- Title
- Deep deterministic learning for pattern recognition of different cardiac diseases through the internet of medical things
- Creator
- Iqbal, Uzair; Wah, Teh; Habib ur Rehman, Muhammad; Mujtaba, Ghulam; Imran, Muhammad; Shoaib, Muhammad
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/181856
- Identifier
- vital:16009
- Identifier
-
https://doi.org/10.1007/s10916-018-1107-2
- Identifier
- ISBN:0148-5598 (ISSN)
- 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. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
- Publisher
- Springer New York LLC
- Relation
- Journal of Medical Systems Vol. 42, no. 12 (2018), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Springer Science+Business Media, LLC, part of Springer Nature 2018
- Subject
- 4203 Health Services and Systems; Artificial neural network; Cardiovascular diseases; Deep deterministic learning; Electrocardiography; Internet of medical things; Pattern recognition
- Reviewed
- Funder
- 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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