| Presentation | 2022-05-26 Arterial Blood Pressure Estimation from Electrocardiogram Signals using U-Net Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Blood pressure estimation methods using electrocardiogram (ECG) signals have been recently studied for user-friendly blood pressure estimation. Previous works proposed deep learning models to estimate blood pressure from ECG signals. However, they can only estimate max, min, and mean blood pressures in about a 10-second segment and cannot estimate the continuous blood pressure transition, called arterial blood pressure (ABP). This report presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with a dataset of about 185 hours of ECG signals, we observed that the proposed method estimated ABP with high accuracy. Furthermore, we confirmed that the accuracies of the calculated max, min, and mean ABPs were comparable to those in the previous works, even though our method can estimate ABP. In the end, we discussed the subject-overfitting problem and future work based on the evaluation of our model and a model proposed in the previous work. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Blood pressure / Deep learning / ECG / Health monitoring |
| Paper # | SeMI2022-5 |
| Date of Issue | 2022-05-19 (SeMI) |
| Conference Information | |
| Committee | SeMI / IPSJ-DPS / IPSJ-MBL / IPSJ-ITS |
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| Conference Date | 2022/5/26(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Koji Yamamoto(Kyoto Univ.) |
| Vice Chair | Kazuya Monden(Hitachi) / Yasunori Owada(NICT) |
| Secretary | Kazuya Monden(Cyber Univ.) / Yasunori Owada(Waseda Univ.) / (Osaka Univ.) |
| Assistant | Yuki Katsumata(NTT DOCOMO) / Akihito Taya(Aoyama Gakuin Univ.) / Yu Nakayama(Tokyo Univ. of Agri. and Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Sensor Network and Mobile Intelligence / Special Interest Group on Distributed Processing System / Special Interest Group on Mobile Computing and Pervasive Systems / Special Interest Group on Intelligent Transport Systems and Smart Community |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Arterial Blood Pressure Estimation from Electrocardiogram Signals using U-Net |
| Sub Title (in English) | |
| Keyword(1) | Blood pressure |
| Keyword(2) | Deep learning |
| Keyword(3) | ECG |
| Keyword(4) | Health monitoring |
| 1st Author's Name | Rikuto Yoshizawa |
| 1st Author's Affiliation | Keio University(Keio Univ.) |
| 2nd Author's Name | Kohei Yamamoto |
| 2nd Author's Affiliation | Keio University(Keio Univ.) |
| 3rd Author's Name | Tomoaki Ohtsuki |
| 3rd Author's Affiliation | Keio University(Keio Univ.) |
| Date | 2022-05-26 |
| Paper # | SeMI2022-5 |
| Volume (vol) | vol.122 |
| Number (no) | SeMI-46 |
| Page | pp.pp.20-25(SeMI), |
| #Pages | 6 |
| Date of Issue | 2022-05-19 (SeMI) |