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A Signal Processing Framework for the Detection of Abnormal Cardiac Episodes

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Abstract

Motivation

Cardiologists rely on the long duration Holter electrocardiogram (ECG) recordings in general for assessment of abnormal episodes and such process found to be tedious and time consuming. An automatic abnormal cardiac episode detection algorithm is the need of the hour that needs to be optimized to reduce the manual burden.

Objective

The current study presents a signal processing framework with a cross-database to detect abnormal episodes in long-term ECG signals.

Methodology

The data was pre-processed to remove power line interference and baseline drift using basis pursuit sparsely decomposed tunable-Q wavelet transform (BPSD-TQWT). A total of 44 features of time domain, frequency domain, and time–frequency domain characteristics were extracted from the ECG signal. This proposed work tested classification performance with support vector machine (SVM), K-nearest neighbour (KNN), decision tree, naïve Bayes, the nearest mean classifier, and the nearest root mean square classifiers. The trained models with open-source data were used to predict the abnormal episodes from the proprietary database and vice versa. Finally, the performance was analysed via recall rate, specificity, precision, F1-score, and accuracy.

Results

Among six classification models, SVM performed best. With an open-source database, the SVM model achieved 95.01% accuracy, and detected the abnormal episodes from proprietary database with an accuracy of 99.31%. In addition, with the proprietary database SVM model classified the normal-abnormal cardiac episodes with an accuracy of 99.89% and detected the abnormal episodes from proprietary database with an accuracy of 92.51%.

Conclusion

When the performance results were compared with the literature, it was observed that the proposed framework performed well. As a result, the proposed framework could be used in an autonomous diagnosis system.

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Acknowledgments

The authors would acknowledge the Department of Bio-Technology (DBT) funding for carrying out this research work (BT/PR14751/MED/32/422/2015).

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Authors and Affiliations

  1. Department of Electrical, Electronics and Communication Engineering, GITAM, Bangalore Campus, Bengaluru, India

    Avvaru Srinivasulu

  2. Center for Medical Electronics and Computing, M.S. Ramaiah Institute of Technology, Bengaluru, India

    N. Sriraam

  3. Department of Cardiology, M.S. Ramaiah Medical College and Hospitals, Bengaluru, India

    V. S. Prakash

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  1. Avvaru Srinivasulu
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  2. N. Sriraam
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  3. V. S. Prakash
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Correspondence to N. Sriraam.

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Srinivasulu, A., Sriraam, N. & Prakash, V.S. A Signal Processing Framework for the Detection of Abnormal Cardiac Episodes. Cardiovasc Eng Tech 14, 331–349 (2023). https://doi.org/10.1007/s13239-023-00656-4

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