A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disor...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Khalifa, Yassin [verfasserIn] Mandic, Danilo [verfasserIn] Sejdić, Ervin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning

Übergeordnetes Werk:	Enthalten in: Information fusion - Amsterdam [u.a.] : Elsevier Science, 2000, 69, Seite 52-72
Übergeordnetes Werk:	volume:69 ; pages:52-72

DOI / URN:	10.1016/j.inffus.2020.11.008

Katalog-ID:	ELV005351812

Internformat


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100	1		\|a Khalifa, Yassin \|e verfasserin \|0 (orcid)0000-0001-8017-5959 \|4 aut
245	1	0	\|a A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
264		1	\|c 2020
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337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.
650		4	\|a Event detection
650		4	\|a Hidden Markov models
650		4	\|a Recurrent Neural Networks
650		4	\|a Deep learning
650		4	\|a Biomedical signal processing
650		4	\|a Transfer learning
700	1		\|a Mandic, Danilo \|e verfasserin \|0 (orcid)0000-0001-8432-3963 \|4 aut
700	1		\|a Sejdić, Ervin \|e verfasserin \|0 (orcid)0000-0003-4987-8298 \|4 aut
773	0	8	\|i Enthalten in \|t Information fusion \|d Amsterdam [u.a.] : Elsevier Science, 2000 \|g 69, Seite 52-72 \|h Online-Ressource \|w (DE-627)320648133 \|w (DE-600)2025632-2 \|w (DE-576)259271926 \|x 1566-2535 \|7 nnns
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz
936	b	k	\|a 54.74 \|j Maschinelles Sehen
936	b	k	\|a 53.73 \|j Nachrichtenübertragung
951			\|a AR
952			\|d 69 \|h 52-72

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matchkey_str	article:15662535:2020----::rveohdemromdladeurnnuantokfrvndtcinnlcl
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bklnumber	54.72 54.74 53.73
publishDate	2020
allfields	10.1016/j.inffus.2020.11.008 doi (DE-627)ELV005351812 (ELSEVIER)S1566-2535(20)30414-0 DE-627 ger DE-627 rda eng 620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl Khalifa, Yassin verfasserin (orcid)0000-0001-8017-5959 aut A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning Mandic, Danilo verfasserin (orcid)0000-0001-8432-3963 aut Sejdić, Ervin verfasserin (orcid)0000-0003-4987-8298 aut Enthalten in Information fusion Amsterdam [u.a.] : Elsevier Science, 2000 69, Seite 52-72 Online-Ressource (DE-627)320648133 (DE-600)2025632-2 (DE-576)259271926 1566-2535 nnns volume:69 pages:52-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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 54.72 Künstliche Intelligenz 54.74 Maschinelles Sehen 53.73 Nachrichtenübertragung AR 69 52-72
spelling	10.1016/j.inffus.2020.11.008 doi (DE-627)ELV005351812 (ELSEVIER)S1566-2535(20)30414-0 DE-627 ger DE-627 rda eng 620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl Khalifa, Yassin verfasserin (orcid)0000-0001-8017-5959 aut A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning Mandic, Danilo verfasserin (orcid)0000-0001-8432-3963 aut Sejdić, Ervin verfasserin (orcid)0000-0003-4987-8298 aut Enthalten in Information fusion Amsterdam [u.a.] : Elsevier Science, 2000 69, Seite 52-72 Online-Ressource (DE-627)320648133 (DE-600)2025632-2 (DE-576)259271926 1566-2535 nnns volume:69 pages:52-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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 54.72 Künstliche Intelligenz 54.74 Maschinelles Sehen 53.73 Nachrichtenübertragung AR 69 52-72
allfields_unstemmed	10.1016/j.inffus.2020.11.008 doi (DE-627)ELV005351812 (ELSEVIER)S1566-2535(20)30414-0 DE-627 ger DE-627 rda eng 620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl Khalifa, Yassin verfasserin (orcid)0000-0001-8017-5959 aut A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning Mandic, Danilo verfasserin (orcid)0000-0001-8432-3963 aut Sejdić, Ervin verfasserin (orcid)0000-0003-4987-8298 aut Enthalten in Information fusion Amsterdam [u.a.] : Elsevier Science, 2000 69, Seite 52-72 Online-Ressource (DE-627)320648133 (DE-600)2025632-2 (DE-576)259271926 1566-2535 nnns volume:69 pages:52-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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 54.72 Künstliche Intelligenz 54.74 Maschinelles Sehen 53.73 Nachrichtenübertragung AR 69 52-72
allfieldsGer	10.1016/j.inffus.2020.11.008 doi (DE-627)ELV005351812 (ELSEVIER)S1566-2535(20)30414-0 DE-627 ger DE-627 rda eng 620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl Khalifa, Yassin verfasserin (orcid)0000-0001-8017-5959 aut A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning Mandic, Danilo verfasserin (orcid)0000-0001-8432-3963 aut Sejdić, Ervin verfasserin (orcid)0000-0003-4987-8298 aut Enthalten in Information fusion Amsterdam [u.a.] : Elsevier Science, 2000 69, Seite 52-72 Online-Ressource (DE-627)320648133 (DE-600)2025632-2 (DE-576)259271926 1566-2535 nnns volume:69 pages:52-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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 54.72 Künstliche Intelligenz 54.74 Maschinelles Sehen 53.73 Nachrichtenübertragung AR 69 52-72
allfieldsSound	10.1016/j.inffus.2020.11.008 doi (DE-627)ELV005351812 (ELSEVIER)S1566-2535(20)30414-0 DE-627 ger DE-627 rda eng 620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl Khalifa, Yassin verfasserin (orcid)0000-0001-8017-5959 aut A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning Mandic, Danilo verfasserin (orcid)0000-0001-8432-3963 aut Sejdić, Ervin verfasserin (orcid)0000-0003-4987-8298 aut Enthalten in Information fusion Amsterdam [u.a.] : Elsevier Science, 2000 69, Seite 52-72 Online-Ressource (DE-627)320648133 (DE-600)2025632-2 (DE-576)259271926 1566-2535 nnns volume:69 pages:52-72 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_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 54.72 Künstliche Intelligenz 54.74 Maschinelles Sehen 53.73 Nachrichtenübertragung AR 69 52-72
language	English
source	Enthalten in Information fusion 69, Seite 52-72 volume:69 pages:52-72
sourceStr	Enthalten in Information fusion 69, Seite 52-72 volume:69 pages:52-72
format_phy_str_mv	Article
bklname	Künstliche Intelligenz Maschinelles Sehen Nachrichtenübertragung
institution	findex.gbv.de
topic_facet	Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning
dewey-raw	620
isfreeaccess_bool	false
container_title	Information fusion
authorswithroles_txt_mv	Khalifa, Yassin @@aut@@ Mandic, Danilo @@aut@@ Sejdić, Ervin @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320648133
dewey-sort	3620
id	ELV005351812
language_de	englisch
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author	Khalifa, Yassin
spellingShingle	Khalifa, Yassin ddc 620 bkl 54.72 bkl 54.74 bkl 53.73 misc Event detection misc Hidden Markov models misc Recurrent Neural Networks misc Deep learning misc Biomedical signal processing misc Transfer learning A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
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topic_title	620 DE-600 54.72 bkl 54.74 bkl 53.73 bkl A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals Event detection Hidden Markov models Recurrent Neural Networks Deep learning Biomedical signal processing Transfer learning
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title	A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
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title_full	A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
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title_sort	a review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals
title_auth	A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
abstract	Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.
abstractGer	Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.
abstract_unstemmed	Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.
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title_short	A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals
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up_date	2024-07-06T17:40:30.868Z
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A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

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