| Project/Area Number |
15K16085
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Life / Health / Medical informatics
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| Research Institution | Tokyo Denki University (2017)
Chiba University of Commerce (2015-2016) |
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Principal Investigator |
SAKAI Motoki 東京電機大学, システムデザイン工学部, 准教授 (50597094)
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| Research Collaborator |
Zhu Xin
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 睡眠時無呼吸症候群 / ECG / 呼吸推定 / ECG-Derived Respiration / 心電図 / ECG-derived REspiration / 心電図による呼吸推定 / 新しい心電図誘導法 / 呼吸推定に適した心電図誘導法 |
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| Outline of Final Research Achievements |
EDR is a method to estimate the respiration from the ECG. The EDR has been used in the screening of OSAS to avoid the use of devices. To date, several EDR algorithms have been proposed, but no effective lead system for estimating the respiratory has been presented. This study attempted to investigate an effective ECG channel. In this study, 12 novel ECG channels placed around the lower chest were proposed, and estimation accuracies with the proposed channels were compared with those of six Holter ECG ones. To evaluate effective ECG channels, experiments using 47 subjects recorded ECG signals and actual respiration. The effectiveness of the adopted ECG channels was examined using the correlation coefficient between the actual respiratory rates and ones of EDR. As a result, the most accurate estimations were yielded when both electrodes were located in a way that they could measure both the bucket and pump-handle movements of the ribs and when one electrode was located near the heart.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では、複数の呼吸推定ルゴリズム(EDR)で呼吸の推定をおこなった。提案心電図誘導法から得られた心電図からは、多くのアルゴリズムで、従来の誘導法から得られた心電図以上の精度での推定が可能であった。本研究により、呼吸計測のために特殊な機器を使用することなく、一般的なホルター心電図計測器のみで呼吸の推定を精度よく行うことができることが示された。今後は、提案心電図誘導法で計測された実際の睡眠時無呼吸症候群の患者から得られた心電図から、スクリーニングの段階で重症度を評価する手法の開発に努めたい。
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