An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals

• Published in: IEEE transactions on biomedical engineering Vol. 66; no. 3; pp. 784 - 793
• Main Authors: Yang, Chenxi, Tavassolian, Negar
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Accelerometry - instrumentation; Adult; Algorithms; Biomedical monitoring; Correlation coefficient; Correlation coefficients; Data processing; Digital signal processing; Ejection; EKG; Electrocardiography; Electronic devices; Feasibility studies; Feature extraction; Female; Gyro-cardiography (GCG); Gyroscopes; Heart - physiology; Heart Function Tests - instrumentation; Heart Function Tests - methods; Humans; Independent component analysis; independent component analysis (ICA); Inertial platforms; Legged locomotion; Male; Mechanical stimuli; Monitoring; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; motion cancellation; Principal Component Analysis; Reference materials; seismo-cardiography (SCG); Sensor arrays; Sensors; Signal processing; Signal Processing, Computer-Assisted; sternal cardio-mechanical sensing; Walking - physiology; wearable cardiovascular monitoring
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2018.2856700

This paper proposes a new framework for measuring sternal cardio-mechanical signals from moving subjects using multiple sensors. An array of inertial measurement units are attached to the chest wall of subjects to measure the seismocardiogram (SCG) from accelerometers and the gyrocardiogram (GCG) from gyroscopes. A digital signal processing method based on constrained independent component analysis is applied to extract the desired cardio-mechanical signals from the mixture of vibration observations. Electrocardiogram and photoplethysmography modalities are evaluated as reference sources for the constrained independent component analysis algorithm. Experimental studies with 14 young, healthy adult subjects demonstrate the feasibility of extracting seismo- and gyrocardiogram signals from walking and jogging subjects, with speeds of 3.0 mi/h and 4.6 mi/h, respectively. Beat-to-beat and ensemble-averaged features are extracted from the outputs of the algorithm. The beat-to-beat cardiac interval results demonstrate average detection rates of 91.44% during walking and 86.06% during jogging from SCG, and 87.32% during walking and 76.30% during jogging from GCG. The ensemble-averaged pre-ejection period (PEP) calculation results attained overall squared correlation coefficients of 0.9048 from SCG and 0.8350 from GCG with reference PEP from impedance cardiogram. Our results indicate that the proposed framework can improve the motion tolerance of cardio-mechanical signals in moving subjects. The effective number of recordings during day time could be potentially increased by the proposed framework, which will push forward the implementation of cardio-mechanical monitoring devices in mobile healthcare.