Pattern Analysis Towards Human Verification using Photoplethysmograph Signals

Khan, Muhammad Umar; Aziz, Sumair; Naqvi, Syed Zohaib Hassan; Zaib, Ahmed; Maqsood, Aiman

doi:10.1109/icetst49965.2020.9080751

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…It is possible to divide the methods for PPG-based biometric recognition into three categories [13]: i) methods based on algoritmic approaches [2,32,31,35,10]; ii) methods based on handcrafted features and machine learning classifiers [14,29,5,18,19,36,33,37,25,22]; iii) methods based on DNNs [17,26,9,1,24,13].…”

Section: Related Workmentioning

confidence: 99%

“…The classification is performed by comparing several methods such as k-NN, Fuzzy k-NN, and Gaussian Mixture Model. Similarly, a comparison with k-NN and other classifiers such as decision trees and SVM is also presented in [22].…”

Section: Related Workmentioning

confidence: 99%

“…The state-of-the-art methods for biometric recognition based on PPG signals can use algorithmic approaches [2,32,31,35,10], machine learning techniques based on hand-crafted features [37,25,22,24,33,18,19,29,5,23,14,36], or DNNs [13,1,17,26,9]. To the best of our knowledge, all the methods in the literature process PPG signals using features directly extracted from one-dimensional representations of the signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Labati

Piuri

Rundo

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Nowadays, the number of mobile, wearable, and embedded devices integrating sensors for acquiring cardiac signals is constantly increasing. In particular, plethysmographic (PPG) sensors are widely diffused thanks to their small form factor and limited cost. For example, PPG sensors are used for monitoring cardiac activities in automotive applications and in wearable devices as smartwatches, activity trackers, and wristbands. Recent studies focused on using PPG signals to secure mobile devices by performing biometric recognitions. Although their results are promising, all of these methods process PPG acquisitions as one-dimensional signals. In the literature, feature extraction techniques based on transformations of the spectrogram have been successfully used to increase the accuracy of signal processing techniques designed for other application scenarios. This paper presents a preliminary study on a biometric recognition approach that extracts features from different transformations of the spectrogram of PPG signals and classifies the obtained feature representations using machine learning techniques. To the best of our knowledge, this is the first study in the literature on biometric systems that extracts features from the spectrogram of PPG signals. Furthermore, with respect to most of the state-of-the-art biometric recognition techniques, the proposed approach presents the advantage of not requiring the search of fiducial points, thus reducing the computational complexity and increasing the robustness of the signal preprocessing step. We performed tests using a dataset of samples collected from 42 individuals, obtaining an average classification accuracy of 99.16% for identity verification (FMR of 0.56% at FNMR of 13.50%), and a rank-1 identification error of 7.24% for identification. The results obtained for the considered dataset are better or comparable with respect to the ones of the best-performing methods in the literature.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Labati

Piuri

Rundo

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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show abstract

“…The PuPG signal includes considerable cardiac health characteristic information [ 25 , 26 , 27 ]. The PuPG signals are recently being used for emotions classification [ 28 ] and biometric systems [ 29 ] as well. The main contributions of this work are listed as follows: This is the first study that used PuPG-based signals for the detection of hypertension.…”

Section: Introductionmentioning

confidence: 99%

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

Khan

Aziz

Akram

et al. 2021

Sensors

Self Cite

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Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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PulseID: Multi-scale photoplethysmographic identification using a deep convolutional neural network

Wei,

Xu,

et al. 2024

Biomedical Signal Processing and Control

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Pattern Analysis Towards Human Verification using Photoplethysmograph Signals

Cited by 12 publications

References 28 publications

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Biometric Recognition of PPG Cardiac Signals Using Transformed Spectrogram Images

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme

PulseID: Multi-scale photoplethysmographic identification using a deep convolutional neural network

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