Biometric authentication and identification through electrocardiogram signals

Tese de Mestrado Integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), 2021, Universidade de Lisboa, Faculdade de CiênciasO reconhecimento biométrico tem sido alvo de diversas investigações ao longo dos anos, sendo a impressão digital, a face e a iris, os traços biométricos mais explorados. Apesar do seu elevado potencial no que diz respeito a possíveis aplicações tecnológicas, alguns estudos apresentam limitações a estes traços biométricos, nomeadamente a falta de fiabilidade e praticidade num sistema biométrico. Recentemente, vários estudos exploraram o potencial do uso do electrocardiograma (ECG) como traço biométrico, por ser único e singular para cada indivíduo, e dificilmente roubado por outrem, por ser um sinal fisiológico. Nesta dissertação, foi investigada a possibilidade de usar sinais ECG como traço biométrico para sistemas de identificação e autenticação biométrica. Para tal, recorreu-se a uma base de dados pública chamada Check Your Biosignals Here initiative (CYBHi), criada com o intuito de propiciar investigações biométricas. As sessões de aquisição contaram com 63 participantes e ocorreram em dois momentos distintos separados por três meses, numa modalidade “off-the-person”, com recurso a um elétrodo na palma da mão e eletrolicras nos dedos. Os sinais da primeira aquisição correspondem, num sistema biométrico, aos dados armazenados na base de dados, enquanto que os sinais da segunda aquisição correspondem aos dados que serão identificados ou autenticados pelo sistema. Os sistemas de identificação e autenticação biométrica propostos nesta dissertação incluem diferentes fases: o pré-processamento, o processamento e a classificação. O pré-processamento consistiu na aplicação de um filtro passa-banda IIR de 4ª ordem, para eliminar ruídos e artefactos provenientes de atividade muscular e da impedância elétrica dos aparelhos de aquisição. A fase de processamento consistiu em extrair e gerar os templates biométricos, que serão os inputs dos algoritmos de classificação. Primeiramente, extraíram-se os ciclos cardíacos através do Neurokit2 disponível no Python. Para tal, foram localizados os picos R dos sinais ECG e, posteriormente, estes foram segmentados em ciclos cardíacos, com 200 amostras antes e 400 amostras depois dos picos. Com o objetivo de remover os segmentos mais ruidosos, os ciclos cardíacos foram submetidos a um algoritmo de eliminação de segmentos que consistiu em encontrar, para cada sujeito, os 20 e 60 ciclos mais próximos entre si, designados de Set 1 e Set 2, respetivamente. A partir desses dois conjuntos de ciclos, criaram-se dois tipos de templates: 1) os ciclos cardíacos, e 2) escalogramas gerados a partir dos ciclos, através da transformada de wavelet contínua, com dois tamanhos distintos: 56x56 e 224x224, denominados por Size 56 e Size 224, respetivamente. Devido ao elevado tamanho dos escalogramas, foi utilizada a analise de componentes independentes para reduzir a dimensionalidade. Assim, os sistemas biométricos propostos na presente investigação, foram testados com os conjuntos de 20 e 60 templates, quer para ciclos quer para escalogramas, de forma a avaliar o desempenho do sistema quando usados mais ou menos templates para os processos de identificação e autenticação. Os templates foram também testados com e sem normalização, para que pudessem ser analisados os benefícios deste processo. A classificação foi feita através de diferentes métodos, testados numa modalidade “entre-sessões”, isto é, os dados da 2ª aquisição, considerados os dados de teste, foram comparados com os dados da 1ª aquisição, denominados dados de treino, de forma a serem classificados. Quanto ao sistema de identificação com ciclos cardíacos, foram testados diferentes classificadores, nomeadamente LDA, kNN, DT e SVM. Para o kNN e SVM, foi feita uma otimização para encontrar o valor de “k” e os valores de γ e C, respetivamente, que permitem o sistema alcançar o melhor desempenho possível. A melhor performance foi obtida através do LDA, alcançando uma taxa de identificação de 79,37% para a melhor configuração, isto é, usando 60 ciclos normalizados. Os templates com base em escalogramas foram testados como inputs para dois métodos distintos: 1) redes neuronais e 2) algoritmo baseado em distâncias. A melhor performance foi uma taxa de identificação de 69,84%, obtida quando usados 60 escalogramas de tamanho 224, não normalizados. Deste modo, os resultados relativos a identificação provaram que utilizar mais templates (60) para identificar um indivíduo otimiza a performance do sistema biométrico, independentemente do tipo de template utilizado. Para alem disto, a normalização mostrou-se um processo essencial para a identificação com ciclos cardíacos, contudo, tal não se verificou para escalogramas. Neste estudo, demonstrou-se que a utilização de ciclos tem mais potencial para tornar um sistema de identificação biométrica eficiente, do que a utilização de escalogramas. No que diz respeito ao sistema de autenticação biométrica, foi utilizado um algoritmo baseado em distâncias, testado com os dois tipos de templates numa configuração concatenada, isto é, uma configuração na qual cada sujeito e representado por um sinal que contém uma sequência de todos os seus templates, seguidos uns dos outros. A avaliação da performance do sistema foi feita com base nos valores de taxa de autenticação e taxa de impostores, que indicam o número de indivíduos corretamente autenticados face ao número total de indivíduos, e o número de impostores autenticados face ao número total de indivíduos, respetivamente. Os ciclos cardíacos foram testados com e sem redução de dimensionalidade, sendo que a melhor performance foi obtida usando 60 ciclos não normalizados sem redução de dimensionalidade. Para esta configuração, obteve-se uma taxa de autenticação de 90,48% e uma taxa de impostores de 13,06%. Desta forma, concluiu-se que reduzir a dimensionalidade dos ciclos cardíacos prejudica o desempenho do sistema, uma vez que se perdem algumas características indispensáveis para a distinção entre sujeitos. Para os escalogramas, a melhor configuração, que corresponde ao uso de 60 escalogramas normalizados de tamanho 56, atingiu uma taxa de autenticação de 98,42% e uma taxa de impostores de 14,34%. Sendo que a dimensionalidade dos escalogramas foi reduzida com recurso a ICA, foi ainda avaliada a performance do sistema quando reduzido o número de componentes independentes. Os resultados mostraram que um número de componentes igual ao número de sujeitos otimiza o desempenho do sistema, uma vez que se verificou um decréscimo da taxa de autenticação quando reduzido o número de componentes. Assim, concluiu-se que são necessárias 63 componentes independentes para distinguir corretamente os 63 sujeitos. Para a autenticação através de ciclos cardíacos, a normalização e a redução de dimensionalidade são dois processos que degradam a performance do sistema, enquanto que, quando utilizados escalogramas, a normalização e vantajosa. Os resultados obtidos provaram ainda que, contrariamente ao que acontece para processos de identificação, a utilização de escalogramas e uma abordagem mais eficiente e eficaz para a autenticação de indivíduos, do que a utilização de ciclos. Esta investigação comprovou o potencial do ECG enquanto traço biométrico para identificação e autenticação de indivíduos, fazendo uma análise comparativa entre diferentes templates extraídos dos sinais ECG e diferentes metodologias na fase de classificação, e avaliando o desempenho do sistema em cada uma das configurações testadas. Estudos anteriores apresentaram algumas limitações, nomeadamente, o uso de aquisições “on-the-person”, ˜ que apresentam pouco potencial para serem integradas em sistemas biométricos devido à baixa praticidade, e à classificação numa modalidade “intra-sessão”, na qual os dados classificados e os dados armazenados foram adquiridos numa só sessão. Este estudo preenche essas lacunas, visto que utilizou dados adquiridos “off-the-person”, dados esses que foram testados numa modalidade “entre-sessões”. Apesar das aquisições ˜ “off-the-person” estarem sujeitas a mais ruídos e, consequentemente, dificultarem processos de identificação ou autenticação, estas abordagens são as mais adequadas para sistemas biométricos, dada a sua possível integração nas mais diversas aplicações tecnológicas. A modalidade “entre-sessões” resulta também numa pior performance relativamente a utilização de sinais de uma só sessão. No entanto, permite comprovar a estabilidade do ECG ao longo do tempo, o que é um fator indispensável para o funcionamento adequado de um sistema biométrico, uma vez que o mesmo terá que comparar diversas vezes o ECG apresentado no momento de identificação ou autenticação, com o ECG armazenado uma única vez na base de dados. Apesar dos bons resultados apresentados nesta dissertação, no futuro devem ser exploradas bases de dados que contenham mais participantes, com uma faixa etária mais alargada, incluindo participantes com diversas condições de saúde, com aquisições separadas por um período de tempo mais longo, de forma a simular o melhor possível a realidade de um sistema biométrico.Biometrics is a rapidly growing field with applications in personal identification and authentication. Over the recent years, several studies have demonstrated the potential of Electrocardiogram (ECG) to be used as a physiological signature for biometric systems. In this dissertation, the possibility of using the ECG signal as an unequivocal biometric trait for identification and authentication purposes has been presented. The ECG data used was from a publicly available database, the Check Your Biosignals Here initiative (CHBYi) database, developed for biometric purposes, containing records of 63 participants. Data was collected through an off-the-person approach, in two different moments, separated by three months, resulting in two acquisitions per subject. Signals from the first acquisition represent, in a biometric system, the data stored in the database, whereas signals from the second acquisition represent the data to be authenticated or identified. The proposed identification and authentication systems included several steps: signal pre-processing, signal processing, and classification. In the pre-processing phase, signals were filtered in order to remove noises, while the signal processing consisted of extracting and generating the biometric templates. For that, firstly, the cardiac cycles were extracted from the ECG signals, and segment elimination was performed to find the segments more similar to one another, resulting in two sets of templates, with 20 and 60 templates per participant, respectively. After that, two types of templates were generated: 1) templates based on cardiac cycles, and 2) templates based on scalograms generated from the cardiac cycles, with two different sizes, 56x56 and 224x224. Due to the large size of the scalograms, ICA was applied to reduce their dimensionality. Thus, the biometric systems were evaluated with two sets of each type of template in order to analyze the advantages of using more or fewer templates per subject, and the templates were also tested with and without normalization. For the identification system using cardiac cycles, LDA, kNN, DT, and SVM were tested as classifiers in an “across-session” modality, reaching an accuracy of 79.37% for the best model (LDA) in the best configuration (60 normalized cardiac cycles). When using scalograms, two different methodologies were tested: 1) neural network, and 2) a distance-based algorithm. The best accuracy was 69.84% for 60 not-normalized scalograms of Size 224, using NN. Thus, results suggested that the templates based on cardiac cycles are a more promising approach for identification tasks. For the authentication, a distance-based algorithm was used for both templates. Cardiac cycles were tested with and without dimensionality reduction, and the best configuration (60 not-normalized cardiac cycles without dimensionality reduction) reached an accuracy of 90.48% and an impostor score of 13.06%. For the scalograms, the best configuration (60 normalized scalograms of Size 56) reached an accuracy of 98.42% and an impostor score of 14.34%. Therefore, using scalograms for the authentication task proved to be a more efficient and accurate approach. The results from this work support the claim that ECG-based biometrics can be successfully used for personal identification and authentication. This study brings novelty by exploring different templates and methodologies in order to perform a comparative analysis and find the approaches that optimize the performance of the biometric system. Moreover, this represents a step forward towards a real-world application of an ECG-based biometric system, mainly due to the use of data from off-the-person acquisitions in an across-session modality

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient's electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733S117227Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. 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Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.01

2D Hybrid method:Case of VLF signal amplitude variations in the time vicinity of an earthquake

Extraction of information in the form of oscillations from noisy data of natural phenomena such as sounds, earthquakes, ionospheric and brain activity, and various emissions from cosmic objects is extremely difficult. As a method for finding periodicity in such challenging data sets, the 2D Hybrid approach, which employs wavelets, is presented. Our technique produces a wavelet transform correlation intensity contour map for two (or one) time series on a period plane defined by two independent period axes. Notably, by spreading peaks across the second dimension, our method improves apparent resolution of detected oscillations in the period plane and identifies the direction of signal changes using correlation coefficients. We demonstrate the performance of the 2D Hybrid technique on a very low frequency (VLF) signal emitted in Italy and recorded in Serbia in time vicinity of the occurrence of an earthquake on November 3, 2010, near Kraljevo, Serbia. We identified a distinct signal in the range 120-130 s that appears only in association with the considered earthquake. Other wavelets, such as Superlets, which may detect fast transient oscillations, will be employed in the future analysis.Comment: published in Mathematics MDP

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Wearable Sensor Gait Analysis for Fall Detection Using Deep Learning Methods

World Health Organization (WHO) data show that around 684,000 people die from falls yearly, making it the second-highest mortality rate after traffic accidents [1]. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. In light of the recent widespread adoption of wearable sensors, it has become increasingly critical that fall detection models are developed that can effectively process large and sequential sensor signal data. Several researchers have recently developed fall detection algorithms based on wearable sensor data. However, real-time fall detection remains challenging because of the wide range of gait variations in older. Choosing the appropriate sensor and placing it in the most suitable location are essential components of a robust real-time fall detection system. This dissertation implements various detection models to analyze and mitigate injuries due to falls in the senior community. It presents different methods for detecting falls in real-time using deep learning networks. Several sliding window segmentation techniques are developed and compared in the first study. As a next step, various methods are implemented and applied to prevent sampling imbalances caused by the real-world collection of fall data. A study is also conducted to determine whether accelerometers and gyroscopes can distinguish between falls and near-falls. According to the literature survey, machine learning algorithms produce varying degrees of accuracy when applied to various datasets. The algorithm’s performance depends on several factors, including the type and location of the sensors, the fall pattern, the dataset’s characteristics, and the methods used for preprocessing and sliding window segmentation. Other challenges associated with fall detection include the need for centralized datasets for comparing the results of different algorithms. This dissertation compares the performance of varying fall detection methods using deep learning algorithms across multiple data sets. Furthermore, deep learning has been explored in the second application of the ECG-based virtual pathology stethoscope detection system. A novel real-time virtual pathology stethoscope (VPS) detection method has been developed. Several deep-learning methods are evaluated for classifying the location of the stethoscope by taking advantage of subtle differences in the ECG signals. This study would significantly extend the simulation capabilities of standard patients by allowing medical students and trainees to perform realistic cardiac auscultation and hear cardiac auscultation in a clinical environment

Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG