A 65-nm CMOS Low-Power Impulse Radar System for Human Respiratory Feature Extraction and Diagnosis on Respiratory Diseases

被引:18
作者
Tseng, Shao-Ting [1 ]
Kao, Yu-Hsien [1 ]
Peng, Chun-Chieh [1 ]
Liu, Jinn-Yann [1 ]
Chu, Shao-Chang [1 ]
Hong, Guo-Feng [1 ,2 ]
Hsieh, Chi-Hsuan [1 ]
Hsu, Kung-Tuo [1 ,3 ]
Liu, Wen-Te [4 ,5 ,6 ,7 ,8 ]
Huang, Yuan-Hao [1 ]
Huang, Shi-Yu [1 ]
Chu, Ta-Shun [1 ]
机构
[1] Natl Tsing Hua Univ, Dept Elect Engn, Hsinchu 300, Taiwan
[2] Mediatek, Hsinchu 300, Taiwan
[3] ASolid Technol Co Ltd, Hsinchu 300, Taiwan
[4] Taipei Med Univ, Shuang Ho Hosp, Dept Internal Med, Div Pulm Med, New Taipei 235, Taiwan
[5] Taipei Med Univ, Sch Resp Therapy, Coll Med, Taipei 110, Taiwan
[6] Natl Cheng Kung Univ, Dept Engn Sci, Tainan 701, Taiwan
[7] Taipei Med Univ, Taipei Med Univ Hosp, Sleep Res Ctr, Taipei 110, Taiwan
[8] Taipei Med Univ, Shuang Ho Hosp, Sleep Ctr, New Taipei 235, Taiwan
关键词
Biomedical applications; CMOS; digital signal processing (DSP); radar systems; sensors; TRANSCEIVER; TRACK;
D O I
10.1109/TMTT.2016.2536029
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper presents a radar system for extracting human respiratory features. The proposed radar chip comprises three major components: a digital-to-time converter (DTC), a transmitter, and a receiver. The all-digital standard cell-based DTC achieves a timing resolution of 10 ps on a 100-ns time scale, supporting a range-gated sensing process. The transmitter is composed of a digital pulse generator. The receiver comprises a direct-sampling passive frontend for achieving high linearity, an integrator for enhancing the signal-to-noise ratio, and a successive approximation register analog-to-digital converter for signal quantization. A fully integrated CMOS impulse radar chip was fabricated using 65-nm CMOS technology, and the total power consumption is 21 mW. In the backend, a real-time digital signal-processing platform captures human respiratory waveforms via the radar chip and processes the waveforms by applying a human respiratory feature extraction algorithm. Furthermore, a clinical trial was conducted for establishing a new diagnosis workflow for identifying respiratory diseases by the proposed wireless sensor system. The proposed system was validated by applying an adaptive network-based fuzzy inference system and support vector machine algorithm to the clinical trial results. These algorithms confirmed the effectiveness of the proposed system in diagnosing respiratory diseases.
引用
收藏
页码:1029 / 1041
页数:13
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