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Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things

  • Mobile & Wireless Health
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Abstract

Electrocardiography (ECG) sensors play a vital role in the Internet of Medical Things, and these sensors help in monitoring the electrical activity of the heart. ECG signal analysis can improve human life in many ways, from diagnosing diseases among cardiac patients to managing the lifestyles of diabetic patients. Abnormalities in heart activities lead to different cardiac diseases and arrhythmia. However, some cardiac diseases, such as myocardial infarction (MI) and atrial fibrillation (Af), require special attention due to their direct impact on human life. The classification of flattened T wave cases of MI in ECG signals and how much of these cases are similar to ST-T changes in MI remain an open issue for researchers. This article presents a novel contribution to classify MI and Af. To this end, we propose a new approach called deep deterministic learning (DDL), which works by combining predefined heart activities with fused datasets. In this research, we used two datasets. The first dataset, Massachusetts Institute of Technology–Beth Israel Hospital, is publicly available, and we exclusively obtained the second dataset from the University of Malaya Medical Center, Kuala Lumpur Malaysia. We first initiated predefined activities on each individual dataset to recognize patterns between the ST-T change and flattened T wave cases and then used the data fusion approach to merge both datasets in a manner that delivers the most accurate pattern recognition results. The proposed DDL approach is a systematic stage-wise methodology that relies on accurate detection of R peaks in ECG signals, time domain features of ECG signals, and fine tune-up of artificial neural networks. The empirical evaluation shows high accuracy (i.e., ≤99.97%) in pattern matching ST-T changes and flattened T waves using the proposed DDL approach. The proposed pattern recognition approach is a significant contribution to the diagnosis of special cases of MI.

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Funding

Muhammad Imran and Muhammad Shoaib are supported by the Deanship of Scientific Research, King Saud University through Research Group No. RG-1435-051.

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Authors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Uzair Iqbal, Teh Ying Wah, Muhammad Habib ur Rehman & Ghulam Mujtaba

  2. Department of Computer Science, National University of Computer & Emerging Sciences, Lahore, Pakistan

    Muhammad Habib ur Rehman

  3. Department of Computer Science, Sukkur IBA University, Sukkur, 65200, Pakistan

    Ghulam Mujtaba

  4. College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia

    Muhammad Imran & Muhammad Shoaib

Authors
  1. Uzair Iqbal
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  2. Teh Ying Wah
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  3. Muhammad Habib ur Rehman
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  4. Ghulam Mujtaba
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  5. Muhammad Imran
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  6. Muhammad Shoaib
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Correspondence to Teh Ying Wah.

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The authors declare no conflict of interest in the publication of this paper.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

This article is part of the Topical Collection on Mobile & Wireless Health

Appendix 1

Appendix 1

Table 16 Critical analysis of all possible feature combinations
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Iqbal, U., Wah, T.Y., Habib ur Rehman, M. et al. Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things. J Med Syst 42, 252 (2018). https://doi.org/10.1007/s10916-018-1107-2

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  • DOI: https://doi.org/10.1007/s10916-018-1107-2

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