面向可穿戴式的基于LSTM神經(jīng)網(wǎng)絡(luò)的智能心音異常診斷芯片
doi: 10.11999/JEIT230934
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1.
中國科學(xué)院半導(dǎo)體研究所 北京 100083
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2.
中國科學(xué)院大學(xué)集成電路學(xué)院 北京 100049
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3.
中國農(nóng)業(yè)大學(xué) 北京 100083
Intelligent Heart Sound Abnormal Diagnosis Chip Based on LSTM for Wearable Applications
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1.
Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
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2.
School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China
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3.
China Agricultural University, Beijing 100083, China
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摘要: 心血管疾病是造成全球死亡人數(shù)最多的疾病之一,因此對心血管疾病的預(yù)防與提前診斷至關(guān)重要。人工聽診技術(shù)與計算機心音診斷技術(shù)無法滿足對心音長時間聽診的需求,因而可穿戴式聽診設(shè)備越來越受到關(guān)注,但是其具有高精度與低功耗的要求。該文設(shè)計了低功耗的面向可穿戴式的基于長短期記憶網(wǎng)絡(luò)(Long Short-Term Memory, LSTM)的智能心音異常診斷芯片,提出了包括預(yù)處理、特征提取以及異常診斷的心音異常診斷系統(tǒng),并搭建了基于聽診器的心音采集FPGA系統(tǒng),采用了數(shù)據(jù)增強的方法解決數(shù)據(jù)集的不平衡問題。基于預(yù)訓(xùn)練模型設(shè)計了智能心音異常診斷芯片,在SMIC180 nm工藝下完成了版圖設(shè)計和MPW流片。后仿真結(jié)果表明,智能心音異常診斷芯片的診斷準(zhǔn)確率為98.6%,功耗為762 μW,面積為3.06 mm × 2.45 mm,滿足可穿戴式智能心音異常診斷設(shè)備的高性能與低功耗的需求。
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關(guān)鍵詞:
- 可穿戴式 /
- 心音 /
- 異常診斷 /
- 長短期記憶網(wǎng)絡(luò) /
- 低功耗
Abstract: The gravity of cardiovascular disease hazards necessitates the utmost importance of preventive measures and early diagnosis for such ailments. Conventional manual auscultation techniques and computer-based diagnostic methods prove inadequate in meeting the demands of auscultation. Consequently, wearable devices attract increasing attention, but they are required to obtain both a high accuracy and low-power consumption. An intelligent heart sound abnormal diagnostic chip based on LSTM for wearable applications is presented. The abnormal heart sound diagnostic system is developed, including preprocessing, feature extraction, and abnormal diagnosis. Furthermore, an FPGA-based system for heart sounds acquisition is constructed. The challenge of imbalanced datasets is addressed through the implementation of data augmentation techniques. By utilizing pre-trained model as a foundation, the intelligent heart sound abnormal diagnostic chip is developed, and the layout and MPW are finished under SMIC 180nm. The post-simulation results demonstrate that the chip achieves a diagnostic accuracy of 98.6%, a power consumption of 762 μW, and an area of 3.06 mm$ \times $2.45 mm, meeting the high-performance and low-power consumption prerequisites of wearable devices.-
Key words:
- Wearable applications /
- Heart sound /
- Abnormal diagnosis /
- LSTM /
- Low-power
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表 1 心音診斷模型訓(xùn)練參數(shù)
訓(xùn)練參數(shù) 值 框架 Pytorch GPU型號 TITAN5 12 GB EPOCH 300 BATCH SIZE 32 學(xué)習(xí)率 0.0001 優(yōu)化器 Adam 下載: 導(dǎo)出CSV
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