Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.Mohamed Elgendi, Björn Eskofier, Socrates Dokos, Derek Abbot

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects

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

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition

Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the automatic disease diagnosis and recognition and, typically, our research pays attention on automatic classifications for electrophysiological signals, which are measurements of the electrical activity. Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition and seizure detection. With the ECG signals obtained from wearable devices, the candidate developed novel signal processing and machine learning method for continuous monitoring of heart conditions. Compared to the traditional methods based on the devices at clinical settings, the developed method in this thesis is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to enhance the performance. An emotion recognition method with a single channel ECG is developed, where a novel exploitative and explorative GWO-SVM algorithm is proposed to achieve high performance emotion classification. The attractive part is that the proposed algorithm has the capability to learn the SVM hyperparameters automatically, and it can prevent the algorithm from falling into local solutions, thereby achieving better performance than existing algorithms. A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to the spectral-temporal domain, so that the dimension of the input features to the CNN can be significantly reduced, while the detector can still achieve superior detection performance

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

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua