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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Ecg biometric"

Kumar, Sunil. "ECG Biometric Identification." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 2148–52. http://dx.doi.org/10.22214/ijraset.2018.3505.

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TALININGSIH, FAUZI FRAHMA, YUNENDAH NUR FU’ADAH, SYAMSUL RIZAL, ACHMAD RIZAL, and MUHAMMAD ADNAN PRAMUDITO. "Sistem Otentikasi Biometrik Berbasis Sinyal EKG Menggunakan Convolutional Neural Network 1 Dimensi." MIND Journal 7, no. 1 (June 29, 2022): 1–10. http://dx.doi.org/10.26760/mindjournal.v7i1.1-10.

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ABSTRAKBiometrik merupakan salah satu analisis karakteristik individu yang saat ini banyak digunakan, seperti sidik jari, pengenalan suara, dan pengenalan wajah. Metode biometrik tersebut masih memiliki kelemahan seperti mudah untuk dimanipulasi. Oleh karena itu, penelitian ini akan menggunakan sinyal Elektrokardiogram (EKG) sebagai salah satu metode biometrik. Sinyal EKG memiliki keunikan pada setiap individu sehingga sulit untuk dimanipulasi. Penelitian ini mengembangkan sistem otentikasi biometrik berbasis sinyal EKG. Data yang digunakan berasal dari ECG-ID database dengan jumlah 90 subjek. Sinyal EKG yang digunakan hanya menggunakan gelombang PQRST sebagai input model Convolutional Neural Network 1 Dimensi (CNN). Hasil akurasi yang diperoleh menunjukkan 92.2%. Dengan demikian, sistem yang dikembangkan memungkinkan digunakan sebagai otentikasi biometrik.Kata kunci: Biometrik, Sinyal EKG, Convolutional Neural NetworkABSTRACTBiometrics is analyses individual characteristics that are currently widely used, such as fingerprints, voice recognition, and face recognition. The biometric method still has weaknesses, such as being easy to manipulate. Therefore, this study will use an Electrocardiogram (ECG) signal as a biometric method. The ECG signal is unique to each individual, so it is not easy to manipulate. This study develops a biometric authentication system based on ECG signals. The data used comes from the ECG-ID database with a total of 90 subjects. The ECG signal used only PQRST waves as input for the 1-Dimensional Convolutional Neural Network (CNN) model. The accuracy results obtained show 92.2%. Thus, the developed system allows it to be used as biometric authentication.Keywords: Biometric, ECG Signal, Convolutional Neural Network

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Ammour, Nassim, Rami M. Jomaa, Md Saiful Islam, Yakoub Bazi, Haikel Alhichri, and Naif Alajlan. "Deep Contrastive Learning-Based Model for ECG Biometrics." Applied Sciences 13, no. 5 (February 27, 2023): 3070. http://dx.doi.org/10.3390/app13053070.

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The electrocardiogram (ECG) signal is shown to be promising as a biometric. To this end, it has been demonstrated that the analysis of ECG signals can be considered as a good solution for increasing the biometric security levels. This can be mainly due to its inherent robustness against presentation attacks. In this work, we present a deep contrastive learning-based system for ECG biometric identification. The proposed system consists of three blocks: a feature extraction backbone based on short time Fourier transform (STFT), a contrastive learning network, and a classification network. We evaluated the proposed system on the Heartprint dataset, a new ECG biometrics multi-session dataset. The experimental analysis shows promising capabilities of the proposed method. In particular, it yields an average top1 accuracy of 98.02% on a new dataset built by gathering 1539 ECG records from 199 subjects collected in multiple sessions with an average interval between sessions of 47 days.

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Elshahed, Marwa A. "Personal identity verification based ECG biometric using non-fiducial features." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3007. http://dx.doi.org/10.11591/ijece.v10i3.pp3007-3013.

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Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

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Singh, Yogendra Narain, Sanjay Kumar Singh, and Amit Kumar Ray. "Bioelectrical Signals as Emerging Biometrics: Issues and Challenges." ISRN Signal Processing 2012 (July 26, 2012): 1–13. http://dx.doi.org/10.5402/2012/712032.

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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

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Kim, Hanvit, Haena Kim, Se Chun, Jae-Hwan Kang, Ian Oakley, Youryang Lee, Jun Ryu, et al. "A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors." Sensors 18, no. 8 (August 20, 2018): 2738. http://dx.doi.org/10.3390/s18082738.

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Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

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Chou, Ching-Yao, Yo-Woei Pua, Ting-Wei Sun, and An-Yeu (Andy) Wu. "Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis." Sensors 20, no. 11 (June 9, 2020): 3279. http://dx.doi.org/10.3390/s20113279.

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Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.

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Karimian, Nima, Damon Woodard, and Domenic Forte. "ECG Biometric: Spoofing and Countermeasures." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 3 (July 2020): 257–70. http://dx.doi.org/10.1109/tbiom.2020.2992274.

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S. Raju, A., and V. Udayashankara. "A Survey on Unimodal, Multimodal Biometrics and Its Fusion Techniques." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 689. http://dx.doi.org/10.14419/ijet.v7i4.36.24224.

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Presently, a variety of biometric modalities are applied to perform human identification or user verification. Unimodal biometric systems (UBS) is a technique which guarantees authentication information by processing distinctive characteristic sequences and these are fetched out from individuals. However, the performance of unimodal biometric systems restricted in terms of susceptibility to spoof attacks, non-universality, large intra-user variations, and noise in sensed data. The Multimodal biometric systems defeat various limitations of unimodal biometric systems as the sources of different biometrics typically compensate for the inherent limitations of one another. The objective of this article is to analyze various methods of information fusion for biometrics, and summarize, to conclude with direction on future research proficiency in a multimodal biometric system using ECG, Fingerprint and Face features. This paper is furnished as a ready reckoner for those researchers, who wish to persue their work in the area of biometrics.

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Donida Labati, Ruggero, Enrique Muñoz, Vincenzo Piuri, Roberto Sassi, and Fabio Scotti. "Deep-ECG: Convolutional Neural Networks for ECG biometric recognition." Pattern Recognition Letters 126 (September 2019): 78–85. http://dx.doi.org/10.1016/j.patrec.2018.03.028.

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Дисертації з теми "Ecg biometric"

Arteaga, Falconi Juan Sebastian. "Towards an Accurate ECG Biometric Authentication System with Low Acquisition Time." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40129.

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Biometrics is the study of physical or behavioral traits that establishes the identity of a person. Forensics, physical security and cyber security are some of the main fields that use biometrics. Unlike traditional authentication systems—such as password based—biometrics cannot be lost, forgotten or shared. This is possible because biometrics establishes the identity of a person based on a physiological/behavioural characteristic rather than what the person possess or remembers. Biometrics has two modes of operation: identification and authentication. Identification finds the identity of a person among a group of persons. Authentication determines if the claimed identity of a person is truthful. Biometric person authentication is an alternative to passwords or graphical patterns. It prevents shoulder surfing attacks, i.e., people watching from a short distance. Nevertheless, biometric traits of conventional authentication techniques like fingerprints, face—and to some extend iris—are easy to capture and duplicate. This denotes a security risk for modern and future applications such as digital twins, where an attacker can copy and duplicate a biometric trait in order to spoof a biometric system. Researchers have proposed ECG as biometric authentication to solve this problem. ECG authentication conceals the biometric traits and reduces the risk of an attack by duplication of the biometric trait. However, current ECG authentication solutions require 10 or more seconds of an ECG signal in order to have accurate results. The accuracy is directly proportional to the ECG signal time-length for authentication. This is inconvenient to implement ECG authentication in an end-user product because a user cannot wait 10 or more seconds to gain access in a secure manner to their device. This thesis addresses the problem of spoofing by proposing an accurate and secure ECG biometric authentication system with relatively short ECG signal length for authentication. The system consists of an ECG acquisition from lead I (two electrodes), signal processing approaches for filtration and R-peak detection, a feature extractor and an authentication process. To evaluate this system, we developed a method to calculate the Equal Error Rate—EER—with non-normal distributed data. In the authentication process, we propose an approach based on Support Vector Machine—SVM—and achieve 4.5% EER with 4 seconds of ECG signal length for authentication. This approach opens the door for a deeper understanding of the signal and hence we enhanced it by applying a hybrid approach of Convolutional Neural Networks—CNN—combined with SVM. The purpose of this hybrid approach is to improve accuracy by automatically detect and extract features with Deep Learning—in this case CNN—and then take the output into a one-class SVM classifier—Authentication; which proved to outperform accuracy for one-class ECG classification. This hybrid approach reduces the EER to 2.84% with 4 seconds of ECG signal length for authentication. Furthermore, we investigated the combination of two different biometrics techniques and we improved the accuracy to 0.46% EER, while maintaining a short ECG signal length for authentication of 4 seconds. We fuse Fingerprint with ECG at the decision level. Decision level fusion requires information that is available from any biometric technique. Fusion at different levels—such as feature level fusion—requires information about features that are incompatible or hidden. Fingerprint minutiae are composed of information that differs from ECG peaks and valleys. Therefore fusion at the feature level is not possible unless the fusion algorithm provides a compatible conversion scheme. Proprietary biometric hardware does not provide information about the features or the algorithms; therefore, features are hidden and not accessible for feature level fusion; however, the result is always available for a decision level fusion.

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Bin, Safie Sairul Izwan. "Pulse domain novel feature extraction methods with application to ecg biometric authentication." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=17829.

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This thesis presents the concept of representing finite signals in terms of sequential output pulses called pulse domain to extract Electrocardiogram (ECG) features for biometric authentication systems. Two novel methods based on the pulse domain philosophy namely Pulse Active (PA) and Adaptive Pulse Active (APA) techniques are presented in this thesis. A total of 11 algorithms are derived from these two methods and used to generate novel ECG feature vectors. Six algorithms of the PA technique are named as Pulse Active Bit (PAB), Pulse Active Width (PAW), Pulse Active Area (PAA), Pulse Active Mean (PAM), Pulse Active Ratio (PAR) and Pulse Active Harmonic (PAH). Five APA algorithms are named as Adaptive Pulse Active Bit (APAB), Adaptive Pulse Active Width (APAW), Adaptive Pulse Active Area (APAA), Adaptive Pulse Active Mean (APAM) and Adaptive Pulse Active Harmonic (APAH). The proposed techniques are validated using ECG experimental data from 112 subjects. Simulation results indicate that APAW generates the best biometric performance of all 11 algorithms. Selected ranges of PA and APA parameters are determined in this thesis that generates approximate similar biometric performance. Using this suggested range, these parameters are than used as a personal identification number (PIN) which are a part of the proposed PA-APA ECG based multilevel security biometric authentication system.

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BARRA, SILVIO. "Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266893.

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Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost $100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed.

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Favoretto, Saulo. "Máquinas de aprendizado extremo aplicadas à identificação de pessoas através de eletrocardiograma (ECG)." Universidade Tecnológica Federal do Paraná, 2016. http://repositorio.utfpr.edu.br/jspui/handle/1/2417.

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Capes
Esta pesquisa estuda a utilização da rede neural Máquina de Aprendizado Extremo (ELM) para identificação de pessoas (biometria) através do eletrocardiograma (ECG). Os dados biométricos oferecem um nível elevado de segurança para a identificação de pessoas, e o ECG é uma técnica emergente e em crescente desenvolvimento. A ELM foi pouco empregada em sistemas de reconhecimento de padrões que utilizam o sinal de ECG. Desta forma, foram estudadas as técnicas de processamento de sinal: a Transformada Wavelet e a Análise dos Componentes Principais (PCA), com o objetivo de tratar e reduzir a dimensionalidade dos dados de entrada, bem como, fazer um estudo comparativo entre a ELM e a Percepetron Múltiplas Camadas (Multilayer Perceptron – MLP). Os testes foram realizados com 90 pessoas, o sinal de ECG utilizado é referente à derivação I contendo 500 amostras/s e 12-bits de resolução dentro de uma faixa nominal de ±10mV de variação, o número de registros variou de 2 a 20 para cada pessoa. O tamanho de cada ciclo completo de ECG para o processo de formação do espaço amostral foi definido de duas formas: 167 amostras contendo as ondas P+QRS e 280 amostras contendo as ondas P+QRS+T, dos quais foram utilizados os 10 ciclos que possuíam o mais elevado nível de similaridade. Com a Transformada Wavelet, o sinal de ECG foi decomposto em 3 níveis, onde para as ondas P+QRS as reduções foram de 86, 45 e 25 amostras, e para as ondas P+QRS+T foram de 142, 73 e 39 amostras. Já para o PCA o sinal foi reduzido de 10 ciclos cardíacos para apenas 1. Estes foram apresentadas a rede formando os conjuntos de treinamento e teste. Foram utilizadas as Redes Neurais Artificiais ELM e MLP para classificação do ECG. Os resultados obtidos comprovaram que a ELM pode ser utilizada para identificação de pessoas.
This research studies the use of neural network Extreme Learning Machine (ELM) to identify individuals (biometrics) by electrocardiogram (ECG). Biometric data offer a high level of security for identifying people, and ECG is an emerging technique and increasing development. ELM was little used in pattern recognition systems that use the ECG signal. In this way, the signal processing techniques were studied: Wavelet Transform and Principal Component Analysis (PCA), with the objective of treating and reducing the dimensionality of the input data, as was as, to make a comparative study between the ELM and Multilayer Perceptron (MLP). The tests were performed with 90 people, the ECG signal used is related to the lead I containing 500 samples/s and 12- bit resolution within a nominal range of ±10 mV of variation, the number of records ranged from 2 to 20 for each people. The size of each ECG cycle to complete the process of forming the sample space defined in two ways: 167 samples containing the P+QRS waves and 280 samples containing the P+QRS+T waves, of which 10 cycles were used to had the highest level of similarity. With the Wavelet Transform, the ECG signal was decomposed into 3 levels, where for the P+QRS waves the reductions were 86, 45 and 25 samples, and for the P+QRS+T waves were 142, 73 and 39 samples. For PCA, the signal for reduced from 10 cardiac cycles to only 1. These were presented to network forming the joint training and testing. The Artificial Neural Networks ELM and MLP were used for ECG classification. The results obtained proved that the ELM may be used to identify individuals.

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Passos, Henrique dos Santos. "Ensemble de técnicas de representação simbólica para reconhecimento biométrico baseado em sinais de ECG." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/100/100131/tde-18072018-105824/.

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Métodos de identificação de pessoas sempre foram muito importantes para toda a sociedade. Atualmente, as pesquisas em biometria vêm sendo amplamente incentivadas por diversos setores da indústria mundial com o objetivo de melhorar ou substituir os atuais sistemas de segurança e de identificação de pessoas. O campo da biometria abarca uma grande variedade de tecnologias usadas para identificar e verificar a identidade de uma pessoa por meio da mensuração e análise de diversas características físicas e/ou comportamentais do ser humano. Diversas modalidades biométricas têm sido propostas para reconhecimento de pessoas, como impressão digital, íris, face e fala. Estas modalidades biométricas possuem características distintas em termos de desempenho, mensurabilidade e aceitabilidade. Uma questão a ser considerada com a aplicação biométrica é sua robustez a ataques por circunvenção, repetição e ofuscação. Esses ataques estão se tornando cada vez mais frequentes e questionamentos estão sendo levantados a respeito dos níveis de segurança das formas de reconhecimento. Sinais biomédicos como eletrocardiograma (ECG), eletroencefalograma (EEG) e eletromiograma (EMG) têm sido cada vez mais estudados e aplicados ao reconhecimento biométrico. Em específico, os sinais de ECG têm sido largamente adotados para o reconhecimento biométrico em diversos trabalhos. Por outro lado, análise de séries temporais tem sido usada com sucesso em muitas diferentes aplicações para identificar padrões temporais nos dados. Embora dinâmica simples possa ser observada com ferramentas analíticas tradicionais tais como transformada de fourier, transformada wavelet, a representação simbólica pode melhorar a análise de processos que são complexos e possivelmente caótico. Além disso, representação simbólica pode também reduzir a sensibilidade a ruído e melhorar bastante a eficiência computacional. No entanto, existem aspectos estruturais e paramétricos de projeto que podem conduzir a uma degradação de desempenho. Na ausência de uma metodologia sistemática e de baixo custo para a proposição de técnicas de representação simbólicas otimamente especificadas, os comitês de máquinas, mais especificamente ensemble, se apresentam como alternativas promissoras. Neste estudo, os componentes do ensemble, que correspondem as técnicas de representação simbólicas, e seus respectivos parâmetros foram selecionados via algoritmos evolutivos. O objetivo é explorar conjuntamente potencialidades advindas das técnicas de representação simbólicas e comitê de máquinas para reconhecimento biométrico baseado em sinais de ECG. Resultados experimentais conduzidos sobre dois conjuntos de dados disponíveis publicamente indicam que a abordagem proposta pode melhorar o desempenho do reconhecimento quando comparada com as técnicas tradicionais
Identification people methods have been very important for the whole society. Currently, research on biometrics have been widely encouraged by various sectors of the industry worldwide in order to improve or replace existing security systems and people identification. The field of biometrics includes a variety of technologies used to identify or verify the identity of a person by measuring and analyzing various physical and/or behavioral aspects of the human being. Several biometric methods have been proposed for recognition of people, such as fingerprint, iris, face and speech. These biometric modalities have different characteristics in terms of performance, measurability and acceptability. One issue to be considered with the biometric application in the real world is its robustness to attacks by circumvention, repetition and obfuscation. These attacks are becoming more frequent and more questions are being raised about the levels of security that this technology can offer. Biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG) and electromyogram (EMG) have been increasingly studied and applied to biometric recognition. Specifically, ECG signals have been widely adopted for biometric recognition in various works. On the other hand, time series analysis has been used successfully in many different applications to identify temporal patterns in the data. Although simple dynamics can be observed with traditional analytical tools such as fourier transform, wavelet transform, the symbolic representation can improve the analysis of processes that are complex and possibly chaotic. In addition, symbolic representation can also reduce noise sensitivity and greatly improve computational efficiency. However, there are structural and parametric design aspects that can lead to performance degradation. In the absence of a systematic and inexpensive methodology for proposing optimally specified symbolic representation techniques, machine committees, more specifically ensemble, present themselves as promising alternatives. In this study, the components of the committee, which correspond to techniques of symbolic representation, and their respective parameters were selected via evolutionary algorithms. The objective is to jointly explore the potentialities of both symbolic representation techniques and machine committee for biometric recognition based on ECG signals. Experimental results conducted on two publicly available datasets indicate that the proposed approach may improve recognition performance when compared to traditional techniques

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LAUDATO, Gennaro. "Innovative information systems to monitor biomedical parameters during high demanding tasks." Doctoral thesis, Università degli studi del Molise, 2021. http://hdl.handle.net/11695/100496.

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The objective of this PhD project is, as its research core, the application of Machine Learning techniques and Big Data analytics to monitor, in a non-invasive way, vital parameters of individuals engaged in tasks that require a high psychophysical effort. The industrial partners of this project are Formula Medicine (as Italian industrial partner with advisor Dr. Riccardo Ceccarelli) and AOTech (foreign industrial partner with advisor mr. Sebastien Philippe). Formula Medicine is a sports medicine center able to offer medical assistance and training programs both physical and mental. Its strength is represented by the Mental Economy Gym, a gym dedicated to the optimization of mental resources. AOTech, on the other hand, is a partner company of Formula Medicine and a leader in the definition of high-tech products and services for the automotive industry and motorsport. AOTech has designed a sport driving simulator, able to reproduce all the main world circuits. The simulator, thanks to the equipment of a hydraulic system, allows to relive physical and mental sensations very similar to those perceived during real driving. The software system also allows vehicle’s data extraction. Within the present project, also taking into account the research domain of the industrial partners, the focus has been addressed to the monitoring of athletes belonging to motorsport with two linked but distinct objectives: the first, strictly related to the analysis of the drivers’ body performances and the second dedicated to the automatic identification of cardiac pathologies starting from electrocardiographic data. Finally, the know-how on the monitoring of biomedical parameters, acquired during the first years of this PhD project in the field of motorsport, was exported to the field of software engineering with the aim of verifying the possibility of predicting the correctness of a programming task that a software developer performs, based on the continuous monitoring of his body parameters. As a first result of the PhD, novel metrics have been defined to objectify effort, physical consumption, stress, and other factors. These metrics have been included in the software in use in Formula Medicine to have a measure of performance. In addition, part of them were correlated with the race performance of the drivers, through the integration of the body data with the data derived from the driving simulator used in AOTech. With regards to the second research focus, a decision support system was defined in the context of early diagnosis of cardiac diseases. The recommendation system consists of several algorithms that accept as input a digital electrocardiographic lead and identify the presence of a possible cardiac pathology. Finally, in the software engineering research field, the production of a developer was measured by evaluating the absence of defects in the source code. Preliminary results show that the proposed approach—that takes into account biomedical and code-based features—allows to discriminate with fair accuracy the outcome of a programming task, reaching an accuracy higher than 80%. This result was compared to state of the art metrics based on measures on the source code. It was higher than source code metrics, thus demonstrating the importance of biometric measurements in the identification of correctness of a coding task.

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Ferreira, Iuri Emmanuel de Paula. "Delineamentos D-ótimos para os modelos de Michaelis-Menten e de Hill /." Botucatu : [s.n.], 2010. http://hdl.handle.net/11449/87920.

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Orientador: Luzia Aparecida Trinca
Banca: Cláudia Pio Ferreira
Banca: Silvio Sandoval Zocchi
Banca: Miriam Harumi Tsunemi
Banca: Julia Maria Pavan Soler
Resumo: Os resultados de muitos experimentos em áreas da biologia, como a farmacologia, a bioquímica e a agronomia, geralmente são analisados por ajustes de modelos não-lineares através dos quais pretende-se explicar a resposta através dos fatores pré-especificados no experimento. As estimações dos parâmetros ou das funções de interesse podem ser imprecisas se os níveis dos fatores não forem adequadamente escolhidos, impossibilitando ao pesquisador a obtenção da informação desejada sobre o objeto de estudo. A construção de um delineamento ótimo, que maximize a informação sobre algum aspecto de interesse, é crucial para o sucesso da prática experimental. O objetivo deste trabalho foi a obtenção de delineamentos D-ótimos exatos para modelos não-lineares utilizados para estudar cinética enzimática e transporte de minerais no organismo, como o de Michaelis-Menten e o de RiU. Para este fim, duas abordagens foram consideradas, a saber, a de delineamentos localmente ótimos e a pseudo-Bayesiana. Com o auxílio dos algoritmos genético e exchange foi possível obter delineamentos D-ótimos exatos para o modelo de Michaelis-Menten, para o modelo de RiU e para ambos, considerando-se valores diferentes e distribuições com diversos coeficientes de variação como informação a priori
Abstract: The results of many experiments in biological fields, as pharmacology, biochemistry and agriculture, usually are analyzed by fitting nonlinear models, which are supposed to describe well the resp'onse to the pre-specified factors in the experiment. The estimates of the parameters or of their functions of interest could be imprecise if the factor levels are not adequately chosen. The construction of an optimum design, which maximizes the information about some aspect of interest, is crucial for the success of the experimental practice. The aim of this work was constructing exact D-optimal designs for nonlinear models usually used in studies of enzyme kinetics and mineral transport in organisms, such as the Michaelis-Menten and RiU models. Two approaches were considered, the locally optimal and pseudo- Bayesian designs. Genetic and Exchange algorithms were used for getting exact designs aiming at the Michaelis-Menten model, aiming at the RiU model, each one separately, and aiming at both models when considering a composite criterion. Different values and probability distributions with several variation coefficients were considered as prior information
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Martins, Andréa Camila dos Santos. "O método de pontos interiores no planejamento da radioterapia /." Botucatu : [s.n.], 2011. http://hdl.handle.net/11449/95056.

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Анотація:

Orientador: Helenice de Oliveira Florentino Silva
Banca: Andréa Carla Gonçalves Vianna
Banca: Antônio Roberto Balbo
Resumo: Um tratamento do câncer por radioterapia tem como objetivo a eliminação das células do tumor e preservação das células saudáveis, obtendo assim uma melhor homo-geneização da dose administrada e menor possibilidade de complicações clínicas durante o tratamento. O sucesso do tratamento depende de um bom planejamento. Para um planejamento ótimo, técnicas matemáticas estão sendo utilizadas com o objetivo de maximizar a radiação no tumor e minimizar a radiação nas regiões vizinhas, com isto modelos de programação linear têm sido ótimas ferramentas para auxiliar a construção dos planos de tratamento por radioterapia. Assim, este trabalho visa: estudar os principais conceitos envolvidos no planejamento do tratamento do câncer por radioterapia; estudar modelos de programação linear (PL) aplicados ao planejamento ótimo; fazer um amplo estudo sobre a técnica de pontos interiores para PL e apresentar uma aplicação desta técnica para resolução de um problema de planejamento ótimo para o tratamento do câncer por radioterapia
Abstract: A cancer treatment by radiotherapy aims to eliminate tumor cells and preservation of healthy cells, thus getting a better homogenization of the administered dose and fewer chances of complications during treatment. Treatment success depends on good planning. For an optimal planning, mathematical techniques are being used in order to maximize radiation at tumor and minimize radiation in the surrounding regions, thus linear programming models has been great tools to assist the construction of treatment plans for radiation therapy. Thus, this work aims: studying the key concepts involved in planning the treatment of cancer by radiotherapy; study the models the linear program- ming (PL) applied to optimal planning; make a broad study on the technique of interior point for PL and present an enforcement of this technique for solving a problem of optimal planning for cancer treatment by radiotherapy
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Santos, Maurício Bedim dos. "Delineamentos ótimos para experimentos farmacocinéticos /." Botucatu : [s.n.], 2010. http://hdl.handle.net/11449/87911.

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Orientador: Luiza Aparecida Trinca
Banca: José Silvio Govone
Banca: Silvio Sandoval Zocchi
Resumo: Os ensaios na area de farmacologia cl nica envolvem coletas sangu neas e medidas da informação (concentração de um fármaco) em horários pré estabelecidos. A prática atual, na maioria das vezes, estabelece os tempos de coleta arbitrariamente, o que pode resultar em dados pouco informativos para ajustar um modelo. Uma metodologia para resolver este tipo de problema e a construcão de delineamentos otimos. Em geral, os modelos envolvem equações não lineares. Sendo que um modelo popular e o modelo monocompartimental (de primeira ordem de absorção e eliminação) que possui três parâmetros. O problema principal de delineamento para modelos não lineares e que a matriz de variâncias e covariâncias dos estimadores dos parâmetros depende dos valores destes, dificultando o planejamento. Outra dificuldade é que várias coletas são realizadas num mesmo sujeito e portanto as respostas são correlacionadas. Assim, a matriz de variâncias e covariâncias depende também das correlações que podem ser incorporadas considerando-se um modelo não linear com efeitos aleatórios. Esse trabalho visa o estudo da teoria de delineamentos... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Trials in clinical pharmacology involves colleting blood samples and measuring the concentration of a drug at pre-especi ed moments. Current practice, usually xes the point times arbitrarily, which can result in uninformative data to t the aimed model. A methodology for solving such problems is the construction of optimum designs. In general, the models involve nonlinear equations. A popular model is the one-compartment model ( rst-order absorption and elimination). This model has three parameters. The main problem of design for nonlinear models is that the matrix of variances and covariances of the estimators of the parameters depends on the values of these, making the planning more di cult. Another di culty is that several samples are performed in the same subject and therefore the responses are correlated. The matrix of variances and covariances also depends on the correlations. The correlations can be incorporated by considering a nonlinear model with random e ects. This work aims to study the theory of optimal designs and the construction of algorithm to optimize designs under the nonlinear model with xed e ects and random e ects. The methodology can produce local optimum designs at some prior value of the parameters or try to reach global optimum through the incorporation of probability distributions of the parameters which are taken into account when calculating the value of the criterion used such designs are called Bayesians. Based on the results of an experiment from the literature D and Aw local and Bayesian optimum designs were obtained. To compare designs their e ciencies were calculated
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Viana, Rodrigo Sartorelo Salemi. "Programação linear à criação de planejamentos otimizados em radioterapia /." Botucatu : [s.n.], 2010. http://hdl.handle.net/11449/87916.

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Анотація:

Orientador: Helenice de Oliveira Florentino Silva
Banca: Diana Rodrigues de Pina
Banca: Maria do Socorro Nogueira Rangel
Resumo: Um planejamento para radioterapia é considerado ótimo quando todos os parâmetros envolvidos sejam eles físicos ou biológicos, foram investigados e adequados individualmente para cada paciente. Neste tipo de planejamento, a grande preocupação é com a irradiação do tumor com o mínimo dano possível aos tecidos saudáveis da região irradiada, principalmente aos órgãos de riscos. O planejamento ótimo para radioterapia pode ser auxiliado pela Programação Linear e existe uma ampla literatura abordando este assunto, mas, a maioria das formulações matemáticas publicadas não contemplam um cenário do ponto de vista de aplicações práticas, pois não incorporam determinados fatores que são de extrema importância para a construção de um planejamento real, como exemplos a atenuação do feixe de radiação e a beterogeneidade na composição dos tecidos irradiados. Assim, este trabalho apresenta uma metodologia para correção de heterogeneidade na composição dos diferentes tipos de tecidos irradiados baseado nas proporções entre seus diferentes coeficientes de atenuação linear. Esta metodologia tem como objetivo tornar as simulações de planejamentos otimizados mais próximos dos planejamentos reais e desta forma, possibilitar um estudo mais amplo e confiável, faznedo com que modelos de programação linear possam ser utilizados como ferramentas auxiliares na criação de planejamentos reais para radioterapia
Abstract: Planning for radiotherapy is considered optimal when all the parameters involved, physical or biological, have been investigated and are appropriate for each patient. In this type of planning, the major concern is with the irradiation of the tumor with the minimum possible damage to healthly tissues of the irradiated region, especially the organs at risk. The optimal planning for radiation therapy can be aided by Linear Programming and there is a wide literature addressing this subject. However, most published mathematical formulations do not contemplate a scenario in terms of practical applications. They do not incorporate certain factors that are extremely important for building a real planning, as examples there are attenuation of the radiation beam and the heterogeneous composition of the irradiated tissue. This work presents a methodology for correction of heterogeneity in the composition of different types of tissuers irradiated based on the proportions among their different linear attenuation coefficient. This methodology aims to make the simulations of optimized planning closer to the real planning and thus enable a more comprehensive and reliable, allowing the use of linear programming models as aids in the creation of real planning for radiotherapy
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Книги з теми "Ecg biometric"

Lee, Jimmy Kim-Mil. ECG feature extraction without fiducial detection: Applications to ECG biometric recognition. 2006.

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Bilański, Piotr. Trypodendron laeve Eggers w Polsce na tle wybranych aspektów morfologicznych i genetycznych drwalników (Trypodendron spp., Coleoptera, Curculionidae, Scolytinae). Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-38-0.

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In Poland, there are 4 species of the liypodendron genus: T lineaium Oliv., T domestkum L., T signature Fakir. and 7: laeve Egg. Trypodendron laeve is the leastknown of this group. Many factors had influence on the state of research on this species, including taxonomic aspects. Taking into account the unsatisfactory state of knowledge regarding the prevalence of T iaeve in Poland, as well as scarce information on the morphology of this species, research was undertaken to I) document the presence, including new sites, of T laeve in Poland and define, if possible, the habitat and trophic conditions that may affect its occurrence, as well as II) determinate suitability of biometric and genetic methods for correct identification of t laeve against the background of other ambrosia beetle species. Research on the occurrence of T laeve in Poland, was carried out on 143 areas located throughout the country, representing various environmental conditions, primarily such as species composition of tree stands, terrain, altitude (from 16 to 929 meters above sea level) and their location in relation to zoogeographic regions. The research material was obtained mainly using various types of traps for catching ambrosia beetles baited with pheromone. Only in a few cases when attacking the wood of trees, the imagines of ambrosia beetles were obtained without luring agents. The research was conducted in 2007-2016. From the insect individuals identified on the grounds of morphological traits as T lineatum, T laeve, T domesticum and T signatum, originating from selected locations in Poland, 3-11 specimens were collected, for which genetic analyses were performed based on the COI gene fragments obtained by PCR. The research included tests for following paramcter: s sequence similarity, phylogenetic, evolutionary divergence and genetic. structure. As a result of research on the occurrence of ambrosia beetles in Poland, a total of 44207 individuals belonging to four species were collected: T lineatutn, 7: laeve, T domesticum and T signatum, whose share was respectively: 49.2%, 31.4%; 19.1% and 0.3%. In Poland, 1: laeve's imagines were found in 124 out of 143 examined sites. The presence of L reeve has been documented for the first time in 14 zoogeographic regions. This species was commonly found on study areas located from 118 to 929 m above sea level. In Poland the tree species attacked by T Mate include Pinus sylvestris L. and Picea abies (L) H. Karst. In Poland, T laeve as a host plant prefers sylvestris and reaches the highest population densities in the stands of this species. The work presents the exact morphological characteristics of T laeve and indicates the most important features that distinguish it from the other Trypodendrun spp. occurring in Poland. It has also been shown that the best results in the determination of species of the liypodendron genus, regardless of their sex, can be obtained using phylogenetic analysis based on a fragment of the COI gene.

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Частини книг з теми "Ecg biometric"

Pal, Anita, and Yogendra Narain Singh. "ECG Biometric Recognition." In Communications in Computer and Information Science, 61–73. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0023-3_7.

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Zheng, Gang, Shengzhen Ji, Min Dai, and Ying Sun. "ECG Based Identification by Deep Learning." In Biometric Recognition, 503–10. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_54.

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Zheng, Gang, Xiaoxia Sun, Shengzhen Ji, Min Dai, and Ying Sun. "ECG Based Biometric by Superposition Matrix in Unrestricted Status." In Biometric Recognition, 553–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97909-0_59.

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Dai, Min, Baowen Zhu, Gang Zheng, and Yisha Wang. "A Method of ECG Identification Based on Weighted Correlation Coefficient." In Biometric Recognition, 633–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_74.

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Sun, Yanwen, Gongping Yang, Yuwen Huang, Kuikui Wang, and Yilong Yin. "Learning Discriminative Representation for ECG Biometrics Based on Multi-Scale 1D-PDV." In Biometric Recognition, 415–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31456-9_46.

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Saini, Sanjeev Kumar, Guru Gobind Singh, and Rashmi Gupta. "ECG-Based Biometric Authentication Systems Using Artificial Intelligence Methods." In Multimodal Biometric Systems, 61–77. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003138068-5.

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Djelouat, Hamza, Mohammed Al Disi, Abbes Amira, Faycal Bensaali, and Xiaojun Zhai. "Compressive Sensing Based ECG Biometric System." In Advances in Intelligent Systems and Computing, 126–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_11.

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Srivastva, Ranjeet, and Yogendra Narain Singh. "ECG Biometric Analysis Using Walsh–Hadamard Transform." In Advances in Data and Information Sciences, 201–10. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8360-0_19.

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Gupta, Ashish, Rajesh Kumar, and Devvrat Tyagi. "Wireless Sensor Network for IoT-Based ECG Monitoring System Using NRF and LabVIEW." In Multimodal Biometric Systems, 125–34. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003138068-10.

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Diab, Mohamad O., Alaa Seif, Maher Sabbah, Mohamad El-Abed, and Nijez Aloulou. "A Review on ECG-Based Biometric Authentication Systems." In Series in BioEngineering, 17–44. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0956-4_2.

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Тези доповідей конференцій з теми "Ecg biometric"

Bashar, Md Khayrul, Yuji Ohta, and Hiroaki Yoshida. "ECG-based biometric authentication using mulscale descriptors: ECG-based biometric authentication." In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2015. http://dx.doi.org/10.1109/iciibms.2015.7439465.

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Plataniotis, Konstantinos N., Dimitrios Hatzinakos, and Jimmy K. M. Lee. "ECG Biometric Recognition Without Fiducial Detection." In 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference. IEEE, 2006. http://dx.doi.org/10.1109/bcc.2006.4341628.

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Carvalho, João, Susana Brás, and Armando Pinho. "Entropy-Based ECG Biometric Identification." In Entropy 2021: The Scientific Tool of the 21st Century. Basel, Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/entropy2021-09795.

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Sanchez-Casanova, Jorge, Antonio Miranda-Escalada, Raul Sanchez-Reillo, and Pablo Bartolome-Molina. "ECG biosignals in biometric recognition." In 2017 International Carnahan Conference on Security Technology (ICCST). IEEE, 2017. http://dx.doi.org/10.1109/ccst.2017.8167817.

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Wang, Kuikui, Gongping Yang, Lu Yang, Yuwen Huang, and Yilong Yin. "STERLING: Towards Effective ECG Biometric Recognition." In 2021 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2021. http://dx.doi.org/10.1109/ijcb52358.2021.9484360.

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Tantawi, M., A. Salem, and M. F. Tolba. "ECG signals analysis for biometric recognition." In 2014 14th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2014. http://dx.doi.org/10.1109/his.2014.7086192.

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Fatimah, Binish, G. Priyanka, Rehana Sultana, and N. Rekha. "Analysis of ECG for biometric identification." In 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020. http://dx.doi.org/10.1109/icccnt49239.2020.9225361.

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Safie, S. I. "ECG slope features for Biometric Authentication." In 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). IEEE, 2018. http://dx.doi.org/10.1109/icsima.2018.8688793.

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Loong, Justin Leo Cheang, Sim Kok Swee, Rosli Bear, Khazaimatol S. Subari, and Muhammad Kamil Abdullah. "Effects of diseased ECG on the robustness of ECG biometric systems." In 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). IEEE, 2010. http://dx.doi.org/10.1109/iecbes.2010.5742250.

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"TOWARDS A FINGER BASED ECG BIOMETRIC SYSTEM." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003286803480353.

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Звіти організацій з теми "Ecg biometric"

Watson, Craig I., Michael D. Garris, Elham Tabassi, Charles L. Wilson, R. Michael McCabe, Stanley Janet, and Kenneth Ko. User's guide to export controlled distribution of NIST biometric image software (NBIS-EC). Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.ir.7391.

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