Motion Artifacts Reduction for Noninvasive Hemodynamic Monitoring of Conscious Patients Using Electrical Impedance Tomography: A Preliminary Study.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs occurred. A total of 36 measurements with 113 one-hour sub-datasets were collected from 14 patients. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 L per minute (LPM), respectively, compared to 4.05 and 3.82 LPM for the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability by at least two times, particularly in high-motion environments.

Tidal volume and stroke volume changes caused by respiratory events during sleep and their relationship with OSA severity: a pilot study.

PURPOSE: Breath-by-breath tidal volume (TV) and beat-by-beat stroke volume (SV) were continuously measured in patients with OSA undergoing polysomnography (PSG). The objectives were to (1) determine the changes in TV/SV in response to respiratory events and (2) assess the relationship between these changes and the disease severity. METHODS: From the PSG data of nine patients with OSA, six different types of respiratory events were identified, i.e., flow limitation (FL), respiratory effort related arousal (RERA), hypopnea with arousal only (Ha), hypopnea with desaturation only (Hd), hypopnea with arousal and desaturation (Had), and apnea. The measured TV and SV values during and after each respiratory event were compared with the pre-event baseline values. RESULTS: The mean TV/SV reductions during all hypopneas and apneas were 38.1%/4.2% and 70.5%/8.8%, respectively. Among three different hypopnea types, the reductions in TV during Hd and Had were significantly greater than those during Ha. The TV reductions during Ha and FL were similar. After RERA, Ha, Had, and apnea, there was an overshoot in TV and SV values, whereas there was no overshoot after FL and Hd. During RERA, there was no reduction in TV/SV. CONCLUSIONS: The changes in TV during and after each type of respiratory event were significantly different in most cases. The changes in SV between hypopnea and apnea were different with statistical significance. The AHI does not properly account for the ventilation losses caused by respiratory events. Thus, TV measurements might be useful in the future in assessing the OSA severity in conjunction with the AHI.

Feature Extraction of Upper Airway Dynamics during Sleep Apnea using Electrical Impedance Tomography.

Characterizing upper airway occlusion during natural sleep could be instrumental for studying the dynamics of sleep apnea and designing an individualized treatment plan. In recent years, obstructive sleep apnea (OSA) phenotyping has gained attention to classify OSA patients into relevant therapeutic categories. Electrical impedance tomography (EIT) has been lately suggested as a technique for noninvasive continuous monitoring of the upper airway during natural sleep. In this paper, we developed the automatic data processing and feature extract methods to handle acquired EIT data for several hours. Removing ventilation and blood flow artifacts, EIT images were reconstructed to visualize how the upper airway collapsed and reopened during the respiratory event. From the time series of reconstructed EIT images, we extracted the upper airway closure signal providing quantitative information about how much the upper airway was closed during collapse and reopening. Features of the upper airway dynamics were defined from the extracted upper airway closure signal and statistical analyses of ten OSA patients' data were conducted. The results showed the feasibility of the new method to describe the upper airway dynamics during sleep apnea, which could be a new step towards OSA phenotyping and treatment planning.