Healthcare Decision-Making with an ECG-Based Biometric System

Biometric systems that provide accurate identification are essential for making informed decisions in various fields. In this study, we propose a novel approach for identifying medical patients using the QRS waveform of their ECG signal. ECG signals are a highly secure biometric trait as they are difficult to spoof and reproduce. To derive a representative QRS waveform, the signals were subject to preprocessing via a 4th-order bandpass filter, amplitude normalization, and R-peak detection through the Pan-Tompkins algorithm. Subsequently, a vector encompassing 50 samples before and after each index was generated. An identification accuracy of 99.47% was achieved using 48 healthy subjects from the PTB Diagnostic ECG Database. To further test the system, we used 282 healthy and diseased subjects from the same database and achieved an accuracy of 97.60%. Our proposed approach provides a promising solution for biometric identification, which can improve decision-making in various fields such as security and healthcare. With the increasing demand for accurate identification, our study highlights the potential for ECG-based biometrics as a secure and reliable modality for decision-making.