| Project/Area Number |
16500299
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Biomedical engineering/Biological material science
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| Research Institution | Hokkaido Tokai University |
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Principal Investigator |
OKAMOTO Eiji Hokkaido Tokai University, Dept of Information Science, Professor, 工学部, 教授 (30240633)
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| Co-Investigator(Kenkyū-buntansha) |
MITAMURA Yoshinori Hokkaido University, Graduate School of information, Professor, 大学院情報科学研究科, 教授 (70002110)
YOSHHIZAWA Makoto Tohoku University, Information synergy center, Professor, 情報シナジーセンター, 教授 (60166931)
YAMBE Tomoyuki Tohoku University, Inst. of aging and cancer, Professor, 加齢医学研究所, 教授 (70241578)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2004: ¥1,900,000 (Direct Cost: ¥1,900,000)
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| Keywords | artificial heart / ventricular assist device / fuzzy control / autonomous system / implantable battery / information transmission / early diagnosis / lithium polymer / FPGA / コンプライアンスチャンバ / 自己診断 / ニューラルネットワーク / 補助循環 / 補助人工心臓 / ファージ推論 / 適応ノイズフィルタ / ファージ制御 / 人工知能 |
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| Research Abstract |
Basic study for realization of adaptation, evolution, and self-diagnosis on an artificial heart has been carried out by an autonomous distributed control system for an ultra-lightweight pulsatile VAD (volume of 285 ml and weight of 360 g). The autonomous distributed control system was built by 4 layers (input layer, measurement layer, control layer, output layer) and an object manager, which was realized on a lchip microcomputer (Hitachi, SH7044, Japan). The control layer consists of 3 autonomous objects: position control, velocity control, and current control. The position and velocity control objects are constructed by fuzzy inference algorism in order to build doctors' and engineers' knowledge into the VAD control. The object manager paces events on each object in order to maintain time series processes.
In vitro experiments showed that The VAD has maximum pump outflow of 5.1 L/min against afterload of 100 mmHg under control of our autonomous control system.
The self-diagnosis has been realized by a neural network based early diagnosis system using artificial heart sound. A long-term animal experiment showed that the early diagnosis system automatically and successfully had detected malfunction of pump 24 hours before pump stoppage.
The above results show that our VAD system promises possibility as a new superior artificial heart system.
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