UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from Photoplethysmography Article Swipe
Yanke Guo
,
Qunfeng Tang
,
Zhencheng Chen
,
Shiyong Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13101869
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/electronics13101869
Electrocardiography (ECG) is generally used in clinical practice for cardiovascular diagnosis and for monitoring cardiovascular status. It is considered to be the gold standard for diagnosing cardiovascular diseases and assessing cardiovascular status. However, it is not always easy to obtain. Unlike ECG devices, photoplethysmography (PPG) devices can be placed on body parts such as the earlobes, fingertips, and wrists, making them more comfortable and easier to obtain. Several methods for reconstructing ECG signals using PPG signals have been proposed, but some of these methods are subject-specific models. These models cannot be applied to multiple subjects and have limitations. This study proposes a neural network model based on UNet and bidirectional long short-term memory (BiLSTM) networks as a group model for reconstructing ECG from PPG. The model was verified using 125 records from the MIMIC III matched subset. The experimental results demonstrated that the proposed model was, on average, able to achieve a Pearson‘s correlation coefficient, root mean square error, percentage root mean square difference, and Fréchet distance of 0.861, 0.077, 5.302, and 0.278, respectively. This research can use the correlation between PPG and ECG to reconstruct a better ECG signal from PPG, which is crucial for diagnosing cardiovascular diseases.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics13101869
- https://www.mdpi.com/2079-9292/13/10/1869/pdf?version=1715346814
- OA Status
- gold
- Cited By
- 7
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396871821
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4396871821Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics13101869Digital Object Identifier
- Title
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UNet-BiLSTM: A Deep Learning Method for Reconstructing Electrocardiography from PhotoplethysmographyWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-10Full publication date if available
- Authors
-
Yanke Guo, Qunfeng Tang, Zhencheng Chen, Shiyong LiList of authors in order
- Landing page
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https://doi.org/10.3390/electronics13101869Publisher landing page
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https://www.mdpi.com/2079-9292/13/10/1869/pdf?version=1715346814Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2079-9292/13/10/1869/pdf?version=1715346814Direct OA link when available
- Concepts
-
Photoplethysmogram, Electrocardiography, Artificial intelligence, Computer science, Deep learning, Medicine, Cardiology, Telecommunications, WirelessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.parts | 51 |
| abstract_inverted_index.study | 99 |
| abstract_inverted_index.these | 82 |
| abstract_inverted_index.using | 73, 128 |
| abstract_inverted_index.which | 192 |
| abstract_inverted_index.0.077, | 169 |
| abstract_inverted_index.0.278, | 172 |
| abstract_inverted_index.0.861, | 168 |
| abstract_inverted_index.5.302, | 170 |
| abstract_inverted_index.Unlike | 40 |
| abstract_inverted_index.always | 36 |
| abstract_inverted_index.better | 187 |
| abstract_inverted_index.cannot | 89 |
| abstract_inverted_index.easier | 64 |
| abstract_inverted_index.error, | 158 |
| abstract_inverted_index.making | 59 |
| abstract_inverted_index.memory | 112 |
| abstract_inverted_index.models | 88 |
| abstract_inverted_index.neural | 102 |
| abstract_inverted_index.placed | 48 |
| abstract_inverted_index.signal | 189 |
| abstract_inverted_index.square | 157, 162 |
| abstract_inverted_index.Several | 67 |
| abstract_inverted_index.achieve | 150 |
| abstract_inverted_index.applied | 91 |
| abstract_inverted_index.between | 180 |
| abstract_inverted_index.crucial | 194 |
| abstract_inverted_index.devices | 45 |
| abstract_inverted_index.matched | 135 |
| abstract_inverted_index.methods | 68, 83 |
| abstract_inverted_index.models. | 86 |
| abstract_inverted_index.network | 103 |
| abstract_inverted_index.obtain. | 39, 66 |
| abstract_inverted_index.records | 130 |
| abstract_inverted_index.results | 139 |
| abstract_inverted_index.signals | 72, 75 |
| abstract_inverted_index.status. | 15, 31 |
| abstract_inverted_index.subset. | 136 |
| abstract_inverted_index.wrists, | 58 |
| abstract_inverted_index.(BiLSTM) | 113 |
| abstract_inverted_index.Fréchet | 165 |
| abstract_inverted_index.However, | 32 |
| abstract_inverted_index.average, | 147 |
| abstract_inverted_index.clinical | 6 |
| abstract_inverted_index.devices, | 42 |
| abstract_inverted_index.diseases | 27 |
| abstract_inverted_index.distance | 166 |
| abstract_inverted_index.multiple | 93 |
| abstract_inverted_index.networks | 114 |
| abstract_inverted_index.practice | 7 |
| abstract_inverted_index.proposed | 143 |
| abstract_inverted_index.proposes | 100 |
| abstract_inverted_index.research | 175 |
| abstract_inverted_index.standard | 23 |
| abstract_inverted_index.subjects | 94 |
| abstract_inverted_index.verified | 127 |
| abstract_inverted_index.assessing | 29 |
| abstract_inverted_index.diagnosis | 10 |
| abstract_inverted_index.diseases. | 198 |
| abstract_inverted_index.earlobes, | 55 |
| abstract_inverted_index.generally | 3 |
| abstract_inverted_index.proposed, | 78 |
| abstract_inverted_index.considered | 18 |
| abstract_inverted_index.diagnosing | 25, 196 |
| abstract_inverted_index.monitoring | 13 |
| abstract_inverted_index.percentage | 159 |
| abstract_inverted_index.short-term | 111 |
| abstract_inverted_index.Pearson‘s | 152 |
| abstract_inverted_index.comfortable | 62 |
| abstract_inverted_index.correlation | 153, 179 |
| abstract_inverted_index.difference, | 163 |
| abstract_inverted_index.fingertips, | 56 |
| abstract_inverted_index.reconstruct | 185 |
| abstract_inverted_index.coefficient, | 154 |
| abstract_inverted_index.demonstrated | 140 |
| abstract_inverted_index.experimental | 138 |
| abstract_inverted_index.limitations. | 97 |
| abstract_inverted_index.bidirectional | 109 |
| abstract_inverted_index.respectively. | 173 |
| abstract_inverted_index.cardiovascular | 9, 14, 26, 30, 197 |
| abstract_inverted_index.reconstructing | 70, 120 |
| abstract_inverted_index.subject-specific | 85 |
| abstract_inverted_index.Electrocardiography | 0 |
| abstract_inverted_index.photoplethysmography | 43 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5041982037, https://openalex.org/A5041400795 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I5343935 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.83289499 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |