Distilling clinically interpretable information from data collected on next-generation wearable sensors
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 nee...
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| Format: | Article |
| Language: | en_US |
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2014
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| Online Access: | http://hdl.handle.net/1721.1/86388 https://orcid.org/0000-0003-4357-6854 https://orcid.org/0000-0002-5930-7694 https://orcid.org/0000-0002-9823-8652 https://orcid.org/0000-0002-2446-1499 |
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| author | Haslam, Bryan Todd Gordhandas, Ankit Verghese, George C. Heldt, Thomas Ricciardi, Catherine E. |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Haslam, Bryan Todd Gordhandas, Ankit Verghese, George C. Heldt, Thomas Ricciardi, Catherine E. |
| author_sort | Haslam, Bryan Todd |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:48:37Z |
| format | Article |
| id | mit-1721.1/86388 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:48:37Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | mit-1721.1/863882022-09-30T16:58:47Z Distilling clinically interpretable information from data collected on next-generation wearable sensors Haslam, Bryan Todd Gordhandas, Ankit Verghese, George C. Heldt, Thomas Ricciardi, Catherine E. Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Clinical Research Center Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics Haslam, Bryan Todd Gordhandas, Ankit Ricciardi, Catherine Verghese, George C. Heldt, Thomas 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. Texas Instruments Incorporated United States. Dept. of Defense (National Defense Science and Engineering Graduate Fellowship) 2014-05-02T19:06:06Z 2014-05-02T19:06:06Z 2011-08 Article http://purl.org/eprint/type/ConferencePaper 978-1-4577-1589-1 978-1-4244-4121-1 978-1-4244-4122-8 1557-170X INSPEC Accession Number: 12424957 http://hdl.handle.net/1721.1/86388 Haslam, B., A. Gordhandas, C. Ricciardi, G. Verghese, and T. Heldt. “Distilling Clinically Interpretable Information from Data Collected on Next-Generation Wearable Sensors.” 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug. 30 2011-Sept. 3 2011, Boston, MA. pp.1729-1732. 22254660 https://orcid.org/0000-0003-4357-6854 https://orcid.org/0000-0002-5930-7694 https://orcid.org/0000-0002-9823-8652 https://orcid.org/0000-0002-2446-1499 en_US http://dx.doi.org/10.1109/IEMBS.2011.6090495 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf PMC |
| spellingShingle | Haslam, Bryan Todd Gordhandas, Ankit Verghese, George C. Heldt, Thomas Ricciardi, Catherine E. Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title | Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title_full | Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title_fullStr | Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title_full_unstemmed | Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title_short | Distilling clinically interpretable information from data collected on next-generation wearable sensors |
| title_sort | distilling clinically interpretable information from data collected on next generation wearable sensors |
| url | http://hdl.handle.net/1721.1/86388 https://orcid.org/0000-0003-4357-6854 https://orcid.org/0000-0002-5930-7694 https://orcid.org/0000-0002-9823-8652 https://orcid.org/0000-0002-2446-1499 |
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