RESUMO
Objective:In the future, thoracic Electrical Impedance Tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as a ventilation-related and cardiac-related signal is missing. Approach:This study analyses the potential of Multi-dimensional Ensemble Empirical Mode Decomposition (MEEMD) by application of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and a novel frequency-based combination criterion for de-trending, denoising and source separation of EIT image streams, collected from 9 healthy male test subjects with similar age and constitution. Main results:In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on the Root Mean Square Error (RMSE) between the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal. Significance:Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional de-trending and noise-removal. For all analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related and the PPG signal. .
RESUMO
The detection of muscle contraction and the estimation of muscle force are essential tasks in robot-assisted rehabilitation systems. The most commonly used method to investigate muscle contraction is surface electromyography (EMG), which, however, shows considerable disadvantages in predicting the muscle force, since unpredictable factors may influence the detected force but not necessarily the EMG data. Electrical impedance myography (EIM) investigates the change in electrical impedance during muscle activities and is another promising technique to investigate muscle functions. This paper introduces the design, development, and evaluation of a device that performs EMG and EIM simultaneously for more robust measurement of muscle conditions subject to artifacts. The device is light, wearable, and wireless and has a modular design, in which the EMG, EIM, micro-controller, and communication modules are stacked and interconnected through connectors. As a result, the EIM module measures the bioimpedance between 20 and 200 Ω with an error of less than 5% at 140 SPS. The settling time during the calibration phase of this module is less than 1000 ms. The EMG module captures the spectrum of the EMG signal between 20-150 Hz at 1 kSPS with an SNR of 67 dB. The micro-controller and communication module builds an ARM-Cortex M3 micro-controller which reads and transfers the captured data every 1 ms over RF (868 Mhz) with a baud rate of 500 kbps to a receptor connected to a PC. Preliminary measurements on a volunteer during leg extension, walking, and sit-to-stand showed the potential of the system to investigate muscle function by combining simultaneous EMG and EIM.
