GOAL: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds. METHODS: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. RESULTS: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. CONCLUSION: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. SIGNIFICANCE: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.
GOAL: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subject's preferred pace) and moderately fast (1.34-1.45 m/s) speeds. METHODS: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. RESULTS: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. CONCLUSION: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. SIGNIFICANCE: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement-aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular system's response to stress (exercise), and thus provide a more holistic assessment of overall health.
Authors: Dariush Mozaffarian; Emelia J Benjamin; Alan S Go; Donna K Arnett; Michael J Blaha; Mary Cushman; Sandeep R Das; Sarah de Ferranti; Jean-Pierre Després; Heather J Fullerton; Virginia J Howard; Mark D Huffman; Carmen R Isasi; Monik C Jiménez; Suzanne E Judd; Brett M Kissela; Judith H Lichtman; Lynda D Lisabeth; Simin Liu; Rachel H Mackey; David J Magid; Darren K McGuire; Emile R Mohler; Claudia S Moy; Paul Muntner; Michael E Mussolino; Khurram Nasir; Robert W Neumar; Graham Nichol; Latha Palaniappan; Dilip K Pandey; Mathew J Reeves; Carlos J Rodriguez; Wayne Rosamond; Paul D Sorlie; Joel Stein; Amytis Towfighi; Tanya N Turan; Salim S Virani; Daniel Woo; Robert W Yeh; Melanie B Turner Journal: Circulation Date: 2016-01-26 Impact factor: 29.690
Authors: Jonathan Zia; Jacob Kimball; Sinan Hersek; Md Mobashir Hasan Shandhi; Beren Semiz; Omer T Inan Journal: IEEE J Biomed Health Inform Date: 2019-07-26 Impact factor: 5.772
Authors: Md Mobashir Hasan Shandhi; William H Bartlett; James Alex Heller; Mozziyar Etemadi; Aaron Young; Thomas Plotz; Omer T Inan Journal: IEEE J Biomed Health Inform Date: 2021-03-05 Impact factor: 5.772