| 姓名 |
林伯翰(Bo-Han Lin)
查詢紙本館藏 |
畢業系所 |
生醫科學與工程學系 |
| 論文名稱 |
設計具低功耗無線傳輸及結合人工智慧判讀之長時間聽診監測系統
(Design a long-term auscultation monitoring system with low-power wireless transmission and artificial intelligence interpretation)
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| 相關論文 | |
| 檔案 |
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[Bibtex 格式]
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| 摘要(中) |
在傳統心臟聽診檢查心血管疾病中(瓣膜狹窄、閉鎖不全)，主要是由醫生個人的經驗和較為主觀的判斷，在許多的診斷中較難討論出一個客觀的標準。因此本研究提出了一款可攜式的數位聽診工具，藉由將心音訊號數位化，完整呈現生理訊號的聲音、另一面將所儲存的生理訊號分析，可藉由機器學習，和資料庫中的類似案例做比對，提供一個客觀輔助診斷。
系統主要可分為類比電路和數位訊號處理這兩個部分。
其中類比電路部分包含兩組不同頻段的二階帶通濾波器(分別是心音訊號 : 20~1k Hz和動靜脈簍管音訊號 : 50~1k Hz)，再結合一顆全頻段的麥克風以及機構設計自製的聽診頭。
當前端收完訊號後，類比數位轉換器把前端的訊號經過微處理器程式編程下在穩定不掉點的狀態下能夠以6.4 kHz取樣頻率做訊號擷取，再利用超低功耗藍芽同步傳輸data到手機端做及時的生理訊號監測，同時把訊號透過音頻放大電路的優化，讓過濾掉雜訊的生理訊號得以在耳機清楚的監聽，最後將raw data儲存在外掛的 Micro SD卡模組，方便在電腦端進行計算與分析，從聽診頭、類比電路設計到微處理器的程式設計，訊噪比可以達到 : 50.6(dB)，使得到的生理訊號能夠作為較客觀並完整的診斷。
本裝置除了能以高解析數位化方式來錄製量測到的生理音，還有手機APP即時監測訊號以及超低功耗、體積小、便於攜帶操作等優點。未來之應用不僅僅只限於大型醫院或加護病房等機構，也可普及於小型醫療機構、戶外急救單位甚至是個人居家，實現即時且普及的生理訊號監測。 |
| 摘要(英) |
In the traditional auscultation examination for cardiovascular diseases (valvular stenosis, atresia), the doctor′s personal experience and analysis is a diagnostic criterion that is difficult to discuss in this diagnosis. Therefore, the study puts forward its own diagnostic criteria. This is a portable digital auscultation tool. The data consists of digitizing the heart sound signal to fully present the sound of the physical signal. On the other hand, it analyzes the stored rational signal, which can be compared with similar cases in the library by machine learning, and provides Auxiliary diagnosis.
The system can be divided into two parts: analog circuit and digital signal processing.
A second-order band communication with different sounds than the circuit part (respectively the heart sound signal : 20~1k Hz and the rattle tube sound signal: 50~1k Hz), combined with a full series of themes and the organization design itself Auscultation head.
After the front end receives the signal, the analog-to-digital converter converts the front end signal. It can use 6.4 kHz micro signal for signal collection in the state of drop, and then use the ultra-low standby Bluetooth to synchronously transmit data to the mobile phone for timely physiological signal monitoring, and at the same time filter the signal through audio amplification The circuit is optimized. The physiological signals that drop the noise can be monitored on the earphones. Finally, the original data is saved in the external micro SD card module, which is convenient for calculation and analysis on the computer side, from the auscultation head, analog circuit design to program design, the communication establishment ratio can reach: 50.6 (dB), write your own physiological communication number as a larger scale and complete diagnosis.
In addition to being able to capture and measure physiological sounds in a high-resolution digital way, this device has the advantages of real-time signal monitoring via APP, time-consuming, small size, and portable operation. Or institutions such as wards, small medical institutions for patients, out-call rescue units, and even personal home communications, to achieve immediate and unenhanced physiological number monitoring. |
| 關鍵字(中) |
★ 生理音
★ 低功耗
★ BLE
★ APP即時監測 |
關鍵字(英) |
★ Physiological sound
★ low power consumption
★ BLE
★ APP real-time monitoring |
| 論文目次 |
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
圖目錄 vi
一、 緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 本文架構 2
二、 研究原理 3
2.1 心音 3
2.1.1 心音的訊號 3
2.1.2 心音的量測方法 6
2.2 末期腎臟病 7
2.2.1 動靜脈簍管音的訊號 7
2.2.2 動靜脈簍管音的量測方法 10
三、 研究方法介紹 12
3.1 前端類比電路 12
3.1.1 前端拾音頭設計 12
3.1.2 電容式麥克風 15
3.1.3 類比濾波電路 16
3.1.4 偏置電壓電路 22
3.1.5 音源輸出電路 23
3.1.6 電源管理設計 25
3.2 數位控制電路 38
3.2.1 微處理器 38
3.2.2 類比數位轉換器 39
3.2.3 Micro SD 卡儲存 41
3.2.4 低功耗藍芽傳輸 43
3.2.5 電路板layout 47
3.3 3D 列印建模與設計 53
3.3.1 電腦繪圖 53
3.3.2 3D 列印切片、製作 55
3.4 電腦數據分析 57
3.4.1 資料編碼及解碼 57
3.4.2 心音訊號擷取 58
3.4.3 心音特徵抓取分類 64
四、 實驗結果分析 68
4.1 類比電路驗證 68
4.2 數位電路驗證 71
4.3 全系統驗證及分析 72
五、 結論與未來展望 73
5.1 結論 73
5.2 未來展望 73
參考文獻 74 |
| 參考文獻 |
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[2] Daisuke Higashi, Keisuke Nishijima, Ken’ichi Furuya, Keiko Tanakay, Satoko Shiny. “Classification of Shunt Murmurs for Diagnosis of Arteriovenous Fistula Stenosis”.
DOI：10.23919/APSIPA.2018.8659641
[3] Raj C Thiagarajan, Prema Sankaran, Abdul Hafeez Baig, Kodeeswari Elangovan1, Raj Gururajan. “Acoustical Design of Digital Stethoscope for Improved Performance”.
[4] Chengyu Liu, David B. Springer, Benjamin Moody, Ricardo C. Abad. “An open access database for the evaluation of heart sound algorithms”.
[5] Hsien-Yi Wang, Cho-Han Wu, Chien-Yue Chen, and Bor-Shyh Lin. “Novel Noninvasive Approach for Detecting Arteriovenous Fistula Stenosis”.
DOI：10.1109/TBME.2014.2308906
[6] Kun-Hsi Tsai, Wei-Chien Wang, Chui-Hsuan Cheng, Chan-Yen Tsai, Jou-Kou Wang,
Tzu-Hao Lin, Shih-Hau Fang, Li-Chin Chen, and Yu Tsao. “Blind Monaural Source Separation on Heart and Lung Sounds Based on Periodic-Coded Deep Autoencoder”.
[7] Zümray Dokur, Tamer Ölmez. “Heart sound classification using wavelet transform and incremental self-organizing map”.
DOI : 10.1016/j.dsp.2008.06.001
[8] Valerie Rennoll, Ian McLane, Dimitra Emmanouilidou, James West, “Mounya Elhilali. Electronic Stethoscope Filtering Mimics the Perceived Sound Characteristics of Acoustic Stethoscope”.
DOI：10.1109/JBHI.2020.3020494
[9] Anton A. Shkel, Eun Sok Kim. “Continuous Health Monitoring With Resonant-Microphone-Array-Based Wearable Stethoscope”.
DOI：10.1109/JSEN.2019.2900713
[10] Hemant Kumar Tiwari, Ashish Harsola. “Development of Embedded Stethoscope for Heart Sound”.
DOI：10.1109/WiSPNET.2016.7566396 |
| 指導教授 |
林澂
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審核日期 |
2021-10-27 |
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