Ecg biometrics using deep learning and relative score threshold classification

PD/BDE/130216/2017The field of biometrics is a pattern recognition problem, where the individual traits are coded, registered, and compared with other database records. Due to the difficulties in reproducing Electrocardiograms (ECG), their usage has been emerging in the biometric field for more secure applications. Inspired by the high performance shown by Deep Neural Networks (DNN) and to mitigate the intra-variability challenges displayed by the ECG of each individual, this work proposes two architectures to improve current results in both identification (finding the registered person from a sample) and authentication (prove that the person is whom it claims) processes: Temporal Convolutional Neural Network (TCNN) and Recurrent Neural Network (RNN). Each architecture produces a similarity score, based on the prediction error of the former and the logits given by the last, and fed to the same classifier, the Relative Score Threshold Classifier (RSTC).The robustness and applicability of these architectures were trained and tested on public databases used by literature in this context: Fantasia, MIT-BIH, and CYBHi databases. Results show that overall the TCNN outperforms the RNN achieving almost 100%, 96%, and 90% accuracy, respectively, for identification and 0.0%, 0.1%, and 2.2% equal error rate (EER) for authentication processes. When comparing to previous work, both architectures reached results beyond the state-of-the-art. Nevertheless, the improvement of these techniques, such as enriching training with extra varied data and transfer learning, may provide more robust systems with a reduced time required for validation.publishersversionpublishe

Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

새로운 심탄도 계측 시스템의 응용 -연속혈압 추정과 생체인식

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 바이오엔지니어링전공, 2014. 8. 김희찬.심탄도 (Ballistocardiogram)는 심박에 동기되어 발생하는 우리 몸의 미세한 진동을 측정한 신호이다. 비침습적으로 심혈관계의 활동을 관찰할 수 있다는 장점 때문에, 20세기 초반에 심탄도의 해석에 대한 많은 연구가 이루어졌다. 그러나 초음파 기기 등 심혈관계 관련 질병들을 진단할 수 있는 새로운 기술들이 개발되면서 상대적으로 실용적이지 못한 특성을 가진 심탄도에 대한 관심은 1970년대 이후에 급격히 줄어들었다. 새로운 센서들의 등장과 마이크로프로세서, 신호처리 기술들의 발전에 힘입어 심탄도 연구는 다시 활기를 띠고 있다. 그러나 이러한 발전들에도 불구하고 심탄도는 의자나 침대 등 상당한 부피를 차지하는 사물을 이용하여 계측되고, 분석을 위해서는 동기화 된 심전도가 동시에 측정되어야 하는 등 측정상의 번거로움이 있다. 또한, 심탄도는 개인 간에는 물론 한 개인에게서도 파형에 많은 변이를 보여 신호의 일관된 해석에 어려움이 있다. 본 학위논문에서는, 이러한 측정 측면과 신호처리 측면에서 현 심탄도 응용의 한계점을 극복할 수 있는 방안을 마련하여, 심탄도의 실질적인 활용 범위를 더욱 확장할 수 있는 방안에 대해 연구하였다. 우선, 심탄도를 심전도와 동시에 무구속적으로 잴 수 있는 필름기반의 패치타입 센서를 개발하였다. 압전소자의 양면에 복수개의 전극을 패터닝하고 각각의 전극에 독립된 기능을 부여해 회로에 연결함으로써 필름 한 장으로 물리적인 신호 (심탄도)와 전기적인 신호(심전도)의 동시 측정이 가능하게 하였다. 센서를 가슴에 부착하였을 때 심전도의 특징적인 R 피크과 심탄도의 특징적인 J 피크를 확인하여 기능을 확인할 수 있었으며, 추가적으로 R-J 간격이 수축기 혈압과 음의 상관관계를 가짐을 이용하여 개발된 센서로 혈압을 추정할 수 있었다. 센서를 통해 예측한 수축기 혈압 오차의 평균값과 표준편차는 각각 -0.16 mmHg와 4.12mmHg으로, 미국과 영국의 혈압계 가이드라인을 모두 만족시킬 수 있었다. 다음으로, 심탄도의 변이적 특성을 새로운 생체인식 기법으로 발전시키는 방안에 대한 연구를 진행하였다. 이를 위하여 심탄도 한 파형 내의 특이점들을 기반으로 특징 벡터를 추출하고 기계학습을 통해 특징들의 변이를 개인들 간의 변이와 한 개인 내에서의 변이로 구분 하였다. 추출된 특징들을 이용하여 35명의 피험자들에게 실험해 본 결과, 단일 심박신호로는 90.20%의 확률로 개개인을 구분할 수 있었으며 7개의 연속된 심박신호로는 98%이상의 성능을 낼 수 있었다. 또한 약 일주일 간격을 두고 반복하여 측정한 데이터와 운동을 통해 심박수가 변화된 데이터의 적용을 통해서 심탄도를 이용한 생체인식 방법의 재현성을 확인할 수 있었다.Ballistocardiogram (BCG) is a recording of body movement, which is generated in synchronous with the heartbeats. Studies on BCG were a field of intense research in the past decades, since it could provide a non-invasive means to monitor cardiovascular activities. However, such interests have slowly diminished after 1970s due to its impractical characteristics compared to the new technologies (i.e. echocardiography) that diagnose cardiovascular system. Studies on BCG are now on its resurgence era, with advent of new sensors, microprocessor, and the signal processing techniques. Notable differences of todays BCG researches, compared to the past ones, are on the emergence of non-diagnostic applications of BCG. Sleep analysis, heartbeat detection and the estimation of pre-ejection time are the few examples of BCG applications that were previously non-existent. Despite these advancements, however, practical usage of BCG has yet to become reality. One reason for this is in its difficulties in instrumentation. In a number of researches, BCGs are often recorded with a sensor attached to bulky objects, for example bed or chair. Also, a synchronously measured electrocardiogram (ECG) is required for the accurate analysis of BCG, therefore, increases the system complexity. Morphological variability of BCG is another limiting factor. Waveforms of BCG are reported to vary among subjects and even in a same person. Such characteristics of BCG impose difficulties in its consistent interpretation and in drawing meaningful information. In this dissertation, we first propose a sensor, namely BE-patch, which can record both the BCG and ECG using a ferro-electret film. As the sensor is thin and flexible and features reduced complexity, it suits for wearable applications in terms both of user compliance and power consumption. The fabrication method of BE-patch and its application in blood pressure estimation is reported in Chapter 2. Using the time delay of R-peak of ECG and J-peak of BCG (so-called, R-J interval), which showed the negative relationship with changes in blood pressure, the beat-by-beat systolic blood pressure (SBP) is estimated. The mean error of the estimated SBP and its standard deviation were ?0.16 and 4.12 mm Hg, respectively and their performance met both the Association for the Advancement of Medical Instrumentation and the British Hypertension Society guidelines. In Chapter 3, the variable aspect of BCG is re-analyzed to develop a biometric application. Waveforms of BCG were described using features and their variability was separated to the inter-individual and the intra-individual variations by applying supervised learning algorithms. The result showed the potential utility of BCG as biometric signal, by achieving identification accuracy of 90.20% using only a cycle of BCG. Then identification increased to 98% when multiple beats were used, and reproducible with time and changes in heart-rates. In Chapter 4, the thesis work is summarized, and future directions to further develop the proposed sensor and applications are discussed.Abstract i List of Tables v List of Figures vi 1. Introduction １ 1.1. History of BCG Research １ 1.2. Recent Advances ７ 1.3. Goal of Thesis Work ９ 2. Blood Pressure Estimation １３ 2.1. Introduction １３ 2.2. Principles of BP Estimation １６ 2.3. Methods ２１ 2.4. Results and Discussions ２６ 2.5. Conclusion ２８ 3. Biometric Application ２９ 3.1. Introduction ２９ 3.2. Methods ３９ 3.3. Results and Discussions ４８ 3.4. Conclusion ５６ 4. Conclusions and Discussions ５７ Bibliography ６５ 국문초록 ７１Docto

Implementação de um sistema de análise automática do ECG para identificação de episódios de fibrilação atrial utilizando uma plataforma de aquisição BITalino® e um smartphone Android™

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAs arritmias cardíacas são distúrbios que afetam a frequência e/ou o ritmo dos batimentos cardíacos. O diagnóstico da maioria das arritmias é feito através da análise do eletrocardiograma (ECG), o qual consiste na representação gráfica da atividade elétrica do coração. A fibrilação atrial (AF) é um tipo de arritmia cardíaca, sendo a mais presente na população mundial. Se não identificada nos estágios iniciais, aumenta as chances de ocorrência de paragens cardíacas e acidente vascular cerebral, que constituem uma das maiores causas de morte no mundo. Uma das principais características presentes no sinal de ECG de indivíduos com AF é a irregularidade no ritmo cardíaco, ou seja, variação no intervalo entre dois picos R consecutivos. Pelo fato da AF muitas vezes se apresentar de forma assintomática, o uso de sistemas computacionais para a análise automática do sinal de ECG se apresenta como uma alternativa interessante para auxiliar o profissional de saúde no diagnóstico dessa arritmia. Nesse contexto, o presente trabalho trata da implementação de um sistema de análise automática do sinal de ECG para identificação de episódios de AF. O sistema consiste em uma etapa de aquisição do sinal realizada por um sensor de ECG BITalino conectado à plataforma BITalino (r)evolution Core, ambos desenvolvidos pela PLUX – Wireless Biosignals S.A. O sinal adquirido é transmitido via comunicação bluetooth para um smartphone com sistema operacional Android™. O processamento do sinal é feito através de um aplicativo desenvolvido através da IDE Android™ Studio. O sistema de análise foi desenvolvido através do software MATLAB® e, posteriormente, implementado no aplicativo com o auxílio da aplicação MATLAB Coder™ e da interface JNI. Em linhas gerais, o sistema de análise é composto por um algoritmo para detecção dos picos da onda R do sinal de ECG, seguido de uma etapa de extração de características, e outra de classificação. A característica utilizada na entrada do modelo de classificação foi o intervalo entre picos R consecutivos. O modelo de classificação utilizado é baseado em redes neurais do tipo LSTM (Long Short-Term Memory). Quando validado sobre os sinais do banco de dados MIT-BIH Atrial Fibrillation, o algoritmo de detecção dos picos da onda R apresentou valores médios de sensibilidade (Se) e preditividade positiva (P+) de 98,99% e 95,95%, respectivamente. O modelo de classificação utilizado apresentou exatidão média de 94,94% na identificação de episódios de AF.Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphic representation of the electrical activity of the heart. Atrial fibrillation (AF) is a type of cardiac arrhythmia, being the most present in the world population. If not identified in the early stages, it increases the chances of cardiac arrest and stroke, which are one of the biggest causes of death in the world. One of the main characteristics present in the ECG signal of individuals with AF is the irregularity in the cardiac rhythm, that is, variation in the interval between two consecutive R peaks. Since AF is often asymptomatic, the use of computer systems for the automatic analysis of the ECG signal is an interesting alternative to assist health professionals in diagnosing this arrhythmia. In this context, this work deals with the implementation of an automatic ECG signal analysis system to identify AF episodes. The system consists of a signal acquisition step performed by a BITalino ECG sensor connected to the BITalino (r)evolution Core platform, both developed by PLUX – Wireless Biosignals SA. The acquired signal is transmitted via bluetooth communication to a smartphone with Android™ operating system. The signal processing is done through an application developed using the IDE Android™ Studio. The analysis system was developed using the MATLAB® software and later implemented in the application with the help of the MATLAB Coder™ application and the JNI interface. In general terms, the analysis system is composed of an algorithm for detecting the peaks of the R wave of the ECG signal, followed by a feature extraction step, and a classification step. The feature used in the entry of the classification model was the interval between consecutive R peaks (RRi). The classification model used is based on a LSTM neural network. When validated over the signals from the MIT-BIH Atrial Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity (Se) and positive predictivity (P+) of 98.99% and 95.95%, respectively. The classification model used had an average accuracy of 94.94% in identifying AF episodes

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas