Cloud-based healthcare framework for real-time anomaly detection and classification of 1-D ECG signals

Abstract

Real-time data collection and pre-processing have enabled the recognition, realization, and prediction of diseases by extracting and analysing the important features of physiological data. In this research, an intelligent end-to-end system for anomaly detection and classification of raw, one-dimensional (1D) electrocardiogram (ECG) signals is given to assess cardiovascular activity automatically. The acquired raw ECG data is pre-processed carefully before storing it in the cloud, and then deeply analyzed for anomaly detection. A deep learning-based auto-encoder(AE) algorithm is applied for the anomaly detection of 1D ECG time-series signals. As a next step, the implemented system identifies it by a multi-label classification algorithm. To improve the classification accuracy and model robustness the improved feature-engineered parameters of the large and diverse datasets have been incorporated. The training has been done using the amazon web service (AWS) machine learning services and cloud-based storage for a unified solution. Multi-class classification of raw ECG signals is challenging due to a large number of possible label combinations and noise susceptibility. To overcome this problem, a performance comparison of a large set of machine algorithms in terms of classification accuracy is presented on an improved feature-engineered dataset. The proposed system reduces the raw signal size up to 95% using wavelet time scattering features to make it less compute-intensive. The results show that among several state-of-the-art techniques, the long short-term memory (LSTM) method has shown 100% classification accuracy, and an F1 score on the three-class test dataset. The ECG signal anomaly detection algorithm shows 98% accuracy using deep LSTM auto-encoders with a reconstructed error threshold of 0.02 in terms of absolute error loss. Our approach provides performance and predictive improvement with an average mean absolute error loss of 0.0072 for normal signals and 0.078 for anomalous signals.

Fig 1. Automatic ECG classification flow.

Fig 2. Illustration of the cloud-based healthcare system.

Fig 3. Unfiltered ECG signal.

Fig 4. Detrend & denoised ECG signal.

Fig 5. LSTM cell diagram [66].

Fig 6. Signal plot of each class.

Fig 7. Peak and interval detection in ECG Signal.

Fig 8. Detection of QRS interval.

Fig 9. Measurement of HRV.

Fig 10. Wavelet transform and scaling function.

Fig 11. Model summary.

Fig 12. Distribution of normal signals loss.

Fig 13. Distribution of anomaly signals Loss.

Fig 14. Reconstruction error of normal & anomaly signals.

Fig 15. Feature selection.

Fig 16. LSTM confusion matrix for ECG classification.

Fig 17. Training & validation accuracy/loss curves.

Fig 18. ROC curves for each class using KNN-weighted.

Fig 19. ROC curves for each class using SVM-cubic.

Fig 20. ROC curves for each class using LSTM.

Fig 21. Precision-recall curve (PRC) of LSTM.