Photoplethysmograph based arrhythmia detection using morphological features

Photoplethysmography (PPG) is a non-invasive optical technique that is used for the detection of cardiovascular diseases. The paroxysmal nature of arrhythmic events and the lack of timely recorded data emphasize the need to develop an automated method for the identification of arrhythmias. The literature shows the detection of a single type of arrhythmia using PPG. However, limited research has been carried out for the detection of multiple types of arrhythmia. In this research work, a new set of morphological features have been proposed for the automated detection of multiple arrhythmias using rule-based and statistical learning-based approaches. The proposed work has been implemented on the retrospective dataset and validated on the prospective dataset. The results show that the rule-based arrhythmia detection method is equipollent to the statistical learning approach with an accuracy of 98.43%/94.16% on the retrospective dataset and 94.16%/93% on the prospective dataset.

Automated summary

Photoplethysmography (PPG) is a non-invasive optical technique used for detecting cardiovascular diseases. Researchers propose a new set of morphological features for automated detection of multiple arrhythmias using rule-based and statistical learning-based approaches. The proposed methods are implemented and validated on retrospective and prospective datasets, and show comparable accuracy rates of 98.43%/94.16% (retrospective) and 94.16%/93% (prospective) for rule-based and statistical learning approaches, respectively.