HeartForce research is focused on the early detection and prevention of heart disease, a predominant subset of cardiovascular disease (CVD). The term CVD covers a group of cardiac disorders and blood vessel diseases, such as peripheral and cerebrovascular diseases (i.e., strokes) that are not directly related to the heart.
We specifically target our research on the early detection of coronary artery disease (CAD), the most common form of heart disease which kills over 7 million people per year globally, followed by arrhythmia, heart valve disease and heart failure.
We have conducted multiple clinical studies with the goal of predicting CAD, arrhythmias, and valvular dysfunctions using SCG signals in combination with an ECG.
- Our research has shown that the vibration characteristics of the heart change significantly due to CAD – both during the relaxation and contraction of the cardiac cycle. HeartForce’s intelligent algorithms are capable of recognizing the vibration characteristics distinguishing between patients with coronary artery disease and healthy patients. In order to verify the overall performance of the prediction accuracy, invasive coronary angiography was used to assess patients with CAD and SCG based results were compared with angiography.
- A combination of ECG and SCG signals have been used for the detection of arrhythmia and in particular, atrial fibrillation (AFib). ECG is used to detect changes in heart rate patterns, whereas SCG is used to detect vibration abnormalities during different phases of cardiac cycles associated with different types of arrhythmias.
- SCG is also used to extract important cardiac timing intervals associated with the opening and closing of heart valves to pinpoint heart valve dysfunctions. To verify these timing intervals, a comprehensive study was conducted to compare timing intervals, namely, the pre-ejection period and left ventricular ejection time with the current gold standard, echocardiography (medical imaging). Based on extensive published research, we have verified that timing patterns can be extracted from a SCG signal.