Abstract Body: Background: Early detection of carotid artery (CA) conditions such as atherosclerosis and dissection are important as they represent major preventable causes of stroke. While medical imaging equipment can track calcification and dissection, they are bulky, time-consuming and unsuitable for remote or continuous monitoring. In this study, we sought to analyze if a wearable, microchip seismometer patch can capture significant differences in the systolic and diastolic blood acoustic vibrational signature from the carotid arteries across various conditions.
Methods: The multi-head StethX seismometer patches were placed on 63 patients seen at the Emory Stroke Clinic via medical tape near the left and right CA and the pulmonic region of the heart to capture the CA blood acoustic and heart sound vibrations. These patients had varying CA conditions (atherosclerosis, dissection, normal, etc.) confirmed by magnetic resonance angiography (MRA), computer tomography angiography (CTA) or carotid ultrasound within the past year and had their blood acoustic vibration signatures recorded for one minute. The signals were then converted into time-frequency representations (TFR) with color scale used as magnitude of signal using continuous wavelet transform to distinguish between healthy and unhealthy carotid arteries.
Results: The TFR of the blood acoustic vibrations captured by the seismometer showed significant differences of as a function of varying CA conditions. For CA with less than 50% stenosis, two distinct peaks representing systolic and diastolic are present, with systolic being lower frequency than diastolic, as shown in the Figure. For CA stenosis of greater than 50%, specifically 70-99%, the diastolic peaks are in a lower frequency range compared to less than 50% stenosis. For CA dissection, the systolic and diastolic peaks both show a higher frequency range compared to patients with less than 50% stenosis.
Conclusions: StethX’s wearable microchip seismometer patch captured distinct acoustic vibration patterns as shown via TFR in patients with varying CA conditions, including moderate to severe atherosclerotic stenosis and dissection, when compared to less than 50% CA stenosis. This enables the application of machine learning models that use the CA blood acoustics along with possibly pulse transit time using a third patch located on the heart pulmonic region as inputs to classify severity of carotid artery stenosis as a digital health screening solution.