Key result
Logistic Regression, Support Vector Machine, and Naïve Bayes models achieved average annotation accuracies of 0.935, 0.925, and 0.86, respectively, for automatic annotation of SCG signals.
Why the study?
Inter-subject variability of seismocardiogram (SCG) signals makes accurate automatic annotation difficult, despite its potential to continuously estimate cardiac health parameters.
Machine learning classifiers, particularly Logistic Regression and Support Vector Machine, can accurately and rapidly annotate Seismocardiogram signals, facilitating potential real-time cardiac monitoring.
ML classifiers may enable rapid seismocardiogram annotation; leaves open prospective clinical validation before adoption.
The automatic annotation of Seismocardiogram (SCG) potentially aid to estimate various cardiac health parameters continuously. However, the inter-subject variability of SCG poses great difficulties to automate its accurate annotation. The objective of the research is to design SCG peak retrieval methods on the top of the ensemble features extracted from the SCG morphology for the automatic annotation of SCG signals. The annotation scheme is formulated as a binary classification problem. Three binary classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) are employed for the annotation and the results are compared with the recent state-of-the-art schemes. The performance evaluation is carried out using 9000 SCG signals of 20 presumably healthy volunteers with no known serious cardiac abnormalities. The SCG signals are acquired from the Physionet public repository “cebsdb”. The models are rigorously validated using metrics “Precision”, “Recall”, and “F-measure” followed by 5-fold cross-validation. The experimental validation with recent state-of-the-art solutions establishes the robustness of the proposed NB, SVM and LR with average annotation accuracy of 0.86, 0.925 and 0.935, respectively. The mean response time of proposed models is in the fraction of 1/10 sec, which establishes its application for the real-time annotation.
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Thakkar et al. (2019) studied Healthy volunteers (n=20). Machine learning models (Naïve Bayes, Support Vector Machine, Logistic Regression) vs. Recent state-of-the-art schemes was evaluated on Average annotation accuracy. Logistic Regression, Support Vector Machine, and Naïve Bayes models achieved average annotation accuracies of 0.935, 0.925, and 0.86, respectively, for automatic annotation of SCG signals.
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