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研究生:馬貝庫
研究生(外文):Bhekumuzi Mathunjwa
論文名稱:Pain evaluation using analgesia nociception index (ANI) and artificial neural network during surgical operation
論文名稱(外文):Pain evaluation using analgesia nociception index (ANI) and artificial neural network during surgical operation
指導教授:謝建興 教授
指導教授(外文):Prof. Jiann-Shing Shieh
口試委員:范守仁徐業良
口試委員(外文):Shou-Zen FanYeh-Liang Hsu
口試日期:29-01-2018
學位類別:碩士
校院名稱:元智大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2018
畢業學年度:106
語文別:英文
論文頁數:31
中文關鍵詞:Electrocardiogram (ECG)Analgesia nociception index (ANI)Surgical operationArtificial neural network (ANN)
外文關鍵詞:Electrocardiogram (ECG)Analgesia nociception index (ANI)Surgical operationArtificial neural network (ANN)
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Background: The International Association for the Study of Pain (IASP), describes the pain as an unpleasant sensory and emotional experience associated with actual or potential tissue damage. Pain is a system of defense mechanism that prompts the body to avoid the source of noxious stimulation and a potential area of tissue damage. Electrocardiogram (ECG) is the electrical activity of the heart. ECG gives the transthoracic interpretation of the electrical activity of the heart over a period of time. Analysis of ECG signal provides information regarding the condition of heart at a given moment. This thesis is aimed to challenge the ability of ANI to detect noxious stimulation during a surgical operation.
Methods: ECG signals were collected from 30 patients aged between 20 to 80 years old that undergone a general anesthesia surgery at the National Taiwan University Hospital (NTUH). RR series was calculated from the ECG and then re-sampled at 8 Hz using a linear interpolation. RRi samples were isolated into a 64 sec moving window and normalized for patient comparability. ANI is calculated from the enveloped area between the local minima and local maxima of the RR series. Artificial neural network (ANN) models including recurrent neural network (RNN) and nonlinear autoregressive neural network (NARX) were applied to predict pain using ANI and Doctors assessment of pain during the surgery.
Results: The results show ANI mean±SD of 40.77±9.99 before the surgery began, 44.62±10.69 after the surgery, 39.52±8.36 during drug administration, 30.39±7.50 during intubation, 24.26±7.04 during electric knife incision and 34.47±8.62 during stitching. The mean and standard deviation for the period of the surgery is 154±62.64 min. Predictive results for training ANN, RNN and NARX gave a mean absolute error of 3.9564, 3.163 and 3.808 respectively.
Conclusion: This paper presents the application of ANI index to evaluate pain during a surgical operation. The clinical study presented in this thesis indicates that a more painful situation in the surgical operation is associated with a decreased ANI index, confirming that ANI monitoring as a tool capable of measuring a change in the level of pain during surgery. Predicting pain using artificial neural network models was not successful and we aim at applying deep learning in the future.
Background: The International Association for the Study of Pain (IASP), describes the pain as an unpleasant sensory and emotional experience associated with actual or potential tissue damage. Pain is a system of defense mechanism that prompts the body to avoid the source of noxious stimulation and a potential area of tissue damage. Electrocardiogram (ECG) is the electrical activity of the heart. ECG gives the transthoracic interpretation of the electrical activity of the heart over a period of time. Analysis of ECG signal provides information regarding the condition of heart at a given moment. This thesis is aimed to challenge the ability of ANI to detect noxious stimulation during a surgical operation.
Methods: ECG signals were collected from 30 patients aged between 20 to 80 years old that undergone a general anesthesia surgery at the National Taiwan University Hospital (NTUH). RR series was calculated from the ECG and then re-sampled at 8 Hz using a linear interpolation. RRi samples were isolated into a 64 sec moving window and normalized for patient comparability. ANI is calculated from the enveloped area between the local minima and local maxima of the RR series. Artificial neural network (ANN) models including recurrent neural network (RNN) and nonlinear autoregressive neural network (NARX) were applied to predict pain using ANI and Doctors assessment of pain during the surgery.
Results: The results show ANI mean±SD of 40.77±9.99 before the surgery began, 44.62±10.69 after the surgery, 39.52±8.36 during drug administration, 30.39±7.50 during intubation, 24.26±7.04 during electric knife incision and 34.47±8.62 during stitching. The mean and standard deviation for the period of the surgery is 154±62.64 min. Predictive results for training ANN, RNN and NARX gave a mean absolute error of 3.9564, 3.163 and 3.808 respectively.
Conclusion: This paper presents the application of ANI index to evaluate pain during a surgical operation. The clinical study presented in this thesis indicates that a more painful situation in the surgical operation is associated with a decreased ANI index, confirming that ANI monitoring as a tool capable of measuring a change in the level of pain during surgery. Predicting pain using artificial neural network models was not successful and we aim at applying deep learning in the future.
A Thesis i
Abstract ii
Acknowledgements iv
Contents v
List of tables vii
List of Figures viii
Abbreviation ix
Chapter 1: Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Purpose 5
1.4 Brief Summary of Chapters 5
Chapter 2: General Anesthesia 6
2.1 General Anesthesia 6
2.2 Anesthesia Process 6
Chapter 3: Analysis Algorithm 8
3.1 ANI computation 8
3.2 RR samples, windowing, normalization and filtering 8
3.3 Area under the curve (AUC) parameter computation 9
3.4 Artificial neural network (ANN) 10
3.5 Recurrent neural network (RNN) 11
3.6 Nonlinear autoregressive neural network (NARX) 12
Chapter 4: Experiment Condition and Method 14
4.1 Data source 14
4.2 Expert Assessment of Pain Level 17
Chapter 5: Results 19
5.1 States of analgesia / nociception during general anaesthesia 19
5.2 ANI Analysis 19
5.3 Prediction results 21
Chapter 6: Conclusion and future work 27
References 28
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