A signal processing framework for multimodal cardiac analysis

Abstract

The heart is a complex organ in the cardiovascular system which its measurement and analysis system in clinical level should be realized in an integrated system including all cardiac vital signs. A previous study combined ECG and PCG analysis could detect murmur symptom. However, the heart mechanical activity could not be described. This study developed a multimodal analysis of cardiac signals consisting of ECG signals, carotid pulse, and PCG. The purpose of this study was to develop and test an appropriate signal processing framework to facilitate parameter extraction and to enhance understanding of underlying mechanisms in the cardiac physiology. Frequency and time-frequency domain analysis of cardiac signals were performed to design sophisticated digital filters. Recursive digital filters were chosen in realizing segmentation methods and the advanced signal processing techniques were performed in parameter extraction. Results show the proposed method was able to detect QRS complex, P and T waves in ECG signal with 88% sensitivity and also percussion wave with 85.62% sensitivity. Sistolic (S1) and diastolic (S2) heart sound also could be separated. Classification of normal and the disease type of heart based on the cardiac parameters resulted by the presented signal processing framework would be next research topic.