Key result
ECG biometric system using JPEG2000 compression achieves 100% recognition accuracy in healthy and diseased subjects.
A novel method using JPEG2000 compressed ECG signals enables accurate and efficient biometric person identification without full decompression.
May support compressed ECG biometrics in mixed cohorts; leaves open large-scale validation before any clinical or security use.
In wireless telecardiology applications, electrocardiogram (ECG) signals are often represented in compressed format for efficient transmission and storage purposes. Incorporation of compressed ECG based biometric enables faster person identification as it by-passes the full decompression. This study presents a new method to combine ECG biometrics with data compression within a common JPEG2000 framework. To this end, an ECG signal is considered as an image and the JPEG2000 standard is applied for data compression. Features relating to ECG morphology and heartbeat intervals are computed directly from the compressed ECG. Different classification approaches are used for person identification. Experiments on standard ECG databases demonstrate the validity of the proposed system for biometric identification with high accuracies on both healthy and diseased subjects.
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WU et al. (2015) studied Healthy and cardiovascular disease (arrhythmia, sudden cardiac death) (n=126). JPEG2000 compressed ECG signals with SVM classifier (FS3 feature set) vs. Uncompressed ECG signals or NN classifier was evaluated on Recognition rate (accuracy). The proposed ECG biometric system using JPEG2000 compressed signals and an SVM classifier achieved a 100% recognition rate on a combined dataset of healthy and diseased subjects.
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