An automated system using Random Forests classification algorithms successfully identified and classified systolic and diastolic murmurs in 198 heart sound signals, outperforming alternative classifiers.
Does a Random Forests classification algorithm improve the automatic identification and classification of heart murmurs compared to alternative classifiers in heart sound signals?
A Random Forests-based system can effectively automate the identification and classification of heart murmurs from sound signals, potentially aiding remote monitoring in assistive environments.
The aging population in many countries, in combination with high government deficits and financial resources limitations, necessitates new methods for the home care of the elderly at reasonable costs based on the exploitation of modern information and communication technologies (ICT). This requires the installation of assistive environments at the homes of elderly people, which include various types of sensors, generating biosignals of other types of signals, which are transferred through networks to local health centers or hospitals in order to be monitored. However, scaling up the application of such ICT-based methods of elderly home care is going to increase tremendously the workload of the medical staff of local health centers or hospitals. Therefore it is of critical importance to develop capabilities for an automated first screening of these signals and identification of abnormal elements and diseases. In this direction the present paper proposes a system for the automatic identification of murmurs in heart sound signals, and also for the classification of them as systolic or diastolic, using a new generation of advanced Random Forests classification algorithms, which are aggregating the prediction of multiple classifiers (ensemble classification). The proposed system has been applied and validated in a representative global dataset of 198 heart sound signals, which come both from healthy medical cases and from cases having systolic and diastolic murmurs. Also, some alternative classifiers have been applied to the same data for comparison purposes. It has been concluded that the proposed systems shows a good performance, which is higher than the examined alternative classifiers.
Loukis et al. (Wed,) conducted a other in Heart murmurs (n=198). Random Forests classification algorithms vs. Alternative classifiers was evaluated on Automatic identification of murmurs and classification as systolic or diastolic. An automated system using Random Forests classification algorithms successfully identified and classified systolic and diastolic murmurs in 198 heart sound signals, outperforming alternative classifiers.
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