| In recent years,with the rapid development of information technology and widespread of the internet,personal identification has been widely used in applications such as financial security,access control,security monitoring and material confidentiality,et al.Nowadays,reliable,stable,simple and low-cost personal identification technologies have broad prospects in the field of information security.Since the traditional identification tools are prone to be lost,forgotten and stolen,they can no longer meet people’s need for personalized privacy protection.To solve the above problem,biometric identification is proposed.As a physiological signal,the electrocardiogram(ECG)can not only reflect the health state of heart,but also contains rich personal identity information.ECG satisfies the necessary requirements for being a biometric,and thus can be used for biometric identification.Moreover,compared with such external biometrics as face and fingerprint,ECG also has its own unique advantages:(1)As an electrical signal inside the body,ECG is much more difficult to be stolen;(2)ECG comes with the attribute of liveness detection,which makes it able to effectively avoid problems caused by synthetic samples,e.g.,photos or gummy fingers that have fingerprint impressions.Due to the above advantages,ECG biometric identification can greatly improve the security of identification,and thus has broad application prospects in the future.Currently,pattern recognition methods are widely adopted by researchers to explore the relationship between ECG signals and personal identity through automatic computer analysis.In the past decades,great progress has been made in ECG biometric identification,and a lot of researches have been implemented.However,due to the complexity of ECG signal and the interference of noise,it is still a big challenge to realize robust and accurate ECG biometric identification in practical application.In order to solve the existing problems in ECG biometric identification,this thesis makes robust ECG biometric identification researches from three respects,namely,robustness to noise,robustness to heart rate change and robustness to fiducial-point detection error,based on the characteristics of ECG signal.The main contents and contributions of this study are presented as follows:1.In the data acquisition process,ECG signal will inevitably be mixed with a variety of noise.The noise will distort the ECG waveform and lead to the decline of identification accuracy.Although denoising process can effectively remove the noise,it also exists the problems of poor universality and easy to filter out identity-related information.To eliminate the adverse effects of denoising process on ECG identification,this thesis combines Discrete Wavelet Transformation and sparse autoencoder to propose a novel temporal-frequency autoencoding based method for noise robust ECG identification,whose main idea is to incorporate denoising process into feature extraction step.In the method,heartbeats are firstly decomposed into multiple time-frequency components by Discrete Wavelet Transformation.Then,feature selection is performed on the obtained components according to the time-frequency distribution of ECG waveforms and noise.By only preserving time-frequency components corresponding to the key waveforms,noise can be efficiently removed.At last,the stacked sparse autoencoder is introduced to implement discriminative feature extraction and classification while depressing the residual noise in a feature learning way.Without considering the impact of heart rate change,the effectiveness of the method is verified by performing comparison experiments on the ECG-ID heart rate stabilization dataset and the MIT-BIH-AHA database.Experimental results show that the proposed method can directly extract effective features from the original noisy signals and achieve high identification accuracy without requiring denoising process.2.When the state of motion or emotion changes,T wave shift will occur on ECG signals due to the heart rate change.The T wave shift can change the morphology of ECG signals,leading to the decline of identification accuracy.To solve the above problem and satisfy the real scenario application,a novel ECG identification method,which does not require denoising process and is robust to heart rate change,is proposed.In the method,firstly,all the heartbeats are normalized by a novel T-QRS-T resampling strategy to correct T wave shift.Compared with traditional T wave shift correction methods,the significant advantage of the proposed T-QRS-T resampling strategy is that it does not rely on the detection of fiducial points with weak amplitude value,e.g.,Q and S.Thus it can better satisfy the requirement of complex real scenario application.Then in the feature extraction process,to solve theproblem that traditional neural networks,e.g.,autoencoder,have numerous parameters and complex optimization process,the principal component analysis network(PCANet)with much less parameters and good noise robustness is adopted to learn discriminative features from normalized noisy heartbeats.Finally,linear support vector machine is adopted to classify the extracted features.To verify the effectiveness of the proposed method,comparison experiments are performed on both ECG-ID heart rate change dataset and ECG-ID dataset with all data.Experimental results show that the proposed method can effectively solve the problem of T wave shift,and realize noise robust ECG biometric identification under heart rate change condition.3.To eliminate the adverse effects of insufficient accuracy of existing fiducial-point detection method on ECG biometric identification,a novel ECG identification method based on combined segment features,which is robust to fiducial-point detection error,is proposed.In the method,firstly,a fixed-length sliding window is used to partition the original ECG signal into multiple short segments,which will replace heartbeat as basic identification unit in the following steps.Secondly,considering the difference of morphology and content among the obtained segments,global and key local representation of the segments are respectively obtained from the perspective of statistical domain and time domain.Here,the statistical domain representation is obtained by Autocorrelation/Discrete Cosine Transformation,and the time domain representation is obtained by the proposed MAX feature extraction method.Thirdly,PCANet is adopted to respectively extract features from the statistical domain representation and the time domain representation,and the extracted features are combined together to realize information complementation.Finally,linear support vector machine is adopted to classify the combined features.To verify the effectiveness of the proposed method,comparison experiments are conducted on ECG-ID heart rate change dataset,ECG-ID heart rate stabilization dataset,the MIT-BIH-AHA database and ECG-ID dataset with all data.Experimental results demonstrate that the proposed method can effectively improve the identification accuracy without requiring fiducial point detection in the preprocessing step. |
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