Biometric Authentication with ECG Signals: A Secure Identification Model Based on Convolutional Autoencoders

Merve Akkuş; Murat Karabatak

Article Details

Biometric Authentication with ECG Signals: A Secure Identification Model Based on Convolutional Autoencoders

Authors:

Merve Akkuş | Murat Karabatak

Publisher:

Fırat University

Main Topics:

Makine Öğrenmesi Algoritmaları | Makine Öğrenme (Diğer)

Journal:

Fırat Üniversitesi Fen Bilimleri Dergisi Vol.37 Issue 2, pp.65-78

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001357966

pISSN:

1308-9064

Keywords:

Abstract:

 Electrocardiography ECG signals provide a unique opportunity for biometric identification by capturing individual electrical properties of the heart. This study explores ECG-based identity recognition using convolutional autoencoders CAE. The proposed method efficiently extracts features from ECG signals, constructing a compact and meaningful representation for identification. Unlike traditional methods, CAE separates intervals with feature expansion and landmark detection, addressing limitations in existing literature. The study employs the MIT-BIH Arrhythmia ECG dataset, ensuring diverse and representative training data. By learning key features and reducing dimensionality, the model compresses and reconstructs input data for precise classification. Recognizing the sensitivity of personal medical data, robust data protection strategies, including encryption and compression, are implemented. Experimental results show a high accuracy of 98.46 in ECG-based identification, validating the approach as an effective biometric authentication method. The findings highlight the potential of cardiac electrical activity as a distinctive biometric identifier. The proposed model contributes to biometric recognition by integrating machine learning techniques and stringent security measures, offering a balanced approach between technological advancement and data privacy. This research paves the way for secure and reliable personal identification using ECG signals.

 Electrocardiography ECG signals provide a unique opportunity for biometric identification by capturing individual electrical properties of the heart. This study explores ECG-based identity recognition using convolutional autoencoders CAE. The proposed method efficiently extracts features from ECG signals, constructing a compact and meaningful representation for identification. Unlike traditional methods, CAE separates intervals with feature expansion and landmark detection, addressing limitations in existing literature. The study employs the MIT-BIH Arrhythmia ECG dataset, ensuring diverse and representative training data. By learning key features and reducing dimensionality, the model compresses and reconstructs input data for precise classification. Recognizing the sensitivity of personal medical data, robust data protection strategies, including encryption and compression, are implemented. Experimental results show a high accuracy of 98.46 in ECG-based identification, validating the approach as an effective biometric authentication method. The findings highlight the potential of cardiac electrical activity as a distinctive biometric identifier. The proposed model contributes to biometric recognition by integrating machine learning techniques and stringent security measures, offering a balanced approach between technological advancement and data privacy. This research paves the way for secure and reliable personal identification using ECG signals.

 Electrocardiography ECG signals provide a unique opportunity for biometric identification by capturing individual electrical properties of the heart. This study explores ECG-based identity recognition using convolutional autoencoders CAE. The proposed method efficiently extracts features from ECG signals, constructing a compact and meaningful representation for identification. Unlike traditional methods, CAE separates intervals with feature expansion and landmark detection, addressing limitations in existing literature. The study employs the MIT-BIH Arrhythmia ECG dataset, ensuring diverse and representative training data. By learning key features and reducing dimensionality, the model compresses and reconstructs input data for precise classification. Recognizing the sensitivity of personal medical data, robust data protection strategies, including encryption and compression, are implemented. Experimental results show a high accuracy of 98.46 in ECG-based identification, validating the approach as an effective biometric authentication method. The findings highlight the potential of cardiac electrical activity as a distinctive biometric identifier. The proposed model contributes to biometric recognition by integrating machine learning techniques and stringent security measures, offering a balanced approach between technological advancement and data privacy. This research paves the way for secure and reliable personal identification using ECG signals.

 Elektrokardiyografi EKG sinyalleri, kalbin bireysel elektriksel özelliklerini yakalayarak biyometrik tanımlama için benzersiz bir fırsat sağlar. Bu çalışma, konvolüsyonel oto kodlayıcılar CAE kullanarak EKG tabanlı kimlik tanımayı araştırmaktadır. Önerilen yöntem, EKG sinyallerinden özellikleri verimli bir şekilde çıkararak kimlik tespiti için kompakt ve anlamlı bir temsil oluşturmaktadır. Geleneksel yöntemlerin aksine CAE, özellik genişletme ve dönüm noktası tespiti ile aralıkları ayırarak mevcut literatürdeki sınırlamaları ele alır. Çalışmada, çeşitli ve temsili eğitim verileri sağlayan MIT-BIH Aritmi EKG veri seti kullanılmıştır. Temel özellikleri öğrenerek ve boyutluluğu azaltarak model, hassas sınıflandırma için girdi verilerini sıkıştırır ve yeniden yapılandırır. Kişisel tıbbi verilerin hassasiyeti göz önünde bulundurularak, şifreleme ve sıkıştırma dahil olmak üzere sağlam veri koruma stratejileri uygulanmaktadır. Deneysel sonuçlar EKG tabanlı tanımlamada 98.46 gibi yüksek bir doğruluk oranı göstererek yaklaşımın etkili bir biyometrik kimlik doğrulama yöntemi olduğunu doğrulamaktadır. Bulgular, ayırt edici bir biyometrik tanımlayıcı olarak kardiyak elektriksel aktivitenin potansiyelini vurgulamaktadır. Önerilen model, teknolojik ilerleme ve veri gizliliği arasında dengeli bir yaklaşım sunarak makine öğrenimi tekniklerini ve sıkı güvenlik önlemlerini entegre ederek biyometrik tanımaya katkıda bulunmaktadır. Bu araştırma, EKG sinyallerini kullanarak güvenli ve güvenilir kişisel tanımlamanın yolunu açmaktadır.

Article Details

Biometric Authentication with ECG Signals: A Secure Identification Model Based on Convolutional Autoencoders

Authors:

Merve Akkuş | Murat Karabatak

Publisher:

Fırat University

Main Topics:

Makine Öğrenmesi Algoritmaları | Makine Öğrenme (Diğer)

Journal:

Fırat Üniversitesi Fen Bilimleri Dergisi Vol.37 Issue 2, pp.65-78

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001357966

pISSN:

1308-9064

Biometric authentication

convolutional autoencoders (CAE)

feature extraction

data dimensionality reduction

privacy and data security

identification accuracyBiyometrik kimlik doğrulama

Evrişimsel otokodlayıcılar (CAE)

özellik çıkarma

sinyal sıkıştırma

gizlilik ve veri güvenliği.

Issues

Article Details

Authors: Merve Akkuş | Murat Karabatak

Publisher: Fırat University

Main Topics: Makine Öğrenmesi Algoritmaları | Makine Öğrenme (Diğer)

Journal: Fırat Üniversitesi Fen Bilimleri Dergisi Vol.37 Issue 2, pp.65-78

Document Type: Article

Publication Date: 9/2025

Document Language: English

Document ID (Org No.): 2001357966

pISSN: 1308-9064

Issues

Authors:

Merve Akkuş | Murat Karabatak

Publisher:

Fırat University

Main Topics:

Makine Öğrenmesi Algoritmaları | Makine Öğrenme (Diğer)

Journal:

Fırat Üniversitesi Fen Bilimleri Dergisi Vol.37 Issue 2, pp.65-78

Document Type:

Article

Publication Date:

9/2025

Document Language:

English

Document ID (Org No.):

2001357966

pISSN:

1308-9064