Key result
A multimodal decision support system using ECG, PCG, and clinical features classified patients with hard chest pain into high and low MI risk groups with 98.0% sensitivity, 100% specificity, and 99.0% accuracy.
Why the study?
Does a multimodal system combining ECG, PCG, and clinical features improve the prediction of MI risk in patients with hard chest pain?
Observational (n=83)
Does a multimodal system combining ECG, PCG, and clinical features improve the prediction of MI risk in patients with hard chest pain?
A multimodal decision support system combining ECG, PCG, and clinical features demonstrated high accuracy in stratifying MI risk among patients with severe chest pain.
Myocardial infarction (MI) also known as heart attack is one of the prevalence cardiovascular diseases. MI that is due to the blockade in the coronary artery is caused by the lack of blood supply (ischemia) to heart tissue. Determining the risk of MI and hospitalizing the victim immediately can prolong patient’s life and enhance the quality of living through appropriate treatment. To make this decision more accurate, in this study, a decision support system is proposed to classify patients with hard chest pain (sign of MI) into high and low risk groups. Such a system can also assist in managing the limitation of bed in the care units such as cardiac care unit by deciding on admitting a subject with a hard chest pain whom refers to a hospital or not. Despite several efforts in this issue, the so far published results demonstrated that distinguishing these patients using just electrocardiogram (ECG) features is not promising. In addition, these methods did not focus on classifying the patients with high and low risks of MI. In this regard, auxiliary features from phonocardiogram (PCG) signals and clinical data were elicited to create a discriminative feature set and ultimately improve the performance of the decision making system. In this research, ECG (from 12 leads), PCG signal and clinical data were acquired from 83 patients two times (morning and evening) in the first day. Since the number of elicited features from the raw data of each patient is high, the irrelevant and non-discriminative features were eliminated by sequential forward selection. The selected features were applied to [Formula: see text]-nearest neighbor classifier resulted in 98.0% sensitivity, 100% specificity and 99.0% accuracy over the patients. The results illustrate that neither clinical data nor ECG features nor PCG features are lonely enough for estimating the risk of MI. Employing features from different modalities can improve the performance such that the developed multimodal-based system overperformed single modal-based systems. The obtained results are promising and suggest that using this system might be useful as a means for altering the risk of MI in patients.
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Zarrabi et al. (2017) conducted an observational in Hard chest pain (risk of myocardial infarction) (n=83). Multimodal decision support system (ECG, PCG, and clinical features) vs. Single modal-based systems was evaluated on Classification of patients into high and low risk groups for MI. A multimodal decision support system using ECG, PCG, and clinical features classified patients with hard chest pain into high and low MI risk groups with 98.0% sensitivity, 100% specificity, and 99.0% accuracy.
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