cecchi, Francesca \|e verfasserin \|4 aut
700	1		\|a Hakiki, Bahia \|e verfasserin \|0 (orcid)0000-0001-8540-7405 \|4 aut
700	1		\|a Grippo, Antonello \|e verfasserin \|4 aut
700	1		\|a Lanatà, Antonio \|e verfasserin \|0 (orcid)0000-0002-6540-5952 \|4 aut
700	1		\|a Carrozza, Maria Chiara \|e verfasserin \|4 aut
700	1		\|a Bocchi, Leonardo \|e verfasserin \|4 aut
700	1		\|a Mannini, Andrea \|e verfasserin \|0 (orcid)0000-0003-0760-052X \|4 aut
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spelling	10.1016/j.bspc.2022.104260 doi (DE-627)ELV009709363 (ELSEVIER)S1746-8094(22)00714-5 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Corsi, Leonardo verfasserin (orcid)0000-0003-2937-3858 aut EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff. Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry Liuzzi, Piergiuseppe verfasserin (orcid)0000-0002-6067-474X aut Ballanti, Sara verfasserin (orcid)0000-0002-0323-8036 aut Scarpino, Maenia verfasserin aut Maiorelli, Antonio verfasserin aut Sterpu, Raisa verfasserin aut Macchi, Claudio verfasserin aut Cecchi, Francesca verfasserin aut Hakiki, Bahia verfasserin (orcid)0000-0001-8540-7405 aut Grippo, Antonello verfasserin aut Lanatà, Antonio verfasserin (orcid)0000-0002-6540-5952 aut Carrozza, Maria Chiara verfasserin aut Bocchi, Leonardo verfasserin aut Mannini, Andrea verfasserin (orcid)0000-0003-0760-052X aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 79 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:79 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 79
allfields_unstemmed	10.1016/j.bspc.2022.104260 doi (DE-627)ELV009709363 (ELSEVIER)S1746-8094(22)00714-5 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Corsi, Leonardo verfasserin (orcid)0000-0003-2937-3858 aut EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff. Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry Liuzzi, Piergiuseppe verfasserin (orcid)0000-0002-6067-474X aut Ballanti, Sara verfasserin (orcid)0000-0002-0323-8036 aut Scarpino, Maenia verfasserin aut Maiorelli, Antonio verfasserin aut Sterpu, Raisa verfasserin aut Macchi, Claudio verfasserin aut Cecchi, Francesca verfasserin aut Hakiki, Bahia verfasserin (orcid)0000-0001-8540-7405 aut Grippo, Antonello verfasserin aut Lanatà, Antonio verfasserin (orcid)0000-0002-6540-5952 aut Carrozza, Maria Chiara verfasserin aut Bocchi, Leonardo verfasserin aut Mannini, Andrea verfasserin (orcid)0000-0003-0760-052X aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 79 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:79 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 79
allfieldsGer	10.1016/j.bspc.2022.104260 doi (DE-627)ELV009709363 (ELSEVIER)S1746-8094(22)00714-5 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Corsi, Leonardo verfasserin (orcid)0000-0003-2937-3858 aut EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff. Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry Liuzzi, Piergiuseppe verfasserin (orcid)0000-0002-6067-474X aut Ballanti, Sara verfasserin (orcid)0000-0002-0323-8036 aut Scarpino, Maenia verfasserin aut Maiorelli, Antonio verfasserin aut Sterpu, Raisa verfasserin aut Macchi, Claudio verfasserin aut Cecchi, Francesca verfasserin aut Hakiki, Bahia verfasserin (orcid)0000-0001-8540-7405 aut Grippo, Antonello verfasserin aut Lanatà, Antonio verfasserin (orcid)0000-0002-6540-5952 aut Carrozza, Maria Chiara verfasserin aut Bocchi, Leonardo verfasserin aut Mannini, Andrea verfasserin (orcid)0000-0003-0760-052X aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 79 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:79 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 79
allfieldsSound	10.1016/j.bspc.2022.104260 doi (DE-627)ELV009709363 (ELSEVIER)S1746-8094(22)00714-5 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Corsi, Leonardo verfasserin (orcid)0000-0003-2937-3858 aut EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff. Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry Liuzzi, Piergiuseppe verfasserin (orcid)0000-0002-6067-474X aut Ballanti, Sara verfasserin (orcid)0000-0002-0323-8036 aut Scarpino, Maenia verfasserin aut Maiorelli, Antonio verfasserin aut Sterpu, Raisa verfasserin aut Macchi, Claudio verfasserin aut Cecchi, Francesca verfasserin aut Hakiki, Bahia verfasserin (orcid)0000-0001-8540-7405 aut Grippo, Antonello verfasserin aut Lanatà, Antonio verfasserin (orcid)0000-0002-6540-5952 aut Carrozza, Maria Chiara verfasserin aut Bocchi, Leonardo verfasserin aut Mannini, Andrea verfasserin (orcid)0000-0003-0760-052X aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 79 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:79 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 79
language	English
source	Enthalten in Biomedical signal processing and control 79 volume:79
sourceStr	Enthalten in Biomedical signal processing and control 79 volume:79
format_phy_str_mv	Article
bklname	Medizintechnik Medizinische Mathematik medizinische Statistik
institution	findex.gbv.de
topic_facet	Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry
dewey-raw	610
isfreeaccess_bool	false
container_title	Biomedical signal processing and control
authorswithroles_txt_mv	Corsi, Leonardo @@aut@@ Liuzzi, Piergiuseppe @@aut@@ Ballanti, Sara @@aut@@ Scarpino, Maenia @@aut@@ Maiorelli, Antonio @@aut@@ Sterpu, Raisa @@aut@@ Macchi, Claudio @@aut@@ Cecchi, Francesca @@aut@@ Hakiki, Bahia @@aut@@ Grippo, Antonello @@aut@@ Lanatà, Antonio @@aut@@ Carrozza, Maria Chiara @@aut@@ Bocchi, Leonardo @@aut@@ Mannini, Andrea @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	515537861
dewey-sort	3610
id	ELV009709363
language_de	englisch
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author	Corsi, Leonardo
spellingShingle	Corsi, Leonardo ddc 610 bkl 44.09 bkl 44.32 misc Disorders of consciousness misc Electroencephalography misc Diagnostic models misc Machine learning misc qEEG misc Brain symmetry EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
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topic_title	610 VZ 44.09 bkl 44.32 bkl EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods Disorders of consciousness Electroencephalography Diagnostic models Machine learning qEEG Brain symmetry
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title	EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
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title_full	EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
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author_browse	Corsi, Leonardo Liuzzi, Piergiuseppe Ballanti, Sara Scarpino, Maenia Maiorelli, Antonio Sterpu, Raisa Macchi, Claudio Cecchi, Francesca Hakiki, Bahia Grippo, Antonello Lanatà, Antonio Carrozza, Maria Chiara Bocchi, Leonardo Mannini, Andrea
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title_sort	eeg asymmetry detection in patients with severe acquired brain injuries via machine learning methods
title_auth	EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
abstract	Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.
abstractGer	Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.
abstract_unstemmed	Lateral brain symmetry indexes, detected by electroencephalography (EEG), are markers of rehabilitative recovery widely used in patients with severe acquired brain injury (sABI). In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). The application of the proposed approach to our sample size supports the generalizability of our model and its translation to clinical practice. The algorithm, heading to automatic EEG analysis, has the potential to speed up analysis of long recordings and, to improve quality of care in settings lacking skilled staff.
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title_short	EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods
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author2	Liuzzi, Piergiuseppe Ballanti, Sara Scarpino, Maenia Maiorelli, Antonio Sterpu, Raisa Macchi, Claudio Cecchi, Francesca Hakiki, Bahia Grippo, Antonello Lanatà, Antonio Carrozza, Maria Chiara Bocchi, Leonardo Mannini, Andrea
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up_date	2024-07-07T00:05:14.720Z
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In this study, Machine Learning algorithms were cross-validated to detect consistent asymmetries, starting from a completely automated features extraction pipeline in the EEG recordings of 54 patients with sABI, classified by two expert neurophysiologists. Raw data were filtered and segmented in two-seconds non-overlapping epochs. Low data quality in frontal electrodes caused up to 40% of epochs rejection, whilst central and posterior electrodes contributed with the greatest number of artefacts-free epochs. Out of more than 3000 extracted features, ∼300 significantly differentiated symmetric and asymmetric EEG recordings, most of them extracted from pairs and lines of electrodes. The best performing solution (nested-cross-validated and optimized Support Vector Machine classifier) detected asymmetry with a test accuracy of 85% (sensitivity 92%, specificity 80%). 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EEG asymmetry detection in patients with severe acquired brain injuries via machine learning methods

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