Distilling clinically interpretable information from data collected on next-generation wearable sensors

Abstract

Medical electronic systems are generating ever larger data sets from a variety of sensors and devices. Such systems are also being packaged in wearable designs for easy and broad use. The large volume of data and the constraints of low-power, extended-duration, and wireless monitoring impose the need for on-chip processing to distill clinically relevant information from the raw data. The higher-level information, rather than the raw data, is what needs to be transmitted. We present one example of information processing for continuous, high-sampling-rate data collected from wearable and portable devices. A wearable cardiac and motion monitor designed by colleagues at MIT simultaneously records electrocardiogram (ECG) and 3-axis acceleration to onboard memory, in an ambulatory setting. The acceleration data is used to generate a continuous estimate of physical activity. Additionally, we use a Portapres continuous blood pressure monitor to concurrently record the arterial blood pressure (ABP) waveform. To help reduce noise, which is an increased challenge in ambulatory monitoring, we use both the ECG and ABP waveforms to generate a robust measure of heart rate from noisy data. We also generate an overall signal abnormality index to aid in the interpretation of the results. Two important cardiovascular quantities, namely cardiac output (CO) and total peripheral resistance (TPR), are then derived from this data over a sequence of physical activities. CO and TPR can be estimated (to within a scale factor) from heart rate, pulse pressure and mean arterial blood pressure, which in turn are directly obtained from the ECG and ABP signals. Data was collected on 10 healthy subjects. The derived quantities vary in a manner that is consistent with known physiology. Further work remains to correlate these values with the cardiac health state.