Key result
An unsupervised learning model using agglomerative clustering on impedance plethysmography data achieved a DB index of 0.13 and a Dunn index of 0.87 for perceiving cardiac cycle characteristics.
Why the study?
Applying unsupervised learning to analyze hidden patterns in medical data and derive valuable cardiac hemodynamic parameters remains challenging.
Does an unsupervised learning model applied to impedance plethysmography accurately perceive cardiac cycle characteristics?
Does an unsupervised learning model applied to impedance plethysmography accurately perceive cardiac cycle characteristics?
An unsupervised learning model using agglomerative clustering on impedance plethysmography data shows potential for deriving cardiac hemodynamic parameters non-invasively.
Nowadays, unsupervised learning presents a new approach to analyze various hidden patterns inside of medical data. Still, it is a great challenge to apply unsupervised learning and produce valuable data, especially to the cardiac system. This paper has proposed an advanced model for the derivation of cardiac hemodynamic parameters from the first derivative impedance signal. This study aims to analyze the plethysmographic wave by non-invasive measurement of electrical impedance of limb. The proposed model is based on unsupervised learning of morphological features of impedance plethysmography (IPG). We conducted and compared the performance evaluation of three clustering techniques on recorded impedance data to perceive the cardiac cycle characteristics. The findings can potentially assist in determining several vital health care variables like blood pressure, arterial stiffness and respiration rate. The proposed model was tested on a recorded IPG dataset and it achieved DB index and Dunn index of 0.13 and 0.87 in agglomerative clustering for an optimal number of clusters.
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Agham et al. (2021) studied Cardiac events. Unsupervised learning model of impedance plethysmography (IPG) vs. Other clustering techniques was evaluated on DB index and Dunn index. An unsupervised learning model using agglomerative clustering on impedance plethysmography data achieved a DB index of 0.13 and a Dunn index of 0.87 for perceiving cardiac cycle characteristics.
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