利用三軸加速器與血氧飽和濃度透過神經網路系統來偵測呼吸中止症

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研究生：	張紘齊 Chang, Hung-Chi
論文名稱：	利用三軸加速器與血氧飽和濃度透過神經網路系統來偵測呼吸中止症 A Neural Network System for Detection of Sleep Apnea Syndrome Through Tri-axial Accelerometer and Oxygen Saturation
指導教授：	黃柏鈞 Huang, Po-Chiun
口試委員:	黃元豪 Huang, Yuan-Hao 羅友倫 Lo, Yu-Lun
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	79
中文關鍵詞：	睡眠呼吸中止症、血氧飽和濃度、睡眠淺呼吸、胸部起伏訊號、腹部起伏訊號、神經網路系統、遞歸神經網路長短期記憶型
外文關鍵詞：	Sleep Apnea, Oxygen Saturation, Hypopnea, Thoracic Movement Signal, Abdominal Movement Signal, Neural Network, LSTM-RNN
相關次數：	點閱：3 下載：0
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睡眠呼吸中止症是一種呼吸的慢性疾病，患者在睡眠時會反覆的呼吸暫時停止，在臨床上，會造成病人甦醒、嗜睡，而長時間下來則會引發高血壓等心血管疾病，嚴重甚至猝死。睡眠呼吸中止症大致上可以分成兩種：阻塞型睡眠呼吸中止症(OSA)及中樞型睡眠呼吸中止症(CSA)。另外，一樣有著嚴重影響程度的睡眠呼吸徵狀：睡眠淺呼吸(Hypopnea)，一併有以上三種睡眠呼吸中止事件。呼吸中止在醫學上定義為呼吸氣流下降超過90%以上並且持續10秒鐘，而淺呼吸則是呼吸氣流下降超過30%持續10秒鐘並伴隨3%以上血氧濃度下降或大腦甦醒。醫學上定義睡眠呼吸及淺呼吸指標(AHI)來辦別患者的嚴重程度。由於病發的時間處在睡眠，常容易導致患者忽略甚至未察覺。當今最標準檢測呼吸中止的方法是透過睡眠多項生理檢查(Polysomnography, PSG)，經由睡眠技師逐一觀察睡眠期間的各項生理訊號，來診斷整晚睡眠呼吸中止的情況。由於整個檢測流程昂貴，並且須配戴多條感測器使患者不舒服並且受環境的限制，本研究使用低成本的兩顆整合型的感測器，包含三軸加速器及心電描技術，分別黏在左胸及左腹來取得胸、腹起伏訊號以及心電圖，再搭配PSG中的血氧濃度訊號，總共三種訊號透過一個人工智慧演算法，來自動感測患者整晚睡眠的呼吸中止現象。其中，心電圖(ECG)是透過人工智慧的演發法來偵測醒睡，目的是減少AHI預估的誤差。

本研究中，兩個透過三軸加速器的訊號先經過低通濾波器過濾心跳雜訊，再經由三軸選擇器(TAA selection method)將三軸整合成單一軸的起伏變化。接下來每0.5秒鐘萃取胸腹起伏訊號的振幅比例、頻率比例和延遲後血氧濃度訊號取每段20秒中的最小值、最大值、平均值、變異數，以上8個特徵加上當下血氧濃度基準值總共9個特徵值透過長短期記憶模型類別的遞歸神經網路分類出當下不同種類的病症。最後，將此結果結合血氧濃度3%下降萃取機制(SpO2 desaturation detector)以及醒睡判別機制(Sleep-Wake classification)來提升準確度並得到最終結果。

透過記憶性模型來偵測呼吸中止症，整體的敏感度及精密度比起已往使用SVM的結果有很大的提升，最終病人嚴重程度的準確度可達89%，AHI差值的平均為5.0，而每20秒的整體準確度為92%。

Sleep Apnea-Hypopnea Syndrome (SAHS) is a respiratory chronic disease that harms body health and worsens sleep quality of patients. The disease causes complete or partial cessation of breathing while sleeping, which are known as Apnea and Hypopnea event. On clinic, it abrupt awakenings accompanied by choking which cause fluctuation of blood pressure and heart rate. Furthermore, SAHS leads to severe disease such as hypertension and cardiovascular failure. Polysomnography (PSG) is the gold standard for diagnosing SAHS. However, PSG is limited environment, uncomfortable, and time consuming. To improve these shortcomings, this thesis uses two low-cost sensor, tri-axial accelerometer and pulse oximeter, as the only biophysiological parameters for automatically screening SAHS by a self-learn artificial intelligence system.

This thesis proposed an algorithm for detecting apnea and hypopnea events by thoracic movement (THO), abdominal movement (ABD), and blood oxygen saturation levels (SpO2) signal during sleep. Besides, to obtain the apnea-hypopnea index (AHI), a neural network model is proposed for classifying sleep or wake with electrocardiography (ECG) signals and SpO2 signal as features.

First, the algorithm reconstruct two three-dimensional signals from two tri-axial accelerometers (TAA) into two one-dimensional signals which represent the thoracic (THO) and abdominal (ABD) movement, which are called TAA-THO and TAA-ABD. Second, segment TAA-THO and TAA-ABD in 10-second to extract both two features, fundamental frequency ratio and 95% quantile amplitude ratio, in every 0.5 second. On the other side, the SpO2 signal is segmented into 20-second after a 20-second delay to extract four features, minimum, maximum, mean, and variance of the first derivative. With above eight parameters and the original SpO2 index in every 0.5 second as features, the proposed algorithm use a LSTM-RNN model to classify every segment into 4 events types such as obstructive sleep apnea events (OSA), central sleep apnea event (CSA), hypopnea event (HYP), and normal event (NOR). Further, a corrective algorithm which detects SpO2 desaturation is added to the classification result. Finally, combining with the sleep-wake detector, the algorithm reports an AHI and sleep status of whole night second by second.

With the memorable and sequential characteristic in RNN model, the proposed algorithm is able to confront pathological characteristics difference between patients. According to our database of 115 subjects in CGUMH, the final AHI classification accuracy is 89.6%, and AHI difference between PSG is 5.0. The overall accuracy of detecting apnea events second by second is 92.1%, and the accuracy of classify 4 classes in every second is 83%.

The results indicates that LSTM-RNN has great potential to classify SAHS due to the similar memorable function to PSG labeling procedure.

Introduction 1
1 Sleep Apnea Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2
3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4 Study Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
5 Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . . .  7

Medical Diagnosis of Sleep Apnea Syndrome 9
1 Polysomnography Examination . . . . . . . . . . . . . . . . . . . . . . . .  9
2 Wireless Sensing Architecture . . . . . . . . . . . . . . . . . . . . . . . 11
3 Sleep Apnea Syndrome Severity and Sleep Apnea Event Types . . . . . . . .   13

Proposed Respiration Signal Processing Algorithm 23
1 Reconstructed Movement Signal from Tri-Axial Accelerometer . . . . . . . . .25
1.1 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
1.2 Trial-Axial Accelerometer Selection Algorithm . . . . . . . . . . . . . . 25
2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
2.1 Features of THO and ABD . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2 Feature of SpO 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32
3 Neural Network Model Classiﬁer . . . . . . . . . . . . . . . . . . . . . . .34
3.1 Neural Network Model for SAHS . . . . . . . . . . . . . . . . . . . . . . 34
3.2 LSTM-RNN Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35
3.3 Proposed LSTM-RNN Architecture . . . . . . . . . . . . . . . . . . . . . .37
4 Signal Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.1 Oxygen Desaturation Detection Algorithm . . . . . . . . . . . . . . . . . 42
4.2 Sleep-Wake Classiﬁcation Algorithm . . . . . . . . . . . . . . . . . . . .44
5 Assessment of Event Detection Accuracy . . . . . . . . . . . . . . . . . . .45

Simulation Result 49
1 Thoracic and Abdominal Waveform . . . . . . . . . . . . . . . . . . . . . . 49
1.1 Normal Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50
1.2 OSA Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
1.3 CSA Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
1.4 HYP Event . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2 Database and Study Design . . . . . . . . . . . . . . . . . . . . . . . . . 53
3 Support Vector Machine Classiﬁcation Performance . . . . . . . . . . . . . .54
3.1 Original Support Vector Machine Performance . . . . . . . . . . . . . . . 56
3.2 Phenotype-base Support Vector Machine Performance . . . . . . . . . . . . 59
3.3 Phenotype-base Support Vector Machine with comorbidity Performance . . . .61
4 Sleep-Wake Classiﬁcation Performance . . . . . . . . . . . . . . . . . . . .64
5 LSTM-RNN Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . .65
6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .72

Conclusion and Future Work 73
1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73
2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
                                

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