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13 results on '"Dickson, Emily L."'

3. Atrial Fibrillation Detection on Reconstructed Photoplethysmography Signals Collected from a Smartwatch using a Denoising Autoencoder

Author

Mohagheghian, Fahimeh, primary, Dong, Han, additional, Ghetia, Om, additional, Chen, Darren, additional, Peitzsch, Andrew, additional, Nishita, Nishat, additional, Ding, Eric Y., additional, Mensah Otabil, Edith, additional, Noorishirazi, Kamran, additional, Hamel, Alexander, additional, Dickson, Emily L., additional, DiMezza, Danielle, additional, Tran, Khanh-Van, additional, McManus, David D., additional, and Chon, Ki H., additional

Published
2023
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4. Noise Reduction in Photoplethysmography Signals Using a Convolutional Denoising Autoencoder With Unconventional Training Scheme

Author

Mohagheghian, Fahimeh, Han, Dong, Ghetia, Om, Peitzsch, Andrew, Nishita, Nishat, Pirayesh Shirazi Nejad, Mahdi, Ding, Eric Y., Noorishirazi, Kamran, Hamel, Alexander, Otabil, Edith Mensah, DiMezza, Danielle, Dickson, Emily L., Tran, Khanh-Van, McManus, David D., and Chon, Ki H.

Abstract

Objective: We propose an efficient approach based on a convolutional denoising autoencoder (CDA) network to reduce motion and noise artifacts (MNA) from corrupted atrial fibrillation (AF) and non-AF photoplethysmography (PPG) data segments so that an accurate PPG-signal-derived heart rate can be obtained. Our method's main innovation is the optimization of the CDA performance for both rhythms using more AF than non-AF data for training the AF-specific CDA model and vice versa for the non-AF CDA network. Methods: To evaluate this unconventional training scheme, our proposed network was trained and tested on 25-sec PPG data segments from 48 subjects from two different databases–the Pulsewatch dataset and Stanford University's publicly available PPG dataset. In total, our dataset contains 10,773 data segments: 7,001 segments for training and 3,772 independent segments from out-of-sample subjects for testing. Results: Using real-life corrupted PPG segments, our approach significantly reduced the average heart rate root mean square error (RMSE) of the reconstructed PPG segments by 45.74% and 23% compared to the corrupted non-AF and AF data, respectively. Further, our approach exhibited lower RMSE, and higher sensitivity and PPV for detected peaks compared to the reconstructed data produced by the alternative methods. Conclusion: These results show the promise of our approach as a reliable denoising method, which should be used prior to AF detection algorithms for an accurate cardiac health monitoring involving wearable devices. Significance: PPG signals collected from wearables are vulnerable to MNA, which limits their use as a reliable measurement, particularly in uncontrolled real-life environments.

Published
2024
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5. A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study

Author

Han, Dong, primary, Ding, Eric Y, additional, Cho, Chaeho, additional, Jung, Haewook, additional, Dickson, Emily L, additional, Mohagheghian, Fahimeh, additional, Peitzsch, Andrew G, additional, DiMezza, Danielle, additional, Tran, Khanh-Van, additional, McManus, David D, additional, and Chon, Ki H, additional

Published
2023
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6. Noise Reduction in Photoplethysmography Signals using a Convolutional Denoising Autoencoder with Unconventional Training Scheme

Author

Mohagheghian, Fahimeh, primary, Han, Dong, additional, Ghetia, Om, additional, Peitzsch, Andrew, additional, Nishita, Nishat, additional, Nejad, Mahdi Pirayesh Shirazi, additional, Ding, Eric Y., additional, Noorishirazi, Kamran, additional, Hamel, Alexander, additional, Otabil, Edith Mensah, additional, DiMezza, Danielle, additional, Dickson, Emily L., additional, Tran, Khanh-Van, additional, McManus, David D., additional, and Chon, Ki H., additional

Published
2023
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7. Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection

Author

Mohagheghian, Fahimeh, primary, Han, Dong, additional, Peitzsch, Andrew, additional, Nishita, Nishat, additional, Ding, Eric, additional, Dickson, Emily L., additional, DiMezza, Danielle, additional, Otabil, Edith M., additional, Noorishirazi, Kamran, additional, Scott, Jessica, additional, Lessard, Darleen, additional, Wang, Ziyue, additional, Whitcomb, Cody, additional, Tran, Khanh-Van, additional, Fitzgibbons, Timothy P., additional, McManus, David D., additional, and Chon, Ki H., additional

Published
2022
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8. Pulsewatch: A Smartwatch System Designed with Stroke Survivors for Continuous Monitoring of Atrial Fibrillation in Older Adults after Stroke or Transient Ischemic Attack (Preprint)

Author

Han, Dong, primary, Ding, Eric Y, additional, Cho, Chaeho, additional, Jung, Haewook, additional, Dickson, Emily L, additional, Mohagheghian, Fahimeh, additional, Peitzsch, Andrew, additional, DiMezza, Danielle, additional, Tran, Khanh-Van, additional, McManus, David D, additional, and Chon, Ki H, additional

Published
2022
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9. A Real-Time PPG Peak Detection Method for Accurate Determination of Heart Rate during Sinus Rhythm and Cardiac Arrhythmia

Author

Han, Dong, primary, Bashar, Syed Khairul, additional, Lázaro, Jesús, additional, Mohagheghian, Fahimeh, additional, Peitzsch, Andrew, additional, Nishita, Nishat, additional, Ding, Eric, additional, Dickson, Emily L., additional, DiMezza, Danielle, additional, Scott, Jessica, additional, Whitcomb, Cody, additional, Fitzgibbons, Timothy P., additional, McManus, David D., additional, and Chon, Ki H., additional

Published
2022
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10. Atrial Fibrillation Detection on Reconstructed Photoplethysmography Signals Collected from a Smart Watch Using Denoising Autoencoder

Author

Mohagheghian, Fahimeh, primary, Han, Dong, additional, Ghetia, Om, additional, Peitzsch, Andrew, additional, Nishita, Nishat, additional, Ding, Eric Y., additional, Mensah Otabil, Edith, additional, Noorishirazi, Kamran, additional, Hamel, Alexander, additional, Dickson, Emily L., additional, Dimezza, Danielle, additional, Tran, Khanh-Van, additional, McManus, David D., additional, and Chon, Ki, additional

Published
2022
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11. Abstract 9886: Use of a Smartwatch and App Designed by Stroke Survivors for Atrial Fibrillation Detection in Older Adults After Stroke/Transient Ischemic Event: Preliminary Findings from an Ongoing Randomized Clinical Trial

Author

Ding, Eric Y, primary, Han, Dong, additional, Dickson, Emily L, additional, DiMezza, Danielle, additional, Scott, Jessica, additional, Mohagheghian, Fahimeh, additional, Peitzsch, Andrew, additional, Saczynski, Jane, additional, Moonis, Majaz, additional, Fitzgibbons, Timothy P, additional, Barton, Bruce, additional, Chon, Ki, additional, and McManus, David D, additional

Published
2021
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13. Multiclass arrhythmia classification using multimodal smartwatch photoplethysmography signals collected in real-life settings.

Author

Han D, Moon J, Díaz LRM, Chen D, Williams D, Mohagheghian F, Ghetia O, Peitzsch AG, Kong Y, Nishita N, Ghutadaria O, Orwig TA, Otabil EM, Noorishirazi K, Hamel A, Dickson EL, DiMezza D, Lessard D, Wang Z, Mehawej J, Filippaios A, Naeem S, Gottbrecht MF, Fitzgibbons TP, Saczynski JS, Barton B, Ding EY, Tran KV, McManus DD, and Chon KH

Abstract

In the early stages of atrial fibrillation (AF), most cases are paroxysmal (pAF), making identification only possible with continuous and prolonged monitoring. With the advent of wearables, smartwatches equipped with photoplethysmographic (PPG) sensors are an ideal approach for continuous monitoring of pAF. There have been numerous studies demonstrating successful capture of pAF events, especially using deep learning. However, deep learning requires a large amount of data and independent testing on diverse datasets, to ensure the generalizability of the model, and most prior studies did not meet these requirements. Moreover, most prior studies using wearable-based PPG sensor data collection were limited either to controlled environments, to minimize motion artifacts, or to short duration data collection. Most importantly, frequent premature atrial and ventricular contractions (PAC/PVC) can confound most AF detection algorithms. This has not been well studied, largely due to limited datasets containing these rhythms. Note that the recent deep learning models show 97% AF detection accuracy, and the sensitivity of the current state-of-the-art technique for PAC/PVC detection is only 75% on minimally motion artifact corrupted PPG data. Our study aims to address the above limitations using a recently completed NIH-funded Pulsewatch clinical trial which collected smartwatch PPG data over two weeks from 106 subjects. For our approach, we used multi-modal data which included 1D PPG, accelerometer, and heart rate data. We used a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) deep learning model to detect three classes: normal sinus rhythm, AF, and PAC/PVC. Our proposed 1D-Bi-GRU model's performance was compared with two other deep learning models that have reported some of the highest performance metrics, in prior work. For three-arrhythmia-classification, testing data for all deep learning models consisted of using independent data and subjects from the training data, and further evaluations were performed using two independent datasets that were not part of the training dataset. Our multimodal model achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. Our model was computationally more efficient (14 times more efficient and 2.7 times faster) and outperformed the best state-of-the-art model by 20.81% for PAC/PVC sensitivity and 2.55% for AF accuracy. We also tested our models on two independent PPG datasets collected with a different smartwatch and a fingertip PPG sensor. Our three-arrhythmia-classification results show high macro-averaged area under the receiver operating characteristic curve values of 96.22%, and 94.17% for two independent datasets, demonstrating better generalizability of the proposed model.

Published
2024
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