Life Science Hardware and Software

Cardiovascular tests during a self-paced maximal exercise protocol (SPV) continually scored high ratings of VO2 max when compared to more traditional procedures. Jenkins et. al sought to understand the underlying causes of this increase in VO2 max by testing SPV versus the more regimented RAMP method. They sought to explore the results through extensive physiological measurement, as well as testing difference in older and younger age groups, while participants completed physical experiments.

The SPV protocol was completed on an air-braked cycle ergometer, which allowed participants to continually vary their Power Output (PO) throughout the test. An electro-magnetically braked cycle ergometer was used for the RAMP protocol, so that PO was fixed for each stage of the incremental RAMP protocol.

VO2 Max is essentially the maximum amount of oxygen utilized during a workout. Forty-four (44) male and female participants completed the experiment, half aged between 18- 30 and half between 50-75. The participants completed each test over a multi-day period. The tests were exhaustive, requiring subjects to cycle in place until they couldn’t any longer.

Jenkins et. al recorded various physiological signals including NIRS, breathing/expired gases, cardiac output/ stroke volume, blood lactate, and electromyography (EMG). BIOPAC’s BioNomadix research acquisition system wirelessly transmitted EMG data using two electrodes placed on participants’ right leg while they completed physical tasks.

Researchers were able find differences in the interaction effects of EMG between the two test protocols in the older group. The results complied with previous research, in that SPV allowed a higher VO2 max compared RAMP. Through monitoring physiological measurement, the study results suggested increased oxygen delivery as to an increase in oxygen-muscle extraction. The researchers found that there wasn’t a significance difference between the two testing protocols with the older population, though it’s unclear why. Overall, the experiment provides greater understanding of what causes differences in VO2 max between the two experimental procedures.

There is a major concern growing in the medical community that the ratio of health workers to population size is decreasing. This means that the number of available doctors and medical professionals is starting to become too small to handle the number of people needing medical help. Technologies are therefore being created to help bridge the gap that is being created. These “Quality of Life Technologies” (QoLTs) have been developed to help monitor the health of people. While these technologies have been able to provide physiological support to individuals, the same could not be said for mental symptoms. If QoLTs could move into the realm of psychology and self-therapy, they could help improve the mood and quality of life for patients. A group of researchers from the Polytechnic University of Bucharest and the University of Lincoln recently published a paper that presents a machine learning approach for stress detection using wearable physiological amplifiers. To induce stress in participants, the researchers had them perform both a public speaking and cognitive task, which according to previous research these tasks caused the highest increase in measurable signals.

For their experimental setup, they used a BIOPAC BioNomadix BN-PPGED wireless transducer, hooked up to an MP150 data acquisition system, to record both EDA and PPG signals. They then used AcqKnowledge 4 software to extract both the PPG autocorrelation signal and Heart Rate Variability (HRV). Their results provided accurate stress detection in individuals. Their analysis marks a good starting point toward real-time mood detection, which could lead to people improving their quality of life. One way they could improve their experimental setup however, would be to use the BioNomadix Logger. This device allows for up to 24 hours of high quality data logging allowing the researchers to analyze a subject’s data from when they encountered stressful situations outside the lab.

Focal hand dystonia, also known as “musician’s cramp,” is a movement disorder that causes involuntary flexing in the fingers, or finger cramps, when playing a musical instrument. This disorder poses a huge problem for professional musicians and in some cases can even threaten their careers. Many methods have been attempted to try and alleviate the ailment, but the most effective training method has been the “slow-down exercise” (SDE). This exercise, based on the fact that symptoms disappear when playing at a slow tempo, involves selecting a short passage that triggers the cramps then slowing the tempo down to where the musician can play without involuntary finger flexing. The same passage is then repeated over and over, gradually increasing the speed over time. While this method has helped improve symptoms, it was unclear what aspects of motor skills improved through SDE training.

Michiko Yoshie, Naotaka Sakai, Tatsuyuki Ohtsuki and Kazutoshi Kudo investigated how SDE affected motor performance, muscular activity, and somatosensation in a dystonic pianist. The study entitled “Slow-Down Exercise Reverses Sensorimotor Reorganization in Focal Hand Dystonia: A Case Study of a Pianist” tested a musician over a 12 month period as she underwent SDE training for 30 minutes a day, playing a specific passage that evoked the finger cramps most substantially. During the motor task, the musician’s surface EMG was recorded using EMG amplifiers and a BIOPAC MP Data Acquisition System. Throughout the rehabilitation process the musician improved her speed of key strokes and actually helped recover her normal motor and somatosensory functions. The researchers even found evidence that showed the brain had the capacity to reverse sensorimotor reorganization that was induced by the focal hand dystonia. The findings objectively show that SDE training not only improves effected people’s key strokes but helps to completely recover from the neurological disorder.

Emotional reactions influence, and may help predict, our decisions and offer valuable information for communication and neuromarketing researchers, but emotion is difficult to measure explicitly. Emotional responses are complex phenomena consisting of multiple components, including evaluation/appraisal, subjective feeling, expression, and physiological reaction. This mix of components is difficult to measure. Researchers can interview or survey participants about their feelings—typical measures include traditional Likert-type questions, open-ended questions, or pictorial scales—but self-reporting doesn’t easily convey true or complete emotional response. Self-reporting is further complicated by the fact that participations often choose different terms to describe their feelings or respond that they feel nothing. Blending self-assessment with physiological changes that reflect visceral responses provides an unfiltered representation of emotion.
 

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. 

Neuromarketing solutions are available that allow you to examine physiological responses to products, environments, other stimuli or decision making tasks. Multiple physiological signals can be recorded and analyzed for Neuromarketing, including EEG. EEG analysis options allow for understanding of the subject's cognitive state for a variety of Neuromarketing tasks.  Gauging level of engagement, workload and drowsiness while performing tasks or participating in consumer test situations can provide valuable insight into product development and marketing. Neuromarketing solutions should be as unobtrusive as possible, allowing for wireless EEG and other physiological signals to be recorded and analyzed from mobile subjects. Whether subjects are in a lab or real-world environment, users should have the ability to stream physiological data live or record and log up to 32-channels of high-quality biopotential data.

Electricity is part of everything the body does.

Neuromarketing and neuroeconomics research use physiological data to examine a participant’s response to a product, service, or decision making task. Researchers combine cognitive, sensorimotor, and affective responses to marketing stimuli from advertisements to political debates.