Dataset Information

Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.

ABSTRACT: Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively.

SUBMITTER: Yang M

PROVIDER: S-EPMC3748040 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.

Yang Meixue M Liu Bin B Zhao Miaomiao M Li Fan F Wang Guoqing G Zhou Fengfeng F

PloS one 20130820 8

Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, ...[more]

PMID: 23977063

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Project description:Biometric technology is becoming increasingly prevalent in several vital applications that substitute traditional password and token authentication mechanisms. Recognition accuracy and computational cost are two important aspects that are to be considered while designing biometric authentication systems. Thermal imaging is proven to capture a unique thermal signature for a person and thus has been used in thermal face recognition. However, the literature did not thoroughly analyse the impact of feature selection on the accuracy and computational cost of face recognition which is an important aspect for limited resources applications like IoT ones. Also, the literature did not thoroughly evaluate the performance metrics of the proposed methods/solutions which are needed for the optimal configuration of the biometric authentication systems. This paper proposes a thermal face-based biometric authentication system. The proposed system comprises five phases: a) capturing the user's face with a thermal camera, b) segmenting the face region and excluding the background by optimized superpixel-based segmentation technique to extract the region of interest (ROI) of the face, c) feature extraction using wavelet and curvelet transform, d) feature selection by employing bio-inspired optimization algorithms: grey wolf optimizer (GWO), particle swarm optimization (PSO) and genetic algorithm (GA), e) the classification (user identification) performed using classifiers: random forest (RF), k-nearest neighbour (KNN), and naive bayes (NB). Upon the public dataset, Terravic Facial IR, the proposed system was evaluated using the metrics: accuracy, precision, recall, F-measure, and receiver operating characteristic (ROC) area. The results showed that the curvelet features optimized using the GWO and classified with random forest could help in authenticating users through thermal images with performance up to 99.5% which is better than the results of wavelet features by 10% while the former used 5% fewer features. In addition, the statistical analysis showed the significance of our proposed model. Compared to the related works, our system showed to be a better thermal face authentication model with a minimum set of features, making it computational-friendly.

Project description:ObjectivesThis study aimed to evaluate a biometric palm vein authentication system to prevent medication administration errors in psychiatric hospitals.DesignThis is a pre-post observational study.SettingConventionally, the medication was distributed after a double check. We developed and introduced a new medication administration cart in two psychiatric hospitals in Japan, in which each patient-specific drug box had to be electronically opened only by palm vein authentication.ParticipantsA total of 3444 and 3523 patients were present 18 months before and after introducing the cart, respectively. Of the 212 nurses recruited, 28 were excluded due to a lack of experience with the conventional medication administration system and incomplete questionnaires.Primary and secondary outcome measuresThe primary outcome was the efficacy of this system by comparing the incidence of medication administration errors before and after introducing the cart. The secondary outcome was a survey regarding nurses' attitudes toward this system.ResultsAfter introduction of the new system, the number of medication errors due to misidentification of persons relative to the total number of admitted patients was significantly reduced from 6/3444 to 2/3523 (p<0.0001). Among 184 nurses, 182 responded that anxiety regarding administration errors was either reduced or unchanged using this system. Male nurses reported a greater increase in work burden than female nurses (OR=3.11, 95% CI=1.44 to 6.72). Nurses working in chronic care wards reported greater time pressure than nurses working in emergency wards (OR=3.33, 95% CI=1.16 to 9.57). Nurses working in dementia care wards reported a greater patient care burden than nurses working in emergency wards (OR=5.67, 95% CI=1.22 to 26.27).ConclusionsThis new system might have potential for reducing the patient misidentification risk during medication without increasing the anxiety experienced by nurses concerning administration errors. However, system usability and efficiency must be improved to reduce additional work burden, time pressure and patient care burden.

Dataset Information

Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.

Publications

Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.

Similar Datasets

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