Clinical Decision Support Systems with Game-based Environments, Monitoring Symptoms of Parkinson’s Disease with Exergames

Parkinson’s Disease (PD) is a malady caused by progressive neuronal degeneration, deriving in several physical and cognitive symptoms that worsen with time. Like many other chronic diseases, it requires constant monitoring to perform medication and therapeutic adjustments. This is due to the significant variability in PD symptomatology and progress between patients. At the moment, this monitoring requires substantial participation from caregivers and numerous clinic visits. Personal diaries and questionnaires are used as data sources for medication and therapeutic adjustments. The subjectivity in these data sources leads to suboptimal clinical decisions. Therefore, more objective data sources are required to better monitor the progress of individual PD patients. A potential contribution towards more objective monitoring of PD is clinical decision support systems. These systems employ sensors and classification techniques to provide caregivers with objective information for their decision-making. This leads to more objective assessments of patient improvement or deterioration, resulting in better adjusted medication and therapeutic plans. Hereby, the need to encourage patients to actively and regularly provide data for remote monitoring remains a significant challenge. To address this challenge, the goal of this thesis is to combine clinical decision support systems with game-based environments. More specifically, serious games in the form of exergames, active video games that involve physical exercise, shall be used to deliver objective data for PD monitoring and therapy. Exergames increase engagement while combining physical and cognitive tasks. This combination, known as dual-tasking, has been proven to improve rehabilitation outcomes in PD: recent randomized clinical trials on exergame-based rehabilitation in PD show improvements in clinical outcomes that are equal or superior to those of traditional rehabilitation. In this thesis, we present an exergame-based clinical decision support system model to monitor symptoms of PD. This model provides both objective information on PD symptoms and an engaging environment for the patients. The model is elaborated, prototypically implemented and validated in the context of two of the most prominent symptoms of PD: (1) balance and gait, as well as (2) hand tremor and slowness of movement (bradykinesia). While balance and gait affections increase the risk of falling, hand tremors and bradykinesia affect hand dexterity. We employ Wii Balance Boards and Leap Motion sensors, and digitalize aspects of current clinical standards used to assess PD symptoms. In addition, we present two dual-tasking exergames: PDDanceCity for balance and gait, and PDPuzzleTable for tremor and bradykinesia. We evaluate the capability of our system for assessing the risk of falling and the severity of tremor in comparison with clinical standards. We also explore the statistical significance and effect size of the data we collect from PD patients and healthy controls. We demonstrate that the presented approach can predict an increased risk of falling and estimate tremor severity. Also, the target population shows a good acceptance of PDDanceCity and PDPuzzleTable. In summary, our results indicate a clear feasibility to implement this system for PD. Nevertheless, long-term randomized clinical trials are required to evaluate the potential of PDDanceCity and PDPuzzleTable for physical and cognitive rehabilitation effects

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using Electrophysiological and Kinematic Activity

As virtual reality (VR) technology continues to gain prominence in commercial, educational, recreational and research applications, there is increasing interest in incorporating physiological sensors in VR devices for passive user-state monitoring to eventually increase the sense of immersion. By recording physiological signals such as the electroencephalogram (EEG), electromyography (EMG) or kinematic parameters during the use of a VR device, the user’s interactions in the virtual environment could be adapted in real time based on the user’s cognitive state. This dissertation evaluates the feasibility of passively monitoring cognitive workload via electrophysiological and kinematic activity while performing a classical n-back task in an interactive VR environment. The results indicate that scalp measurements of electrical activity and controller and headset tracking of kinematic activity can effectively discriminate three workload levels. Since motion and muscle tension can create co-varying task-related artifacts in EEG sensors mounted to the VR headset, decontamination algorithms were developed. The newly developed warp correlation filter (WCF) and linear regression denoising were applied on EEG, which could significantly decrease the influence of these artifacts. Analysis of the scalp recorded spectrum suggest two transient activity (termed pulse-decay effects) that impact feature extraction, modeling, and overall interpretation of workload estimation from scalp recordings. The best classification accuracy could be achieved by combining EMG, EEG and kinematic activity features using an artificial neural network (ANN)

Masks and tubes used to support the neonatal airway – how to improve their fit, seal and correct placement

Despite the many changes in perinatal medicine in the last fifty years, infants still often and unpredictably need assistance with their breathing. Positive pressure delivered through a facemask remains the almost universal initial approach. This is then generally followed by endotracheal intubation if the infant is not responding or if prolonged support is needed. Despite many years of research into mask ventilation, it is still very challenging and leak and airway obstruction remain a problem. The thesis opens with two mask studies that try to solve this problem. The first is a manikin study that compared three different mask holds. It unfortunately found that there was no difference in the mask leak measured using the different holds. It is perhaps reasonable to change holds if the baby isn’t responding as expected. The second study aimed to measure the dimensions of preterm infants’ faces and compare these with the size of the most commonly available face masks. It found that the smallest size of some brands of mask is too large for many preterm infants. Masks of 35mm diameter are suitable for infants <29 weeks PMA or 1000g. Masks of 42 mm diameter are suitable for infants 27-33 weeks PMA or 750-2500g. The thesis then changed focus to neonatal intubation. Intubation is a challenging skill for paediatric trainees to master. In recent years success rates are decreasing. The next studies look at possible ways to change this trend. The first is a Cochrane review that examined if a stylet could improve intubation success. Only one unblended RCT has been performed and found no difference. The most sizable work of the thesis follows and is a RCT that examines if junior trainees intubation success rates are superior if they intubate with a videolaryngoscope. Two hundred and six intubations were randomised to the screen being visible to the supervisor or covered. The success rate when the instructor was able to view the videolaryngoscope screen was 66% (69/104) compared to 41% (42/102) when the screen was covered, (p<0.001), OR 2.81 (95%CI 1.54-5.17). This shows that videolaryngoscopy is a promising tool to help inexperienced trainees become proficient intubators. This study has resulted in videolaryngoscopy becoming a tool commonly used in neonatal intensive cares. The next study looks at recordings of unsuccessful intubations from the RCT. If an attempt is unsuccessful, the intubator and instructor often cannot explain why making it difficult to know what to do differently in the future. The study found that lack of intubation success was most commonly due to failure to recognize midline anatomical structures. Excessive secretions are rarely a factor in elective premeditated and routine suctioning should be discouraged. Better videolaryngoscope blade design may make it easier to direct the tube through the vocal cords. The final work of the thesis is a review that examines devices used during newborn stabilization. Evidence for their use to optimize the thermal, respiratory and cardiovascular management in the delivery room is presented. After completing all this work I think that perhaps it is time to lessen our reliance on facemasks and embrace other airway devices that are showing promise, particularly the laryngeal mask. I feel that universal intubation competency is no longer feasible but universal competency on the use of laryngeal masks probably is. This urgently needs to be addressed in paediatric training programs. Videolaryngoscopy is a promising tool that improves junior intubators’ success rates. To master intubation many intubations are still necessary but the videolaryngoscope allows the slope of the learning curve to steepen. Development is necessary to design scopes of the future that are inexpensive, easily portable and user friendly

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

시계열 데이터 패턴 분석을 위한 종단 심층 학습망 설계 방법론

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2019. 2. 장병탁.Pattern recognition within time series data became an important avenue of research in artificial intelligence following the paradigm shift of the fourth industrial revolution. A number of studies related to this have been conducted over the past few years, and research using deep learning techniques are becoming increasingly popular. Due to the nonstationary, nonlinear and noisy nature of time series data, it is essential to design an appropriate model to extract its significant features for pattern recognition. This dissertation not only discusses the study of pattern recognition using various hand-crafted feature engineering techniques using physiological time series signals, but also suggests an end-to-end deep learning design methodology without any feature engineering. Time series signal can be classified into signals having periodic and non-periodic characteristics in the time domain. This thesis proposes two end-to-end deep learning design methodologies for pattern recognition of periodic and non-periodic signals. The first proposed deep learning design methodology is Deep ECGNet. Deep ECGNet offers a design scheme for an end-to-end deep learning model using periodic characteristics of Electrocardiogram (ECG) signals. ECG, recorded from the electrophysiologic patterns of heart muscle during heartbeat, could be a promising candidate to provide a biomarker to estimate event-based stress level. Conventionally, the beat-to-beat alternations, heart rate variability (HRV), from ECG have been utilized to monitor the mental stress status as well as the mortality of cardiac patients. These HRV parameters have the disadvantage of having a 5-minute measurement period. In this thesis, human's stress states were estimated without special hand-crafted feature engineering using only 10-second interval data with the deep learning model. The design methodology of this model incorporates the periodic characteristics of the ECG signal into the model. The main parameters of 1D CNNs and RNNs reflecting the periodic characteristics of ECG were updated corresponding to the stress states. The experimental results proved that the proposed method yielded better performance than those of the existing HRV parameter extraction methods and spectrogram methods. The second proposed methodology is an automatic end-to-end deep learning design methodology using Bayesian optimization for non-periodic signals. Electroencephalogram (EEG) is elicited from the central nervous system (CNS) to yield genuine emotional states, even at the unconscious level. Due to the low signal-to-noise ratio (SNR) of EEG signals, spectral analysis in frequency domain has been conventionally applied to EEG studies. As a general methodology, EEG signals are filtered into several frequency bands using Fourier or wavelet analyses and these band features are then fed into a classifier. This thesis proposes an end-to-end deep learning automatic design method using optimization techniques without this basic feature engineering. Bayesian optimization is a popular optimization technique for machine learning to optimize model hyperparameters. It is often used in optimization problems to evaluate expensive black box functions. In this thesis, we propose a method to perform whole model hyperparameters and structural optimization by using 1D CNNs and RNNs as basic deep learning models and Bayesian optimization. In this way, this thesis proposes the Deep EEGNet model as a method to discriminate human emotional states from EEG signals. Experimental results proved that the proposed method showed better performance than that of conventional method based on the conventional band power feature method. In conclusion, this thesis has proposed several methodologies for time series pattern recognition problems from the feature engineering-based conventional methods to the end-to-end deep learning design methodologies with only raw time series signals. Experimental results showed that the proposed methodologies can be effectively applied to pattern recognition problems using time series data.시계열 데이터의 패턴 인식 문제는 4차 산업 혁명의 패러다임 전환과 함께 매우 중요한 인공 지능의 한 분야가 되었다. 이에 따라, 지난 몇 년간 이와 관련된 많은 연구들이 이루어져 왔으며, 최근에는 심층 학습망 (deep learning networks) 모델을 이용한 연구들이 주를 이루어 왔다. 시계열 데이터는 비정상, 비선형 그리고 잡음 (nonstationary, nonlinear and noisy) 특성으로 인하여 시계열 데이터의 패턴 인식 수행을 위해선, 데이터의 주요한 특징점을 추출하기 위한 최적화된 모델의 설계가 필수적이다. 본 논문은 대표적인 시계열 데이터인 생체 신호를 사용하여 여러 특징 벡터 추출 방법 (hand-crafted feature engineering methods)을 이용한 패턴 인식 기법에 대하여 논할 뿐만 아니라, 궁극적으로는 특징 벡터 추출 과정이 없는 종단 심층 학습망 설계 방법론에 대한 연구 내용을 담고 있다. 시계열 신호는 시간 축 상에서 크게 주기적 신호와 비주기적 신호로 구분할 수 있는데, 본 연구는 이러한 두 유형의 신호들에 대한 패턴 인식을 위해 두 가지 종단 심층 학습망에 대한 설계 방법론을 제안한다. 첫 번째 제안된 방법론을 이용해 설계된 모델은 신호의 주기적 특성을 이용한 Deep ECGNet이다. 심장 근육의 전기 생리학적 패턴으로부터 기록된 심전도 (Electrocardiogram, ECG)는 이벤트 기반 스트레스 수준을 추정하기 위한 척도 (bio marker)를 제공하는 유효한 데이터가 될 수 있다. 전통적으로 심전도의 심박수 변동성 (Herat Rate Variability, HRV) 매개변수 (parameter)는 심장 질환 환자의 정신적 스트레스 상태 및 사망률을 모니터링하는 데 사용되었다. 하지만, 표준 심박수 변동성 매개 변수는 측정 주기가 5분 이상으로, 측정 시간이 길다는 단점이 있다. 본 논문에서는 심층 학습망 모델을 이용하여 10초 간격의 ECG 데이터만을 이용하여, 추가적인 특징 벡터의 추출 과정 없이 인간의 스트레스 상태를 인식할 수 있음을 보인다. 제안된 설계 기법은 ECG 신호의 주기적 특성을 모델에 반영하였는데, ECG의 은닉 특징 추출기로 사용된 1D CNNs 및 RNNs 모델의 주요 매개 변수에 주기적 특성을 반영함으로써, 한 주기 신호의 스트레스 상태에 따른 주요 특징점을 종단 학습망 내부적으로 추출할 수 있음을 보였다. 실험 결과 제안된 방법이 기존 심박수 변동성 매개변수와 spectrogram 추출 기법 기반의 패턴 인식 방법보다 좋은 성능을 나타내고 있음을 확인할 수 있었다. 두 번째 제안된 방법론은 비 주기적이며 비정상, 비선형 그리고 잡음 특성을 지닌 신호의 패턴인식을 위한 최적 종단 심층 학습망 자동 설계 방법론이다. 뇌파 신호 (Electroencephalogram, EEG)는 중추 신경계 (CNS)에서 발생되어 무의식 상태에서도 본연의 감정 상태를 나타내는데, EEG 신호의 낮은 신호 대 잡음비 (SNR)로 인해 뇌파를 이용한 감정 상태 판정을 위해서 주로 주파수 영역의 스펙트럼 분석이 뇌파 연구에 적용되어 왔다. 통상적으로 뇌파 신호는 푸리에 (Fourier) 또는 웨이블렛 (wavelet) 분석을 사용하여 여러 주파수 대역으로 필터링 된다. 이렇게 추출된 주파수 특징 벡터는 보통 얕은 학습 분류기 (shallow machine learning classifier)의 입력으로 사용되어 패턴 인식을 수행하게 된다. 본 논문에서는 이러한 기본적인 특징 벡터 추출 과정이 없는 베이지안 최적화 (Bayesian optimization) 기법을 이용한 종단 심층 학습망 자동 설계 기법을 제안한다. 베이지안 최적화 기법은 초 매개변수 (hyperparamters)를 최적화하기 위한 기계 학습 분야의 대표적인 최적화 기법인데, 최적화 과정에서 평가 시간이 많이 소요되는 목적 함수 (expensive black box function)를 갖고 있는 최적화 문제에 적합하다. 이러한 베이지안 최적화를 이용하여 기본적인 학습 모델인 1D CNNs 및 RNNs의 전체 모델의 초 매개변수 및 구조적 최적화를 수행하는 방법을 제안하였으며, 제안된 방법론을 바탕으로 Deep EEGNet이라는 인간의 감정상태를 판별할 수 있는 모델을 제안하였다. 여러 실험을 통해 제안된 모델이 기존의 주파수 특징 벡터 (band power feature) 추출 기법 기반의 전통적인 감정 패턴 인식 방법보다 좋은 성능을 나타내고 있음을 확인할 수 있었다. 결론적으로 본 논문은 시계열 데이터를 이용한 패턴 인식문제를 여러 특징 벡터 추출 기법 기반의 전통적인 방법을 통해 설계하는 방법부터, 추가적인 특징 벡터 추출 과정 없이 원본 데이터만을 이용하여 종단 심층 학습망을 설계하는 방법까지 제안하였다. 또한, 다양한 실험을 통해 제안된 방법론이 시계열 신호 데이터를 이용한 패턴 인식 문제에 효과적으로 적용될 수 있음을 보였다.Chapter 1 Introduction 1 1.1 Pattern Recognition in Time Series 1 1.2 Major Problems in Conventional Approaches 7 1.3 The Proposed Approach and its Contribution 8 1.4 Thesis Organization 10 Chapter 2 Related Works 12 2.1 Pattern Recognition in Time Series using Conventional Methods 12 2.1.1 Time Domain Features 12 2.1.2 Frequency Domain Features 14 2.1.3 Signal Processing based on Multi-variate Empirical Mode Decomposition (MEMD) 15 2.1.4 Statistical Time Series Model (ARIMA) 18 2.2 Fundamental Deep Learning Algorithms 20 2.2.1 Convolutional Neural Networks (CNNs) 20 2.2.2 Recurrent Neural Networks (RNNs) 22 2.3 Hyper Parameters and Structural Optimization Techniques 24 2.3.1 Grid and Random Search Algorithms 24 2.3.2 Bayesian Optimization 25 2.3.3 Neural Architecture Search 28 2.4 Research Trends related to Time Series Data 29 2.4.1 Generative Model of Raw Audio Waveform 30 Chapter 3 Preliminary Researches: Patten Recognition in Time Series using Various Feature Extraction Methods 31 3.1 Conventional Methods using Time and Frequency Features: Motor Imagery Brain Response Classification 31 3.1.1 Introduction 31 3.1.2 Methods 32 3.1.3 Ensemble Classification Method (Stacking & AdaBoost) 32 3.1.4 Sensitivity Analysis 33 3.1.5 Classification Results 36 3.2 Statistical Feature Extraction Methods: ARIMA Model Based Feature Extraction Methodology 38 3.2.1 Introduction 38 3.2.2 ARIMA Model 38 3.2.3 Signal Processing 39 3.2.4 ARIMA Model Conformance Test 40 3.2.5 Experimental Results 40 3.2.6 Summary 43 3.3 Application on Specific Time Series Data: Human Stress States Recognition using Ultra-Short-Term ECG Spectral Feature 44 3.3.1 Introduction 44 3.3.2 Experiments 45 3.3.3 Classification Methods 49 3.3.4 Experimental Results 49 3.3.5 Summary 56 Chapter 4 Master Framework for Pattern Recognition in Time Series 57 4.1 The Concept of the Proposed Framework for Pattern Recognition in Time Series 57 4.1.1 Optimal Basic Deep Learning Models for the Proposed Framework 57 4.2 Two Categories for Pattern Recognition in Time Series Data 59 4.2.1 The Proposed Deep Learning Framework for Periodic Time Series Signals 59 4.2.2 The Proposed Deep Learning Framework for Non-periodic Time Series Signals 61 4.3 Expanded Models of the Proposed Master Framework for Pattern Recogntion in Time Series 63 Chapter 5 Deep Learning Model Design Methodology for Periodic Signals using Prior Knowledge: Deep ECGNet 65 5.1 Introduction 65 5.2 Materials and Methods 67 5.2.1 Subjects and Data Acquisition 67 5.2.2 Conventional ECG Analysis Methods 72 5.2.3 The Initial Setup of the Deep Learning Architecture 75 5.2.4 The Deep ECGNet 78 5.3 Experimental Results 83 5.4 Summary 98 Chapter 6 Deep Learning Model Design Methodology for Non-periodic Time Series Signals using Optimization Techniques: Deep EEGNet 100 6.1 Introduction 100 6.2 Materials and Methods 104 6.2.1 Subjects and Data Acquisition 104 6.2.2 Conventional EEG Analysis Methods 106 6.2.3 Basic Deep Learning Units and Optimization Technique 108 6.2.4 Optimization for Deep EEGNet 109 6.2.5 Deep EEGNet Architectures using the EEG Channel Grouping Scheme 111 6.3 Experimental Results 113 6.4 Summary 124 Chapter 7 Concluding Remarks 126 7.1 Summary of Thesis and Contributions 126 7.2 Limitations of the Proposed Methods 128 7.3 Suggestions for Future Works 129 Bibliography 131 초 록 139Docto