Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring
<h3>Background and Motivations</h3><p dir="ltr">Physiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine lea...
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2024
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| _version_ | 1864513529431719936 |
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| author | Sakib Mahmud (15302404) |
| author2 | Muhammad E.H. Chowdhury (17151154) Serkan Kiranyaz (3762058) Malisha Islam Tapotee (17823464) Purnata Saha (17823467) Anas M. Tahir (16870077) Amith Khandakar (14151981) Abdulrahman Alqahtani (6056309) |
| author2_role | author author author author author author author |
| author_facet | Sakib Mahmud (15302404) Muhammad E.H. Chowdhury (17151154) Serkan Kiranyaz (3762058) Malisha Islam Tapotee (17823464) Purnata Saha (17823467) Anas M. Tahir (16870077) Amith Khandakar (14151981) Abdulrahman Alqahtani (6056309) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sakib Mahmud (15302404) Muhammad E.H. Chowdhury (17151154) Serkan Kiranyaz (3762058) Malisha Islam Tapotee (17823464) Purnata Saha (17823467) Anas M. Tahir (16870077) Amith Khandakar (14151981) Abdulrahman Alqahtani (6056309) |
| dc.date.none.fl_str_mv | 2024-07-15T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.eswa.2024.123167 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Wearable_wrist_to_finger_photoplethysmogram_translation_through_restoration_using_super_operational_neural_networks_based_1D-CycleGAN_for_enhancing_cardiovascular_monitoring/25038299 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Communications engineering Information and computing sciences Data management and data science Machine learning TTR-GAN 1D-CycleGANs Super-ONN Wearables Blind PPG Restoration Wrist to Finger PPG Translation Heart Rate Variability |
| dc.title.none.fl_str_mv | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background and Motivations</h3><p dir="ltr">Physiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine learning algorithms, frequently struggle to effectively restore the inherent bodily signals amidst the array of randomly generated distortions. This often leads to the modification or even the degradation of the underlying physiological information.</p><h3>Methods</h3><p dir="ltr">To enhance heart rate estimation from wrist PPG (wPPG) signals, this study introduces the Translation Through Restoration GAN (TTR-GAN). TTR-GAN comprises cascaded dual-stage 1D Cycle Generative Adversarial Networks (1D-CycleGANs) constructed using Super-ONNs. In the first phase, corrupted wPPG waveforms are blindly restored using a 1D-CycleGAN-based restoration framework. Subsequently, in the second phase, the restored wPPG waveforms are translated into clean finger PPG (fPPG) signals through a 1D-CycleGAN-based signal-to-signal translation or synthesis framework. Both the restorer and translator GANs undergo independent evaluation using robust temporal, spectral, and clinical metrics.</p><h3>Results</h3><p dir="ltr">The application of the multipass restoration scheme to the wPPG signals resulted in significantly lower entropy compared to the raw wPPGs, indicating reduced irregularity. Using the proposed PRTX metric to evaluate the translational ability of the multichannel translator CycleGAN, we achieved a substantial improvement of 35.88% in wrist-to-finger PPG translation. The correlation between the pulse rate and pulse rate variations estimated from the generated fPPG signals and the heart rate and heart rate variability readings from the ground truth ECG improved by approximately 10.4% and 14.7%, respectively, when compared to the raw wPPG signals.</p><h3>Conclusion</h3><p dir="ltr">The proposed TTR-GAN can be implemented in wearable devices to obtain reliable real-time cardiovascular data during daily activities.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2024.123167" target="_blank">https://dx.doi.org/10.1016/j.eswa.2024.123167</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a49a2abdcb4dbaa60804ef28af16195b |
| identifier_str_mv | 10.1016/j.eswa.2024.123167 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25038299 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoringSakib Mahmud (15302404)Muhammad E.H. Chowdhury (17151154)Serkan Kiranyaz (3762058)Malisha Islam Tapotee (17823464)Purnata Saha (17823467)Anas M. Tahir (16870077)Amith Khandakar (14151981)Abdulrahman Alqahtani (6056309)EngineeringBiomedical engineeringCommunications engineeringInformation and computing sciencesData management and data scienceMachine learningTTR-GAN1D-CycleGANsSuper-ONNWearablesBlind PPG RestorationWrist to Finger PPG TranslationHeart Rate Variability<h3>Background and Motivations</h3><p dir="ltr">Physiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine learning algorithms, frequently struggle to effectively restore the inherent bodily signals amidst the array of randomly generated distortions. This often leads to the modification or even the degradation of the underlying physiological information.</p><h3>Methods</h3><p dir="ltr">To enhance heart rate estimation from wrist PPG (wPPG) signals, this study introduces the Translation Through Restoration GAN (TTR-GAN). TTR-GAN comprises cascaded dual-stage 1D Cycle Generative Adversarial Networks (1D-CycleGANs) constructed using Super-ONNs. In the first phase, corrupted wPPG waveforms are blindly restored using a 1D-CycleGAN-based restoration framework. Subsequently, in the second phase, the restored wPPG waveforms are translated into clean finger PPG (fPPG) signals through a 1D-CycleGAN-based signal-to-signal translation or synthesis framework. Both the restorer and translator GANs undergo independent evaluation using robust temporal, spectral, and clinical metrics.</p><h3>Results</h3><p dir="ltr">The application of the multipass restoration scheme to the wPPG signals resulted in significantly lower entropy compared to the raw wPPGs, indicating reduced irregularity. Using the proposed PRTX metric to evaluate the translational ability of the multichannel translator CycleGAN, we achieved a substantial improvement of 35.88% in wrist-to-finger PPG translation. The correlation between the pulse rate and pulse rate variations estimated from the generated fPPG signals and the heart rate and heart rate variability readings from the ground truth ECG improved by approximately 10.4% and 14.7%, respectively, when compared to the raw wPPG signals.</p><h3>Conclusion</h3><p dir="ltr">The proposed TTR-GAN can be implemented in wearable devices to obtain reliable real-time cardiovascular data during daily activities.</p><h2>Other Information</h2><p dir="ltr">Published in: Expert Systems with Applications<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2024.123167" target="_blank">https://dx.doi.org/10.1016/j.eswa.2024.123167</a></p>2024-07-15T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.eswa.2024.123167https://figshare.com/articles/journal_contribution/Wearable_wrist_to_finger_photoplethysmogram_translation_through_restoration_using_super_operational_neural_networks_based_1D-CycleGAN_for_enhancing_cardiovascular_monitoring/25038299CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250382992024-07-15T03:00:00Z |
| spellingShingle | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring Sakib Mahmud (15302404) Engineering Biomedical engineering Communications engineering Information and computing sciences Data management and data science Machine learning TTR-GAN 1D-CycleGANs Super-ONN Wearables Blind PPG Restoration Wrist to Finger PPG Translation Heart Rate Variability |
| status_str | publishedVersion |
| title | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| title_full | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| title_fullStr | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| title_full_unstemmed | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| title_short | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| title_sort | Wearable wrist to finger photoplethysmogram translation through restoration using super operational neural networks based 1D-CycleGAN for enhancing cardiovascular monitoring |
| topic | Engineering Biomedical engineering Communications engineering Information and computing sciences Data management and data science Machine learning TTR-GAN 1D-CycleGANs Super-ONN Wearables Blind PPG Restoration Wrist to Finger PPG Translation Heart Rate Variability |