MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation

Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when...
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Gespeichert in:

Autor*in:	Sun, Yifan [verfasserIn] Shen, Jingyan [verfasserIn] Jiang, Yunfan [verfasserIn] Huang, Zhaohui [verfasserIn] Hao, Minsheng [verfasserIn] Zhang, Xuegong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network

Übergeordnetes Werk:	Enthalten in: Biomedical signal processing and control - Amsterdam [u.a.] : Elsevier, 2006, 89
Übergeordnetes Werk:	volume:89

DOI / URN:	10.1016/j.bspc.2023.105747

Katalog-ID:	ELV066702461

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100	1		\|a Sun, Yifan \|e verfasserin \|4 aut
245	1	0	\|a MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
264		1	\|c 2023
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520			\|a Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.
650		4	\|a Atrial fibrillation
650		4	\|a Electrocardiogram
650		4	\|a Deep learning
650		4	\|a Attention mechanism
650		4	\|a Recurrent neural network
700	1		\|a Shen, Jingyan \|e verfasserin \|4 aut
700	1		\|a Jiang, Yunfan \|e verfasserin \|4 aut
700	1		\|a Huang, Zhaohui \|e verfasserin \|0 (orcid)0000-0001-6869-7965 \|4 aut
700	1		\|a Hao, Minsheng \|e verfasserin \|0 (orcid)0000-0001-6749-5659 \|4 aut
700	1		\|a Zhang, Xuegong \|e verfasserin \|0 (orcid)0000-0002-9684-5643 \|4 aut
773	0	8	\|i Enthalten in \|t Biomedical signal processing and control \|d Amsterdam [u.a.] : Elsevier, 2006 \|g 89 \|h Online-Ressource \|w (DE-627)515537861 \|w (DE-600)2241886-6 \|w (DE-576)261592653 \|x 1746-8108 \|7 nnns
773	1	8	\|g volume:89
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912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
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912			\|a GBV_ILN_150
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912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
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912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2008
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912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
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912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
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912			\|a GBV_ILN_2112
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912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
936	b	k	\|a 44.09 \|j Medizintechnik \|q VZ
936	b	k	\|a 44.32 \|j Medizinische Mathematik \|j medizinische Statistik \|q VZ
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Indexfelder

author_variant	y s ys j s js y j yj z h zh m h mh x z xz
matchkey_str	article:17468108:2023----::mrnmlieemliaktetobsdeurnnuantokodsrmntoado
hierarchy_sort_str	2023
bklnumber	44.09 44.32
publishDate	2023
allfields	10.1016/j.bspc.2023.105747 doi (DE-627)ELV066702461 (ELSEVIER)S1746-8094(23)01180-1 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Sun, Yifan verfasserin aut MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention. Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network Shen, Jingyan verfasserin aut Jiang, Yunfan verfasserin aut Huang, Zhaohui verfasserin (orcid)0000-0001-6869-7965 aut Hao, Minsheng verfasserin (orcid)0000-0001-6749-5659 aut Zhang, Xuegong verfasserin (orcid)0000-0002-9684-5643 aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 89 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 89
spelling	10.1016/j.bspc.2023.105747 doi (DE-627)ELV066702461 (ELSEVIER)S1746-8094(23)01180-1 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Sun, Yifan verfasserin aut MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention. Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network Shen, Jingyan verfasserin aut Jiang, Yunfan verfasserin aut Huang, Zhaohui verfasserin (orcid)0000-0001-6869-7965 aut Hao, Minsheng verfasserin (orcid)0000-0001-6749-5659 aut Zhang, Xuegong verfasserin (orcid)0000-0002-9684-5643 aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 89 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 89
allfields_unstemmed	10.1016/j.bspc.2023.105747 doi (DE-627)ELV066702461 (ELSEVIER)S1746-8094(23)01180-1 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Sun, Yifan verfasserin aut MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention. Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network Shen, Jingyan verfasserin aut Jiang, Yunfan verfasserin aut Huang, Zhaohui verfasserin (orcid)0000-0001-6869-7965 aut Hao, Minsheng verfasserin (orcid)0000-0001-6749-5659 aut Zhang, Xuegong verfasserin (orcid)0000-0002-9684-5643 aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 89 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 89
allfieldsGer	10.1016/j.bspc.2023.105747 doi (DE-627)ELV066702461 (ELSEVIER)S1746-8094(23)01180-1 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Sun, Yifan verfasserin aut MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention. Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network Shen, Jingyan verfasserin aut Jiang, Yunfan verfasserin aut Huang, Zhaohui verfasserin (orcid)0000-0001-6869-7965 aut Hao, Minsheng verfasserin (orcid)0000-0001-6749-5659 aut Zhang, Xuegong verfasserin (orcid)0000-0002-9684-5643 aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 89 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 89
allfieldsSound	10.1016/j.bspc.2023.105747 doi (DE-627)ELV066702461 (ELSEVIER)S1746-8094(23)01180-1 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Sun, Yifan verfasserin aut MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention. Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network Shen, Jingyan verfasserin aut Jiang, Yunfan verfasserin aut Huang, Zhaohui verfasserin (orcid)0000-0001-6869-7965 aut Hao, Minsheng verfasserin (orcid)0000-0001-6749-5659 aut Zhang, Xuegong verfasserin (orcid)0000-0002-9684-5643 aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 89 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:89 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 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_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 89
language	English
source	Enthalten in Biomedical signal processing and control 89 volume:89
sourceStr	Enthalten in Biomedical signal processing and control 89 volume:89
format_phy_str_mv	Article
bklname	Medizintechnik Medizinische Mathematik medizinische Statistik
institution	findex.gbv.de
topic_facet	Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network
dewey-raw	610
isfreeaccess_bool	false
container_title	Biomedical signal processing and control
authorswithroles_txt_mv	Sun, Yifan @@aut@@ Shen, Jingyan @@aut@@ Jiang, Yunfan @@aut@@ Huang, Zhaohui @@aut@@ Hao, Minsheng @@aut@@ Zhang, Xuegong @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	515537861
dewey-sort	3610
id	ELV066702461
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">ELV066702461</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240124093149.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240124s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.bspc.2023.105747</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV066702461</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1746-8094(23)01180-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.09</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.32</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Yifan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. 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author	Sun, Yifan
spellingShingle	Sun, Yifan ddc 610 bkl 44.09 bkl 44.32 misc Atrial fibrillation misc Electrocardiogram misc Deep learning misc Attention mechanism misc Recurrent neural network MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
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topic_title	610 VZ 44.09 bkl 44.32 bkl MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation Atrial fibrillation Electrocardiogram Deep learning Attention mechanism Recurrent neural network
topic	ddc 610 bkl 44.09 bkl 44.32 misc Atrial fibrillation misc Electrocardiogram misc Deep learning misc Attention mechanism misc Recurrent neural network
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title	MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
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title_full	MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
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title_sort	mma-rnn: a multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
title_auth	MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
abstract	Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.
abstractGer	Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.
abstract_unstemmed	Background:: Atrial fibrillation significantly impacts individual health, public wellness, and healthcare expenditure. The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.
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title_short	MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation
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author2	Shen, Jingyan Jiang, Yunfan Huang, Zhaohui Hao, Minsheng Zhang, Xuegong
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up_date	2024-07-06T18:41:44.768Z
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The primary tool for its detection is the Electrocardiograph (ECG), a non-invasive test that generates abundant data requiring expert human analysis. This task becomes exceedingly complex when interpreting ECG signals with cyclical patterns, fluctuating lengths, and vulnerability to noise. Despite these challenges, there exists a significant gap in the research focusing on further differentiating persistent and paroxysmal atrial fibrillation, a task akin to multi-class discrimination, and pinpointing the exact start and end of abnormal episodes, a process central to localization.Methodology:: We present the Multi-level Multi-task Attention-based Recurrent Neural Network (MMA-RNN), a solution founded on a hierarchical structure that leverages Bidirectional Long and Short-Term Memory Networks (Bi-LSTM) and attention layers to discern three-level sequential features. This framework enhances information synergy and reduces error accumulation by deploying a multi-head classifier that concurrently tackles both previously mentioned critical tasks, facilitating a more streamlined and accurate analysis.Results:: We affirm the proficiency of our model through rigorous testing on the CPSC 2021 dataset, where it achieved 0.9437 out of 1 in discrimination and 1.3538 out of 1.6479 in localization. Our method also exhibited superior performance in line with official evaluation benchmarks.Conclusions:: Our innovative algorithm holds the potential for real-time AF monitoring through wearable technology, promoting proactive healthcare and early intervention.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Atrial fibrillation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Electrocardiogram</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Attention mechanism</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recurrent neural network</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shen, Jingyan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Yunfan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Zhaohui</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6869-7965</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hao, Minsheng</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-6749-5659</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xuegong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9684-5643</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Biomedical signal processing and control</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier, 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MMA-RNN: A multi-level multi-task attention-based recurrent neural network for discrimination and localization of atrial fibrillation

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