Shah Ameet, Singh Dhanpratap, Mohamed Heba G, Bharany Salil, Rehman Ateeq Ur, Hussen Seada
School of Computer Science and Engineering, Lovely Professional University, Grand Trunk Rd, Phagwara, 144411, Punjab, India.
Department of Electrical Engineering, College of Engineering , Princess Nourah bint Abdulrahman University, Riyadh, P.O. Box 84428, 11671, Saudi Arabia.
Sci Rep. 2025 Mar 18;15(1):9230. doi: 10.1038/s41598-025-93906-5.
Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.
年轻人心脏骤停是近期全球面临的一种风险，并且人们注意到患有心律失常的人更容易患各种心脏病。人工分类容易出错，当然，需要自动化来对心电图信号进行分类，以准确预测心律失常。所提出的自注意力人工智能自动编码器算法被证明是一种有效的心律失常分类策略，采用了新颖的改进卡尔曼滤波器预处理。对于-5db，我们实现了24.00的信噪比改善（SNRimp）、0.055的均方根误差（RMSE）、22.1%的百分比残差（PRD）；对于其他情况，分别为20.4的SNRimp、0.0245的RMSE、12%的PRD，以及14.05的SNRimp、0.010的RMSE和7.25%的PRD，这在预处理过程中降低了心电图信号噪声，提高了心电图波形的QRS复合波和R-R波峰的可见性。提取的特征被用于神经元网络，以使用新开发的自注意力自动编码器（AE）算法对麻省理工学院-比哈尔心律失常数据库进行分类。将结果与现有模型进行比较，结果表明所提出的系统在心律失常的分类和预测方面表现出色，精确率为99.91%，召回率为99.86%，准确率为99.71%。证实了自注意力自动编码器的训练结果很有前景，它有助于诊断复杂心脏状况的心电图，以解决现实世界中的心脏问题。
