| Summary: | Background and objectives: Cardiovascular health is monitored by many different approaches in order to diagnose and prevent critical patient conditions. The wide used of Photoplethysmography in medicine can facilitate heart status monitoring by heart rate variability (HRV) calculations extracted from blood volume variation measurements. The purpose of this work is to develop a computational process which relies on the evaluation of HRV as a prognostic marker in the stratification of the clinical heart conditions: Idiopathic Dilated Cardiomyopathy (IDC), Chagas Cardiomyopathy (CC) and Ischemic Cardiomyopathy (IC). Methods: The database created for the study was developed with the assistance of Dr. Carlos Alberto Studart Gomes Hospital in the city of Fortaleza, CE. The detection of cardiac anomaly is analyzed by computational intelligence and pattern recognition algorithms. The classifiers used are the Multilayer Perceptron (MLP), Self-Organizing Map (SOM), Kernel kmeans (Kk-means), and k-Nearest Neighbors (k-NN). Results: The results indicate SOM and Kk-means classifiers are the most suitable algorithms to be applied because they hold the highest true rates (Acc = 85.44%–100%) and sensitivity (Se = 92.85%–100%). Conclusions: Among the heart conditions considered in this paper, Chagas Cardiomyopathy analysis presented the best results for any algorithm because the differences in the patterns of this disease are easier to identify due to arrhythmogenic characteristics. IDC and IC have similar statistical variables versus healthy subjects, which may complicate the pattern recognition performed by classifiers. The combination of extracted PPG signal features with machine learning may result in a high stratification performance and be of assistance to doctors for early diagnosis of disease.
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