Cardiac Rhythm Identification Based on Chest Wall Motion, 10-9171Printer Friendly Version
Inclusive Dates: 11/22/1999 - 03/22/2000
Background - The human heart has distinct tissues that function as an electrical conduction system. This system coordinates the contraction of the cardiac muscle to ensure an organized and efficient heart beat. The normal beating heart has an inherent rhythm and thus beats in a coordinated fashion. Life-threatening cardiac arrhythmias, namely ventricular tachycardia and ventricular fibrillation, both of which can lead to heart attacks and sudden cardiac death, do not use the specialized conduction system. As a result, the heart beat is not coordinated. In fact, the motion of a heart exhibiting ventricular fibrillation, a lethal cardiac rhythm, has been described as resembling a bowl of Jell-O.
Approach - The hypotheses investigated in this project were: 1) because the beating heart produces vibrations in the chest wall, the vibrations can be measured using an anisotropic magnetoresistive (AMR) sensor and 2) the measurements can be used to identify cardiac rhythms. The specific aims accomplished for this project were to develop a sensor device that uses commercially available AMR sensors to detect chest wall vibrations that result from the beating heart and to demonstrate that a correlation exists between chest wall vibrations and the electrocardiogram.
Accomplishments - A motion sensor device using AMR sensors was fabricated based on a previous SwRI design. The device measured the motion of the chest wall while the electrocardiogram (ECG) recorded. Under an Institutional Review Board-approved protocol, twelve data sets were collected from seven subjects. The sensor was placed on the chest wall at various locations and on the neck. The data were acquired using off-the-shelf, analog-to-digital converters and custom-written data-acquisition software created using a graphical programming language. Custom-written analysis software performed signal filtering and peak detection, and a commercial spreadsheet software application calculated the correlation coefficient. For all data sets, the correlation coefficients between the peak of the QRS complex, i.e. a value that represents the contraction of the ventricles seen in the ECG and the peak of the signals obtained from the motion sensor device, were greater than 0.995. Aspects of the data indicate that the sensor is detecting motion that results more from blood flow (i.e., blood pressure) than from mechanical motion induced by the beating heart. These data were shown to a number of potential clients and resulted in an externally funded project.