A Naïve Bayes classifier using photoplethysmography signals achieved a test accuracy of 94.44% for differentiating healthy from unhealthy subjects and 89.37% for determining the disease type.
Observational (n=360)
Does photoplethysmography (PPG) signal analysis accurately identify and classify cardiovascular diseases in healthy subjects and patients with cardiovascular disease?
A low-cost PPG signal classifier using time-domain features can accurately identify and classify cardiovascular diseases.
A low-cost, fast, dependable, repeatable, non-invasive, portable, and simple-to-use vascular screening tool for coronary artery diseases (CADs) is preferred. Photoplethysmography (PPG), a low-cost optical pulse wave technology, is one method with this potential. PPG signals come from changes in the amount of blood in the microvascular bed of tissue. Therefore, these signals can be used to figure out anomalies within the cardiovascular system. This work shows how to use PPG signals and feature selection-based classifiers to identify cardiorespiratory disorders based on the extraction of time-domain features. Data were collected from 360 healthy and cardiovascular disease patients. For analysis and identification, five types of cardiovascular disorders were considered. The categories of cardiovascular diseases were identified using a two-stage classification process. The first stage was utilized to differentiate between healthy and unhealthy subjects. Subjects who were found to be abnormal were then entered into the second stage classifier, which was used to determine the type of the disease. Seven different classifiers were employed to classify the dataset. Based on the subset of features found by the classifier, the Naïve Bayes classifier obtained the best test accuracy, with 94.44% for the first stage and 89.37% for the second stage. The results of this study show how vital the PPG signal is. Many time-domain parts of the PPG signal can be easily extracted and analyzed to find out if there are problems with the heart. The results were accurate and precise enough that they did not need to be looked at or analyzed further. The PPG classifier built on a simple microcontroller will work better than more expensive ones and will not make the patient nervous.
Fahoum et al. (Mon,) conducted a observational in Coronary artery diseases (n=360). Photoplethysmography (PPG) signals and feature selection-based classifiers was evaluated on Test accuracy of the Naïve Bayes classifier. A Naïve Bayes classifier using photoplethysmography signals achieved a test accuracy of 94.44% for differentiating healthy from unhealthy subjects and 89.37% for determining the disease type.