While simultaneous acquisition of electrocardiography (ECG) data during MRI is a widely used clinical technique, the effects of the MRI environment on impedance cardiography (ICG) data have not been characterized. We collected echo planar MRI scans while simultaneously recording ECG and thoracic impedance using carbon fiber electrodes and customized amplifiers. Here, we show that the key changes in impedance (dZ/dt) and features of the ECG waveforms are not obstructed during MRI. We present a method for ensemble averaging ICG/ECG signals collected during MRI and show that it performs comparably with signals collected outside the MRI environment. These results indicate that ICG can be used during MRI to measure stroke volume, cardiac output, preejection period, and left ventricular ejection time.

The proposed Physiological Signal Processing Laboratory incorporates important new concepts to further its utility as a vehicle for biomedical engineering educational use. The Laboratory incorporates the physical construction, testing and analysis of eight signal processing circuit modules, introduced as lessons. Each module can be characterized through measurement with a BIOPAC MP35 data acquisition system and a student-built square wave generator. The modules are combined sequentially to create a sophisticated and functional electrocardiogram (ECG) amplification and processing system. By the final lesson, the completed ECG signal processor will provide meaningful outputs from signals sourced from the student's own body. Through the application of a single, easy-to-use data acquisition system and associated software to a breadboard circuitry laboratory, students can build, test and analyze signal processing modules, verify their performance against mathematical simulation using graphical comparisons, combine modules, collect physiological signals sourced from their own bodies, and evaluate the results. By developing the complete ECG signal processor, module by module (as eight lessons), students develop an understanding of system design and development methodologies. In addition, when collecting data directly from their own bodies, students' curiosity is stimulated to create an environment more amenable to inquiry-based learning.

The proposed Biomedical Signal Processing Laboratory incorporates several components that enhance its usefulness for inquiry-based learning. The Laboratory orients around the physical construction and testing of a variety of simple signal processing circuit modules, introduced as lessons. The characteristics of each module can be easily determined through measurement with a BIOPAC data acquisition system. Additionally, the system software permits simple comparisons between real-world and simulated signal processing module characteristics. The modules can be combined in a step-by-step fashion to create a variety of sophisticated and functional signal processing systems. Signal processing systems established by the Laboratory provide meaningful outputs from signals sourced from the student's own body. Through the application of an single, easy-to-use data acquisition system and associated software, students can build and test signal processing modules, verify their performance against mathematical simulation using graphical comparisons, combine modules, collect physiological signals sourced from their own bodies and then analyze the results. In the process of collecting data directly from their own bodies, students' curiosity is stimulated and they gain more control over their own learning by being able to test and retest to more fully understand the steps involved in scientific inquiry.

Laboratory courses are used throughout Biomedical Engineering curriculum to give students hands-on, practical experience in scientific, computing and engineering methods. Interest in student-driven, inquiry-based labs has resulted in the availability of new teaching equipment for the exploration of biological systems and physiological processes. The movement to student-driven, inquiry-based labs is rooted in the belief that students will improve their critical thinking skills, achieve a greater understanding of processes explored in the lab and experience reduced frustration when gathering data. New teaching equipment allows for relatively easy collection of real-time physiological data: ECG, EEG, EMG, EOG (eye movement), pulse, skin temperature, respiration (flow and volume), limb and joint motion (distance, velocity and acceleration), electrodermal activity and response, muscle strength. New teaching equipment can aid the transition from instructor-dictated to student-driven laboratories. As students collect data directly from their own bodies, the process therein will stimulate their curiosity and give them more control over their own learning by allowing them to test and retest to more fully understand the steps involved in scientific inquiry. Student-driven laboratory settings can increase student understanding of biomedical engineering principles as well as increase student appreciation of the scientific process.Laboratory courses are used throughout Biomedical Engineering curriculum to give students hands-on, practical experience in scientific, computing and engineering methods. Computerized data acquisition and analysis teaching systems, such as the BIOPAC Student Lab, provide a strong, multi-discipline alternative to conventional classroom approaches. Additionally, computerized teaching systems for the Biomedical Engineering laboratory can effectively augment distance learning and Internet education programs.Interest in student-driven, inquiry-based labs has resulted in the development of new teaching equipment for the exploration of biological systems and physiological processes. The movement to studentdriven, inquiry-based labs is rooted in the belief that students will improve their critical thinking skills, achieve a greater understanding of processes explored in the lab and experience reduced frustration when gathering data 1 . Through laboratory investigation, students learn to:• Computerized lab setups not only make data collection easier, but also allow students to perform analyses that are impossible on traditional lab equipment such as a chart recorder. Students can cut and paste sections of data, perform mathematical and statistical transformations, and copy data to other applications (such as a drawing program or spreadsheet). By employing a mantle of specialized teaching software to optionally control the data collection, processing and analysis characteristics of a conventional data acquisition system, new opportunities are presented to the teacher and student for the understanding...

The electrocardiogram (ECG) and impedance cardiography (ICG) are typically combined to estimate electromechanical features such as the pre-ejection period (PEP) and left ventricular ejection time (LVET); indicators of changes in the cardiac specific drive of the autonomic nervous system (ANS). Current methods of ICG are time intensive in subject preparation and the measurements are vulnerable to non-reproducible subject-specific electrode configuration. Furthermore, analysis of impedance waveforms can be time consuming and labeling of key time points can suffer from experimenter bias. Here we present a wearable heart monitor that includes ECG, but replaces the commonly used 8 ICG electrodes with a single accelerometer (ACC) placed at the suprasternal notch. The ACC indirectly measures movement of the arterial pulse wave as blood is ejected into the aorta and great vessels. The resulting ACC waveform is processed into two smooth and readily identified waves, corresponding to the timing of the opening and closing of the aortic valve. We tested the ACC’s utility and reliability for tracking cardiac ANS tone by comparing PEP and LVET measurements obtained simultaneously with conventional ICG and the ACC. Participants were recorded in the sitting and supine position with ECG, ICG, and ACC. While seated, they engaged in a classic physical stress task known to modulate ANS activity. There were obvious and significant associations between ICG and ACC estimates of PEP and LVET derivatives with respect to time. These findings support ACC as a complementary method for tracking ANS that is robust, time efficient, and readily accessible to researchers.