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系統識別號 U0026-2908202014463400
論文名稱(中文) 應用智慧型手機之異質性數位足跡進行躁鬱症疾患評估
論文名稱(英文) Assessment of Bipolar Disorder using Heterogeneous Data of Smartphone-based Digital Phenotyping
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 劉承叡
研究生(英文) Cheng-Ray Liou
學號 P76074711
學位類別 碩士
語文別 中文
論文頁數 60頁
口試委員 指導教授-吳宗憲
口試委員-王駿發
口試委員-林靜蘭
口試委員-戴顯權
口試委員-葉瑞峰
中文關鍵字 躁鬱症  漢氏憂鬱量表  楊氏躁症量表  異質性資料  數位足跡  智慧型手機  資料缺失  集成方法 
英文關鍵字 Bipolar disorder  Hamilton Depression Rating Scale  Young Mania Rating Scale  Heterogeneous data  Digital phenotyping  Smartphone  Missing data  Ensemble method 
學科別分類
中文摘要 在情感性疾患中,躁鬱症為現今常見的心理疾病之一。量表評估為診斷及追蹤躁鬱症的工具之一,但評估過程的人力及時間需求相當巨大,為了減少社會醫療資源的耗損,本論文透過智慧型手機遠端收集資料,且建立一個自動的量表分數預測系統,以協助臨床使用上更便利的診斷及評估。
本論文設計應用程式於智慧型手機收集使用者不同屬性的數位足跡資料,並透過這些異質性數位足跡來預測漢氏憂鬱量表及楊氏躁症量表的分數,作為躁鬱症的評估參考。本論文藉由應用程式於智慧型手機收集使用者的位置資訊(GPS)、自評量表、每日心情、睡眠時間及多媒體記錄(文字、語音及影像),以此建立一個異質性數位足跡的資料庫。
本論文方法中,將各類別的數位足跡資料進行特徵抽取後,投入模型訓練及預測量表分數,由於過往的研究沒有一套通用的模型,因此本論文選用了7種模型來做測試及比較,並組合不同數量的特徵參數類別來觀察異質性資料的效能提升與否,考量異質性資料中可能完全缺失特定類別的資料,本論文建立一個模型池及運用集成方法來生成分數,以增加系統對資料類別缺失的彈性。
本論文共收集了11位健康組與84位躁鬱症患者的異質性數位足跡,採用了5次交叉驗證做實驗評估依據。實驗結果顯示,Lasso Regression與ElasticNet Regression兩種模型的效果最為突出,且異質性資料比同質性資料擁有更好的效能,在漢氏憂鬱量表分數預測上的誤差為1.36及楊氏躁症量表的預測誤差為0.55。因長期且完整的異質性數位足跡收集困難,未來希望能收集更多的受測者及資料,能有更多的模型和前處理的選擇,也能把使用者的歷史資料作為參考,以此建立一個更加個人化且長期追蹤的系統。
英文摘要 In mental health disorder, Bipolar Disorder (BD) is one of the most common mental illness. Using scales for assessment is one of the approaches for diagnosing and tracking BD patients. However, the requirements for manpower and time is heavy in the process of evaluation. In order to reduce the cost of social and medical resources, this thesis collect the data remotely by using smartphones and build an automatic system to predict the scale score for a more convenient way to diagnosing and evaluating in clinical application.
This thesis designed an android application (App) on smartphones to collect the user’s digital phenotyping data with various categories. This thesis uses these heterogeneous digital phenotyping data to predict the score of Hamilton Depression Rating Scale (HAM-D) and Young Mania Rating Scale (YMRS), as a reference for the evaluation of BD. This thesis collect the user’s data by the App on smartphones, consisting of location data (GPS), self-report scales, daily mood, sleeping time and records of multi-media (text、speech、video), to build a database containing these heterogeneous digital phenotyping data.
First, the features of various digital phenotyping data are extracted individually, and then fed into models for training and predicting the score of scales. As there wasn’t a universal model in previous studies, this thesis picks 7 models for experimental test and comparison. Moreover, combinations of different numbers of feature categories are used to observe the improvement of performance in heterogeneous data. In order to the complete lack of certain categories in heterogeneous data, this thesis builds a model pool and uses ensemble method to predict and generate the score of scales for a more flexible system.
This study collected the heterogeneous digital phenotyping data from 84 BDs and 11 health controls. Five-fold cross validation scheme is employed for evaluation. Experimental results show that the performance of Lasso Regression and ElasticNet Regression are outstanding, and the heterogeneous data has better performance than homogeneous data. The prediction error (MAE) of HAM-D is 1.36 and the error of YMRS is 0.55. In the future, more and long-term data should be collected to make the model more robust and hopefully obtain more feasibility of models and pre-processing. In addition, the tracking of the user’s historical data can be applied to build a more personalized and long-tracking system.
論文目次 摘要 I
Abstract III
Contents V
List of Tables VIII
List of Figures IX

Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and Goal 2
1.3 Literature Review 4
1.3.1 Research of Bipolar Disorder Detection 4
1.3.2 Features 4
1.3.3 Methods and Models 5
1.3.4 Hamilton Depression Rating Scale 5
1.3.5 Young Mania Rating Scale 6
1.3.6 Digital Footprint 6
1.4 Problems 7
1.5 Proposed Method 8

Chapter 2 Database Design and Collection 10
2.1 Introduction 10
2.2 Data Collection 11
2.3 Collection Environment 12
2.4 Data Annotation 15
2.5 Overview and Difficulties 16

Chapter 3 Proposed Methods 18
3.1 Feature Extraction 19
3.1.1 Feature Extraction Process 20
3.1.2 Feature Extraction - Location Information 21
1. Number of Clusters 21
2. Entropy 23
3. Normalized Entropy 24
4. Location Variance 24
5. Home Stay 25
6. Transition Time 25
7. Total Distance 25
8. Diurnal Movement 25
9. Diurnal Movement on Normalized coordinates 26
10. Diurnal Movement on the Distance from home 26
3.1.3 Feature Extraction - Self-report Scale 27
3.1.4 Feature Extraction – Daily Mood 28
3.1.5 Feature Extraction - Multi-media 28
3.1.6 Feature Extraction - Sleep Time 29
1. Sleep Duration 29
2. Sleep Midpoint 29
3. Sleep Regularity 29
3.2 Feature Combination 31
3.3 Scale score Model Construction and Prediction 33
3.3.1 Linear Regression 33
1. Standard Linear Regression 34
2. Ridge Regression 35
3. Lasso Regression 35
4. ElasticNet Regression 36
3.3.2 Polynomial Regression 36
3.3.3 Deep Neural Network 37
3.4 Model Pool Construction 40
3.5 Ensemble Method 41

Chapter 4 Experimental Results and Discussion 43
4.1 Models Performance Analysis 43
4.2 Heterogeneous Data Performance Analysis 44
4.3 Feature Analysis of Heterogeneous Data 45
4.4 Performance of Ensemble Method 49

Chapter 5 Conclusion and Future Work 52

Appendix 54

Depression Anxiety Stress Scales-21 54

Altman Self-Rating Mania Scale 54

Reference 55
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