Distinguishing Hearts: How Machine Learning identifies People based on their Heartbeat

Lipps, Christoph; Bergkemper, Lea; Schotten, Hans D.

doi:10.1109/icabme53305.2021.9604855

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The evaluation result shows that the cubic SVM has the highest accuracy of 98.4%. The authors of [20] compared three different Machine Learning algorithms: KNN, SVM, and Gaussian Naive Bayes (GNB). The result shows an accuracy of over 90%.…”

Section: Ecg-based Recognition Methodsmentioning

confidence: 99%

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Aleidan,

Abbas,

Daadaa

et al. 2023

Applied Sciences

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User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition.

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Section: Ecg-based Recognition Methodsmentioning

confidence: 99%

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Aleidan,

Abbas,

Daadaa

et al. 2023

Applied Sciences

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“…Multinomial classification involves categorizing data into more than two classes, in our case 202 classes, based on the defined features and attributes. ML classifiers are selected based on the thorough literature research; we specifically chose models that demonstrated efficiency on ECG features similar to ours (Hadiyoso et al, 2019;Israel et al, 2005;Lipps et al, 2021;Odinaka et al, 2012;Patro et al, 2017Patro et al, , 2022Sarkar et al, 2015;Zhang et al, 2017). As we delve deeper into the literature, we observe that the following classifiers could be broadly categorized into three distinct groups based on the decision-making mechanism:…”

Section: Machine Learningmentioning

confidence: 99%

CardioPRINT: Biometric identification based on the individual characteristics derived from cardiogram

Tanasković,

Lazarevic,

Knezevic

et al. 2023

Preprint

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Objective: This paper investigates the potential of cardiogram-derived traits from electrocardiogram (ECG) and impedance cardiogram (ICG) for biometric identification. Additionally, the influence of induced emotions on cardiogram attributes and their impact on identification accuracy is explored.Method: We conduct a comparative evaluation of 7 machine learning classifiers using dataset gathered from 202 individuals to identify the highest-performing classifiers. Subsequently, we analyze three different feature sets employing (ECG-only, ICG-only, and both ECG and ICG). Additionally, we investigate the performance of classifiers under altered emotional states to assess classifiers’ robustness.Results: The analysis demonstrates that models employed with both ECG and ICG have the highest accuracies that are statistically significant. The best-performing Random Forest (RF) model using both ECG and ICG achieves an average accuracy of 97.2%. All models reveal a decrease in classification accuracies (~13%) when they are not trained and tested under identical emotional conditions. Conclusion: Our findings suggest that integration of ECG and ICG-based features could increase the accuracy of identification compared to a single-signal-based approach. Although certain models show slight robustness to altered emotional state, the effect of the emotion is evident and future selection of cardiogram-based features, as well as biometric models, should consider emotional responses.

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Identification of Barriers to AI Implementation in Medical Information Systems

Pilch,

Zygała,

Gryncewicz

et al. 2024

Lecture Notes in Networks and Systems

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Distinguishing Hearts: How Machine Learning identifies People based on their Heartbeat

Cited by 4 publications

References 10 publications

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

CardioPRINT: Biometric identification based on the individual characteristics derived from cardiogram

Identification of Barriers to AI Implementation in Medical Information Systems

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