Life Science Hardware and Software

Ensemble analysis averages raw waveform signals through lining up their peaks, allowing researchers to mitigate noise or potential outside artifacts. Researchers Cieslak et al. (2017) from University of California, Santa Barbara, identified and assessed a new open source tool that conducts ensemble analysis of cardiovascular data. The moving ensemble analysis pipeline (MEAP) builds on classic collection and analysis tools; not only in detecting cardiovascular state during an experiment, but also in measuring how cardiovascular cycles change overtime.
Cardiovascular measurements are typically averaged to reduce noise, but traditional measurement methods made capturing changes in cardiovascular cycles restricted to a select window of time. This makes it difficult to assess fast changes with traditional cardiovascular ICG data. With MEAP, variability is better analyzed, allowing it to become a more accurate dimension of assessment.
In assessing MEAP’s viability, researchers measured two participants as they completed four different tasks. The experiment began and ended with a random dot kinetogram task allowing for a baseline control of cardiovascular activity. This was followed by the “cold presser” and “Valsalva,” two tasks that were expected to induce strong physiological reactions. Another task included a video game, seen as having less predictive effects.
Two subjects were measured for ECG and other physiological signals as they completed the four physical and cognitive tasks. BIOPAC’s research solutions included ECG100C utilized for ECG, NICO100C-MRI to collect ICG signals, and NIBP100D CNAP Monitor 500 to record blood pressure. Data was gathered and measured with MP Research System with AcqKnowledge software.
The results pointed to changes typical cardiovascular measures wouldn’t be able to describe. This was seen during the Valsalva maneuver, where rapid baroflex changes occurred. It was also found cardiovascular data varied immensely while performing repetitive tasks.
The paper recognizes MEAP’s potential for rapidly advancing findings that use cardiovascular data. The authors point to this tool’s potential ability for exploring new areas of study that have been difficult to quantify in the past, such as linking cardiovascular reactivity to motivation. In acknowledging the benefits of MEAP, the authors stress the importance of not overstepping smaller aspects of acquisition, such as poorly attached electrodes or imbalanced experiment design. Overall, this paper recognizes, analyzes, and validates this exciting new development in the field of cardiovascular research.

One of the foundational concepts making virtual reality (VR) video games of high interest is the game’s ability to transport the user to a flow state. The visual and auditory stimuli presented are highly immersive, leading the user to focus entirely on the game, often losing track of time. Flow states are characterized by this lack of time awareness, as participants are able to match their abilities with the demands of the game (i.e., the game is not too easy or too hard). But what qualities reflect a flow state as it is happening in the moment? Researchers from Shandong University tested five first-level physiological functions to determine their efficacy in reflecting a flow state, including EMG, EDA, EEG, respiratory rate, and cardiovascular activity. Thirty-six students participated in a VR game while having their physiological responses monitored. Prior to the experiment, the researchers placed BIOPAC’s wearable dual-signal BioNomadix transmitters with appropriate electrodes on the participants to wirelessly record Respiration and ECG data. Participants were then seated in the designated gaming chair for five minutes to record baseline responses. After this, they played the game for six minutes, followed by a questionnaire about flow experience. The results showed that the five physiological functions, as a whole, indicated flow state, though respiratory rate was most effective. The authors note that, as a physiologic arousal marker, respiratory rate best predicted flow state.

Having physiologic indicators of a flow state not only assists future research, but also provides a method for real-time feedback on the efficacy of the game. The authors note that with better biometric data comes the opportunity to provide a better gaming experience, with real-time adjustments. If the physiologic responses and adjustments could be integrated with the gaming software, games would be far more realistic.

An electrocardiogram (ECG or EKG) is a graphical recording of the changes occurring in the electrical potentials between different sites on the skin as a result of cardiac activity. The electrical activity of the heart is a sequence of depolarizations and repolarizations. Depolarization occurs when the cardiac cells, which are electrically polarized, lose their internal negativity. The spread of depolarization travels from cell to cell, producing a wave of depolarization across the entire heart. This wave represents a flow of electricity that can be detected by electrodes placed on the surface of the body. Once depolarization is complete, the cardiac cells are restored to their resting potential, a process called repolarization. This flow of energy takes on the form of the ECG wave, and is characterized by an initial P wave, followed by the QRS complex, and then the T wave. The P wave is associated with depolarization of the atria, the QRS complex is associated with depolarization of the ventricles, and the T wave with repolarization of the ventricles. Ventricular Late Potentials (VLPs, also called Ventricular Delayed Potentials) are small-amplitude, short-duration waves that occur after the QRS complex and are precursors to cardiac arrhythmias.

Use AcqKnowledge® software to apply signal averaging on the ECG signal to detect VLPs. To perform a VLP measurement on an ECG recording, use off-line averaging to trigger on the R-wave peaks and average the time delta of 209 ms before to 200 ms after the occurrence of each peak. AcqKnowledge measurement tools can calculate the duration and Root Mean Square (rms) values of the VLPs. AcqKnowledge also simplifies other ECG Analysis with powerful, fully automated routines for use post ECG recording: use the ECG averaging function to evaluate changes in the ECG complex before, during, and after exercise or dosing; perform heart rate variability (HRV) analysis; measure respiratory sinus arrhythmia (RSA); and more... http://www.biopac.com/ecg-cardiology

Data acquisition hardware and software requirements vary widely based on experiment protocol, classroom setup, field studies, etc. BIOPAC data acquisition hardware platforms support wired, wireless, and fMRI setups, for human or animal subjects, with powerful, intuitive data software for research and teaching applications. Use with a variety of amplifiers, stimulators, triggers, transducers, gas analysis modules, and/or electrodes to acquire life science signals, including ECG, EEG, EOG, EMG, EGG, EDA, Respiration, Pulse, Temperature, Impedance Cardiography, Force, Accelerometry, Goniometry, Dynamometry, Gyro, and more. Combine data hardware for multi-subject or multi-parameter protocols.

Research hardware platforms are fully-integrated with AcqKnowledge^® data acquisition software, which provides automated routines for data scoring, measurement, and reporting, and can support multiple hardware units. Teaching platforms include Biopac Student Lab software with media-rich tutorial style guide lessons for specified objectives, plus active learning options for student-designed experiments and advanced analysis.

Wired (tethered) data acquisition hardware platforms include the MP150 and MP36R Research Systems. The MP150 16-channel system with universal amplifier provides high resolution (16 bit), high-speed acquisition (400 kHz aggregate) with16 analog inputs and two analog outputs, digital I/O lines (to automatically control other TTL level equipment), and online calculation channels. The MP36R 4-channel research system with built-in amplifiers provides four analog inputs and one analog output, I/O port for digital devices, calculation channels, trigger port, headphone jack, and electrode impedance checker. The MP36R supports software-controlled amplifiers and calculation channels.

Mobita^® 32-channel wearable wireless systems are ideal for biopotential applications that demand subject mobility and data logging. The Mobita EEG System uses water electrodes—no skin prep or gels required. Record live data into AcqKnowledge or log to an internal storage card for later upload into AcqKnowledge; modes are easily switched to suit specific protocols.

B-Alert X10^® Wireless Systems provide nine channels of high fidelity EEG plus ECG, and data software for cognitive state metrics software is available. The stand-alone system easily interfaces with MP150 Research System to synchronize with other physiological data.

BioHarness^® with AcqKnowledge is a lightweight, non-restrictive data logger and telemetry system to monitor, record, and analyze a variety of physiological parameters, including ECG, respiration, posture, and acceleration.

Stellar^® Small Animal Telemetry Licenses with AcqKnowledge control wireless data acquisition from Stellar Implantable Telemetry Systems. The easy-to-configure Animal Scheduler works for a subset or complete group of conscious, unrestrained small animals for long term recordings. Multiple display modes can be viewed simultaneously, and signal conditioning tools (e.g., filtering and artifact removal) can be applied.


These and other BIOPAC data hardware and software solutions are used in thousands of labs worldwide and cited in thousands of publications. Learn more about research systems and teaching systems.

Electrocardiogram signals can be recorded from humans or animals wirelessly or via tethered amplifiers(BioNomadix). Analyzing changes in ECG rhythms can provide valuable insight into stress, arousal, and exercise research.  Automated ECG Analysis packages are available that provide a wide variety of automated ECG analysis options.