Prospective Evaluation of a Multibeat Analysis Cardiac Index Estimation in Patients With Cardiogenic Shock.

OBJECTIVES: The decision algorithm for managing patients in cardiogenic shock depends on cardiac index (CI) estimates. Cardiac index estimation via thermodilution (CI-TD) using a pulmonary artery catheter is used commonly for obtaining CI in these patients. Minimally invasive methods of estimating CI, such as multibeat analysis (CI-MBA), may be an alternative in this population. DESIGN: A prospective, observational study. SETTING: Cardiac intensive care unit. PARTICIPANTS: Twenty-two subjects in cardiogenic shock provided 101 paired CI measurements. INTERVENTIONS: Measurements were obtained concomitantly by intermittent CI-TD and CI-MBA (Argos Cardiac Output Monitor; Retia Medical, Valhalla, NY). For each CI-TD, CI-MBA estimates were averaged over 1 minute to provide paired values. Bland-Altman and 4-quadrant analyses were performed by plotting changes between successive CI measurements (ΔCI) from each of the 2 methods. Concordance was calculated as a percentage using ΔCI data points from the 2 methods, outside an exclusion zone of 15%. MEASUREMENTS AND MAIN RESULTS: The correlation coefficient between CI-MBA and CI-TD was 0.78 across patients. Mean CI-TD was 2.19 ± 0.46 L/min/m2 and mean CI-MBA was 2.38 ± 0.59 L/min/m2. The mean difference between CI-MBA and CI-TD (bias ± SD) was 0.20 ± 0.47 L/min/m2, and the limits of agreement were -0.72 to 1.11 L/min/m2. The percentage error was 40.0%. The concordance rate was 94%. A secondary analysis of a subgroup of patients during periods of arrhythmia demonstrated a similar accuracy of performance of CI-MBA. CONCLUSIONS: Cardiac index-MBA is not interchangeable with CI-TD. However, CI-MBA provides reasonable correlation and clinically acceptable trending ability compared with CI-TD. Cardiac output-MBA may be useful in trending changes in CI in patients with cardiogenic shock, especially in those whose pulmonary artery catheterization placement carries a high risk or is unobtainable.

Agreement between cardiac output estimation by multi-beat analysis of arterial blood pressure waveforms and continuous thermodilution in post cardiac surgery intensive care unit patients.

We sought to assess agreement of cardiac output estimation between continuous pulmonary artery catheter (PAC) guided thermodilution (CO-CTD) and a novel pulse wave analysis (PWA) method that performs an analysis of multiple beats of the arterial blood pressure waveform (CO-MBA) in post-operative cardiac surgery patients. PAC obtained CO-CTD measurements were compared with CO-MBA measurements from the Argos monitor (Retia Medical; Valhalla, NY, USA), in prospectively enrolled adult cardiac surgical intensive care unit patients. Agreement was assessed via Bland-Altman analysis. Subgroup analysis was performed on data segments identified as arrhythmia, or with low CO (less than 5 L/min). 927 hours of monitoring data from 79 patients was analyzed, of which 26 had arrhythmia. Mean CO-CTD was 5.29 ± 1.14 L/min (bias ± precision), whereas mean CO-MBA was 5.36 ± 1.33 L/min, (4.95 ± 0.80 L/min and 5.04 ± 1.07 L/min in the arrhythmia subgroup). Mean of differences was 0.04 ± 1.04 L/min with an error of 38.2%. In the arrhythmia subgroup, mean of differences was 0.14 ± 0.90 L/min with an error of 35.4%. In the low CO subgroup, mean of differences was 0.26 ± 0.89 L/min with an error of 40.4%. In adult patients after cardiac surgery, including those with low cardiac output and arrhythmia CO-MBA is not interchangeable with the continuous thermodilution method via a PAC, when using a 30% error threshold.

Global cortical activity predicts shape of hand during grasping.

Recent studies show that the amplitude of cortical field potentials is modulated in the time domain by grasping kinematics. However, it is unknown if these low frequency modulations persist and contain enough information to decode grasp kinematics in macro-scale activity measured at the scalp via electroencephalography (EEG). Further, it is unclear as to whether joint angle velocities or movement synergies are the optimal kinematics spaces to decode. In this offline decoding study, we infer from human EEG, hand joint angular velocities as well as synergistic trajectories as subjects perform natural reach-to-grasp movements. Decoding accuracy, measured as the correlation coefficient (r) between the predicted and actual movement kinematics, was r = 0.49 ± 0.02 across 15 hand joints. Across the first three kinematic synergies, decoding accuracies were r = 0.59 ± 0.04, 0.47 ± 0.06, and 0.32 ± 0.05. The spatial-temporal pattern of EEG channel recruitment showed early involvement of contralateral frontal-central scalp areas followed by later activation of central electrodes over primary sensorimotor cortical areas. Information content in EEG about the grasp type peaked at 250 ms after movement onset. The high decoding accuracies in this study are significant not only as evidence for time-domain modulation in macro-scale brain activity, but for the field of brain-machine interfaces as well. Our decoding strategy, which harnesses the neural "symphony" as opposed to local members of the neural ensemble (as in intracranial approaches), may provide a means of extracting information about motor intent for grasping without the need for penetrating electrodes and suggests that it may be soon possible to develop non-invasive neural interfaces for the control of prosthetic limbs.

Decoding repetitive finger movements with brain activity acquired via non-invasive electroencephalography.

We investigated how well repetitive finger tapping movements can be decoded from scalp electroencephalography (EEG) signals. A linear decoder with memory was used to infer continuous index finger angular velocities from the low-pass filtered fluctuations of the amplitude of a plurality of EEG signals distributed across the scalp. To evaluate the accuracy of the decoder, the Pearson's correlation coefficient (r) between the observed and predicted trajectories was calculated in a 10-fold cross-validation scheme. We also assessed attempts to decode finger kinematics from EEG data that was cleaned with independent component analysis (ICA), EEG data from peripheral sensors, and EEG data from rest periods. A genetic algorithm (GA) was used to select combinations of EEG channels that maximized decoding accuracies. Our results (lower quartile r = 0.18, median r = 0.36, upper quartile r = 0.50) show that delta-band EEG signals contain useful information that can be used to infer finger kinematics. Further, the highest decoding accuracies were characterized by highly correlated delta band EEG activity mostly localized to the contralateral central areas of the scalp. Spectral analysis of EEG also showed bilateral alpha band (8-13 Hz) event related desynchronizations (ERDs) and contralateral beta band (20-30 Hz) event related synchronizations (ERSs) localized over central scalp areas. Overall, this study demonstrates the feasibility of decoding finger kinematics from scalp EEG signals.

Observation-based training for neuroprosthetic control of grasping by amputees.

Current brain-machine interfaces (BMIs) allow upper limb amputees to position robotic arms with a high degree of accuracy, but lack the ability to control hand pre-shaping for grasping different objects. We have previously shown that low frequency (0.1-1 Hz) time domain cortical activity recorded at the scalp via electroencephalography (EEG) encodes information about grasp pre-shaping. To transfer this technology to clinical populations such as amputees, the challenge lies in constructing BMI models in the absence of overt training hand movements. Here we show that it is possible to train BMI models using observed grasping movements performed by a robotic hand attached to amputees' residual limb. Three transradial amputees controlled the grasping motion of an attached robotic hand via their EEG, following the action-observation training phase. Over multiple sessions, subjects successfully grasped the presented object (a bottle or a credit card) in 53±16 % of trials, demonstrating the validity of the BMI models. Importantly, the validation of the BMI model was through closed-loop performance, which demonstrates generalization of the model to unseen data. These results suggest `mirror neuron system' properties captured by delta band EEG that allows neural representation for action observation to be used for action control in an EEG-based BMI system.

Decoding the evolving grasping gesture from electroencephalographic (EEG) activity.

Shared control is emerging as a likely strategy for controlling neuroprosthetic devices, in which users specify high level goals but the low-level implementation is carried out by the machine. In this context, predicting the discrete goal is necessary. Although grasping various objects is critical in determining independence in daily life of amputees, decoding of different grasp types from noninvasively recorded brain activity has not been investigated. Here we show results suggesting electroencephalography (EEG) is a feasible modality to extract information on grasp types from the user's brain activity. We found that the information about the intended grasp increases over the grasping movement, and is significantly greater than chance up to 200 ms before movement onset.

Restoration of whole body movement: toward a noninvasive brain-machine interface system.

This article highlights recent advances in the design of noninvasive neural interfaces based on the scalp electroencephalogram (EEG). The simplest of physical tasks, such as turning the page to read this article, requires an intense burst of brain activity. It happens in milliseconds and requires little conscious thought. But for amputees and stroke victims with diminished motor-sensory skills, this process can be difficult or impossible. Our team at the University of Maryland, in conjunction with the Johns Hopkins Applied Physics Laboratory (APL) and the University of Maryland School of Medicine, hopes to offer these people newfound mobility and dexterity. In separate research thrusts, were using data gleaned from scalp EEG to develop reliable brainmachine interface (BMI) systems that could soon control modern devices such as prosthetic limbs or powered robotic exoskeletons.

Reconstructing hand kinematics during reach to grasp movements from electroencephalographic signals.

With continued research on brain machine interfaces (BMIs), it is now possible to control prosthetic arm position in space to a high degree of accuracy. However, a reliable decoder to infer the dexterous movements of fingers from brain activity during a natural grasping motion is still to be demonstrated. Here, we present a methodology to accurately predict and reconstruct natural hand kinematics from non-invasively recorded scalp electroencephalographic (EEG) signals during object grasping movements. The high performance of our decoder is attributed to a combination of the correct input space (time-domain amplitude modulation of delta-band smoothed EEG signals) and an optimal subset of EEG electrodes selected using a genetic algorithm. Trajectories of the joint angles were reconstructed for metacarpo-phalangeal (MCP) joints of the fingers as well as the carpo-metacarpal (CMC) and MCP joints of the thumb. High decoding accuracy (Pearson's correlation coefficient, r) between the predicted and observed trajectories (r = 0.76 ± 0.01; averaged across joints) indicate that this technique may be suitable for use with a closed-loop real-time BMI to control grasping motion in prosthetics with high degrees of freedom. This demonstrates the first successful decoding of hand pre-shaping kinematics from noninvasive neural signals.

Movement decoding from noninvasive neural signals.

It is generally assumed that noninvasively-acquired neural signals contain an insufficient level of information for decoding or reconstructing detailed kinematics of natural, multi-joint limb movements and hand gestures. Here, we review recent findings from our laboratory at the University of Maryland showing that noninvasive scalp electroencephalography (EEG) or magnetoencephalography (MEG) can be used to continuously decode the kinematics of 2D 'center-out' drawing, unconstrained 3D 'center-out' reaching and 3D finger gesturing. These findings suggest that these 'far-field', extra-cranial neural signals contain rich information about the neural representation of movement at the macroscale, and thus these neural representations provide alternative methods for developing noninvasive brain-machine interfaces with wide-ranging clinical relevance and for understanding functional and pathological brain states at various stages of development and aging.

Toward improved sensorimotor integration and learning using upper-limb prosthetic devices.

To harness the increased dexterity and sensing capabilities in advanced prosthetic device designs, amputees will require interfaces supported by novel forms of sensory feedback and novel control paradigms. We are using a motorized elbow brace to feed back grasp forces to the user in the form of extension torques about the elbow. This force display complements myoelectric control of grip closure in which EMG signals are drawn from the biceps muscle. We expect that the action/reaction coupling experienced by the biceps muscle will produce an intuitive paradigm for object manipulation, and we hope to uncover neural correlates to support this hypothesis. In this paper we present results from an experiment in which 7 able-bodied persons attempted to distinguish three objects by stiffness while grasping them under myoelectric control and feeling reaction forces displayed to their elbow. In four conditions (with and without force display, and using biceps myoelectric signals ipsilateral and contralateral to the force display,) ability to correctly identify objects was significantly increased with sensory feedback.