Age Classification Based on ECG QRS Wave Using Deep Learning

Electrocardiogram (ECG) signal, which represents electrical activity of human heart, is recently emerging as a secured biometric scheme. Some prior studies have found ECG as an estimator of cardiac age (indicating heart condition). Estimating chronological age from ECG can facilitate anonymous age verification. With that objective, we analyzed ECG records of 4SS4 subjects obtained from PTB-XL database (a large public dataset of 12-lead ECG). In our experimental dataset, 48% subjects were male and 52% were female, 56% were healthy subjects and 44% had some cardiac abnormalities. 1000 samples from each record were first passed through a band-pass filter, followed by discrete wavelet decomposition up to 4th level and reconstruction through the approximation coefficients (using sym-4 mother wavelet). The purpose was to obtain the QRS wave, a low frequency component of the ECG signal, which is considered as a good indicator of chronological age. This processed signal was then used for age classification using a deep neural network model; which consisted of 3 consequent layers of ID CNN, batch-normalization, max-pooling; LSTM layer, fully connected layers and a regression model for classification. 6 different age segments were used as classification labels (0-17 years, 18-29 years, 30-39 years, 40-49 years, 50-59 years and 60-69 years). As the result show, classification accuracy did not vary significantly between all subjects and healthy subjects, while experimenting with all age segments. However, accuracy significantly improved and showed less fluctuation while experimenting with discontinuous age segments. This indicates better effectiveness of ECG-based age segmentation for discontinuous age groups. Importantly, our proposed method achieved significantly lower MAE (mean absolute error) in comparison with other methods available in literature.