Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel
Abstract
:1. Introduction
2. Proposed Methodology
2.1. Signal Denoising
2.2. Signal Preparation
2.2.1. R-Peak Detection
2.2.2. Heartbeat Segmentation
2.2.3. Amplitude Normalisation
2.2.4. Outlier Detection and Removal
- Compute the normalised cross-correlation between each template () on the set and each of the others (), with . From all coefficients obtained, store in only the maximum.
- Get the average normalised cross-correlation for each template:
- Arrange A in descending order, and set an initial cluster with the n first templates;
- Get the mean m of the cluster, and compute ;
- Add the next template to the cluster if ;
- Repeat steps 4 and 5 until a template is rejected.
2.2.5. Ensemble Construction
2.3. Feature Extraction
- Discrete Cosine transform: The DCT coefficients were extracted from the ensemble heartbeats. The coefficients selected correspond to the frequency range [0, 40] Hz (total of 52 features);
- Haar Wavelet transform: The set of detail coefficients of the second level of decomposition with DWT using Haar wavelets was experimentally selected to serve as feature set for recognition (total of 163 features).
2.4. Recognition
- Support Vector Machines (SVM): SVM compute an optimal hyperplane dividing two classes, ensuring maximum margin between this boundary and the nearest samples. Kernels can be used to work with non-linearly separable datasets, and multiclass problems can be solved by combining binary classifiers [62];
- k-Nearest-Neighbours (kNN): kNN is a non-parametric, non-linear classifier. Based on the location of the object to be classified, kNN will find the k nearest train samples and predict the class most frequently verified [63];
- Multilayer Perceptrons (MLP): Multilayer Perceptrons are composed by neurons, which apply non-linear operations to their inputs. These are disposed in an input layer, which receives the features; an output layer, which outputs class scores; and a variable number of hidden layers in between. The connections between the neurons have their weights trained through error backpropagation [63];
- Gaussian Mixture Models - Universal Background Models (GMM-UBM): GMM models the distribution of the samples of each individual as a set of normal distributions, whose parameters can be used to classify unknown objects. UBM offers advantages in scarce training situations, by training the model with all samples first, and only then adapting for each subject [64].
- User-tuned authentication: This technique was inspired by the notion of individuality among subjects exposed on Biometric Menagerie [65,66]. As subjects are unique, user-tuned authentication used a bespoke threshold/reference for acceptance/rejection for each enrolled individual, instead of a single threshold shared by all;
- Past score weighting: Outliers are expected to be frequent in highly noisy settings. Past Score weighting aims to reduce outlier influence by adjusting the most recent score using past scores, weighted by their recency. With as the probability of the current sample belonging to class i, the weighted score was computed through:In Equation (4), N denotes the number of past scores to consider, and score weights were computed through a half-Gaussian function with tunable parameters:
3. Results and Discussion
3.1. Signal Denoising
3.2. Outlier Detection and Removal
3.3. Features and Recognition
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
BPF | Bandpass Filter |
DCT | Discrete Cosine Transform |
DWT | Discrete Wavelet Transform |
ECG | Electrocardiogram |
EEMD | Ensemble Empirical Mode Decomposition |
EER | Equal Error Rate |
FAR | False Acceptance Rate |
FRR | False Rejection Rate |
GMM | Gaussian Mixture Models |
HPF | Highpass Filter |
IDR | Identification Rate |
kNN | k-Nearest Neighbours |
LDA | Linear Discriminant Analysis |
MAF | Moving Average Filter |
MLP | Multilayer Perceptron |
NCCC | Normalised Cross-Correlation Clustering |
PCA | Principal Component Analysis |
RBF | Radial Basis Function |
SG | Savitzky-Golay |
SIMCA | Soft Independent Modelling of Class Analogy |
SVM | Support Vector Machines |
UBM | Universal Background Models |
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Pinto, J.R.; Cardoso, J.S.; Lourenço, A.; Carreiras, C. Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel. Sensors 2017, 17, 2228. https://doi.org/10.3390/s17102228
Pinto JR, Cardoso JS, Lourenço A, Carreiras C. Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel. Sensors. 2017; 17(10):2228. https://doi.org/10.3390/s17102228
Chicago/Turabian StylePinto, João Ribeiro, Jaime S. Cardoso, André Lourenço, and Carlos Carreiras. 2017. "Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel" Sensors 17, no. 10: 2228. https://doi.org/10.3390/s17102228
APA StylePinto, J. R., Cardoso, J. S., Lourenço, A., & Carreiras, C. (2017). Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel. Sensors, 17(10), 2228. https://doi.org/10.3390/s17102228