Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism
Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation–inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) metho...
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| description | Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation–inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were
correlated
. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our ex |
| doi_str_mv | 10.1007/s10916-015-0275-6 |
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| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1730069086</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1709190139</sourcerecordid><originalsourceid>FETCH-LOGICAL-c438t-ec760cde2d9629a1a4f8abd42aecdb590e3b4016d84f11009e5cd709fafa6cb03</originalsourceid><addsrcrecordid>eNqNkU9rFDEYh4Modq1-AC8S8OKho2-Smfw5bkvXFQotWMFbyCSZ7SwzyZrMHOqnN8NWEaHUSwLJ8_4Sfg9Cbwl8JADiUyagCK-ANBVQ0VT8GVqRRrCKS_X9OVoBqWXVNEqeoFc57wFAcS5eohPKiWJK0hXab_uh9WmqtrMJO3ybTMhdTKOZ-hjwucne4XUww332GccOb27IWVnoGTbB4c1PfDl4O6Xog_WHOzPEXTIj_trvykzGfcDrwca7OPR5fI1edOXQv3nYT9G3zeXtxba6uv785WJ9VdmayanyVnCwzlOnOFWGmLqTpnU1Nd66tlHgWVsD4U7WHSk1KN9YJ0B1pjPctsBO0Ydj7iHFH7PPkx77bP0wmODjnDURDIArkPw_0FKwAsLU0yhXEpiAmhb0_T_oPs5pKWShBJNM1qJQ5EjZFHNOvtOH1I8m3WsCetGrj3p10asXvXr577uH5Lkdvfsz8dtnAegRyOUq7Hz66-lHU38BLhGucQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1697383847</pqid></control><display><type>article</type><title>Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Lin, Chin-Feng ; Su, Jiun-Yi ; Wang, Hao-Min</creator><creatorcontrib>Lin, Chin-Feng ; Su, Jiun-Yi ; Wang, Hao-Min</creatorcontrib><description>Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation–inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were
correlated
. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy
ratios
of the wave of an alcoholic observer to its
refereed
total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16
s
. For normal EEG signals, more than 1 % of the total energy is
distributed
in the δ wave, with a reaction time 0 to 4 s. We observed that the alcoholic subject reaction times were slower than those of the normal subjects, and the alcoholic subjects could have experienced a cognitive error. This phenomenon is due to the intoxicated central nervous systems of the alcoholic subjects.</description><identifier>ISSN: 0148-5598</identifier><identifier>EISSN: 1573-689X</identifier><identifier>DOI: 10.1007/s10916-015-0275-6</identifier><identifier>PMID: 26193982</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Alcoholism ; Alcoholism - physiopathology ; Algorithms ; Central nervous system ; Chronic Disease ; Correlation ; Education & Training ; Electroencephalography ; Health Informatics ; Health Sciences ; Humans ; Medicine ; Medicine & Public Health ; Nervous system ; Observers ; Pictures ; Reaction Time ; Signal Processing, Computer-Assisted ; Statistics for Life Sciences ; Transformations</subject><ispartof>Journal of medical systems, 2015-09, Vol.39 (9), p.83-8, Article 83</ispartof><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-ec760cde2d9629a1a4f8abd42aecdb590e3b4016d84f11009e5cd709fafa6cb03</citedby><cites>FETCH-LOGICAL-c438t-ec760cde2d9629a1a4f8abd42aecdb590e3b4016d84f11009e5cd709fafa6cb03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10916-015-0275-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10916-015-0275-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26193982$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lin, Chin-Feng</creatorcontrib><creatorcontrib>Su, Jiun-Yi</creatorcontrib><creatorcontrib>Wang, Hao-Min</creatorcontrib><title>Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism</title><title>Journal of medical systems</title><addtitle>J Med Syst</addtitle><addtitle>J Med Syst</addtitle><description>Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation–inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were
correlated
. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy
ratios
of the wave of an alcoholic observer to its
refereed
total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16
s
. For normal EEG signals, more than 1 % of the total energy is
distributed
in the δ wave, with a reaction time 0 to 4 s. We observed that the alcoholic subject reaction times were slower than those of the normal subjects, and the alcoholic subjects could have experienced a cognitive error. This phenomenon is due to the intoxicated central nervous systems of the alcoholic subjects.</description><subject>Alcoholism</subject><subject>Alcoholism - physiopathology</subject><subject>Algorithms</subject><subject>Central nervous system</subject><subject>Chronic Disease</subject><subject>Correlation</subject><subject>Education & Training</subject><subject>Electroencephalography</subject><subject>Health Informatics</subject><subject>Health Sciences</subject><subject>Humans</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Nervous system</subject><subject>Observers</subject><subject>Pictures</subject><subject>Reaction Time</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Statistics for Life Sciences</subject><subject>Transformations</subject><issn>0148-5598</issn><issn>1573-689X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNqNkU9rFDEYh4Modq1-AC8S8OKho2-Smfw5bkvXFQotWMFbyCSZ7SwzyZrMHOqnN8NWEaHUSwLJ8_4Sfg9Cbwl8JADiUyagCK-ANBVQ0VT8GVqRRrCKS_X9OVoBqWXVNEqeoFc57wFAcS5eohPKiWJK0hXab_uh9WmqtrMJO3ybTMhdTKOZ-hjwucne4XUww332GccOb27IWVnoGTbB4c1PfDl4O6Xog_WHOzPEXTIj_trvykzGfcDrwca7OPR5fI1edOXQv3nYT9G3zeXtxba6uv785WJ9VdmayanyVnCwzlOnOFWGmLqTpnU1Nd66tlHgWVsD4U7WHSk1KN9YJ0B1pjPctsBO0Ydj7iHFH7PPkx77bP0wmODjnDURDIArkPw_0FKwAsLU0yhXEpiAmhb0_T_oPs5pKWShBJNM1qJQ5EjZFHNOvtOH1I8m3WsCetGrj3p10asXvXr577uH5Lkdvfsz8dtnAegRyOUq7Hz66-lHU38BLhGucQ</recordid><startdate>20150901</startdate><enddate>20150901</enddate><creator>Lin, Chin-Feng</creator><creator>Su, Jiun-Yi</creator><creator>Wang, Hao-Min</creator><general>Springer US</general><general>Springer Nature 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Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism</title><author>Lin, Chin-Feng ; Su, Jiun-Yi ; Wang, Hao-Min</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-ec760cde2d9629a1a4f8abd42aecdb590e3b4016d84f11009e5cd709fafa6cb03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Alcoholism</topic><topic>Alcoholism - physiopathology</topic><topic>Algorithms</topic><topic>Central nervous system</topic><topic>Chronic Disease</topic><topic>Correlation</topic><topic>Education & Training</topic><topic>Electroencephalography</topic><topic>Health Informatics</topic><topic>Health Sciences</topic><topic>Humans</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Nervous system</topic><topic>Observers</topic><topic>Pictures</topic><topic>Reaction Time</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Statistics for Life Sciences</topic><topic>Transformations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Chin-Feng</creatorcontrib><creatorcontrib>Su, Jiun-Yi</creatorcontrib><creatorcontrib>Wang, Hao-Min</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered 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Hao-Min</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism</atitle><jtitle>Journal of medical systems</jtitle><stitle>J Med Syst</stitle><addtitle>J Med Syst</addtitle><date>2015-09-01</date><risdate>2015</risdate><volume>39</volume><issue>9</issue><spage>83</spage><epage>8</epage><pages>83-8</pages><artnum>83</artnum><issn>0148-5598</issn><eissn>1573-689X</eissn><abstract>Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation–inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were
correlated
. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy
ratios
of the wave of an alcoholic observer to its
refereed
total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16
s
. For normal EEG signals, more than 1 % of the total energy is
distributed
in the δ wave, with a reaction time 0 to 4 s. We observed that the alcoholic subject reaction times were slower than those of the normal subjects, and the alcoholic subjects could have experienced a cognitive error. This phenomenon is due to the intoxicated central nervous systems of the alcoholic subjects.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>26193982</pmid><doi>10.1007/s10916-015-0275-6</doi><tpages>8</tpages></addata></record> |
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| subjects | Alcoholism Alcoholism - physiopathology Algorithms Central nervous system Chronic Disease Correlation Education & Training Electroencephalography Health Informatics Health Sciences Humans Medicine Medicine & Public Health Nervous system Observers Pictures Reaction Time Signal Processing, Computer-Assisted Statistics for Life Sciences Transformations |
| title | Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism |
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