Data Logging | Improvements in Wireless Wearables for Physiology Research
Researchers have long recognized the need for a better tool for Electroencephalogram
(EEG) ambulatory monitoring. While many have come up with certain wired
solutions, there has not been a valid solution for long term logging or
telemetry. BIOPAC Systems, Inc. has now introduced the brand new BioNomadix
Logger, a true wireless and wearable ambulatory monitoring device. The Logger
allows researchers the option for long term monitoring with its ability to
store data for later upload or it can also telemeter back to a computer for
live recording in a lab setting. The new BioNomadix logger truly allows
you to conduct physiology experiments anywhere and everywhere – you can log up
to 24 hours of high-quality data outside the lab and includes ECG,
EEG, EMG, EOG, EGG, EDA, Pulse, Respiration, Temperature, Cardiac Output, Heel
& Toe Strike, Clench Force, Accelerometer, & Goniometry signals. The
device is small in size, only about the size of your hand, making it easy for
subjects to take it with them in their everyday activities. The BioNomadix Logger fills the void where other more non-accessible devices could not. A study performed by researchers at the University of Minho aimed to create a wireless, wearable EEG ambulatory monitoring solution through a combination of other devices. They tested their tool against other devices (such as BIOPAC’s B-Alert X10) to measure the quality of the EEG signal. Although the researcher’s tool was able to record high quality data, it was bulky and could only be used on subjects in a lab. The BioNomadix Logger thus picks up where this study left off, allowing subjects to log data while performing everyday activities. The Logger’s small size also bypasses the bulky nature of other EEG ambulatory monitoring devices. The BioNomadix Logger thus represents a great step forward in long term wireless, wearable, ambulatory monitoring and data logging devices.
ECG Analysis: VLPs | Data Acquisition
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
ECG Analysis of Putting Tournament
An electrocardiogram (ECG; a.k.a.
EKG) recording can be extremely useful for analysis of a variety of
physiological studies. When combined with automated ECG analysis software,
researchers can identify ECG intervals, assess heart rate variability (HRV),
classify heart beats, and much more. ECG analysis results can be used with other
parameters to perform a complete physiological examination. Analyzing changes in ECG rhythms can provide
valuable insight into stress, arousal, and exercise research. A wide
range of physiological studies incorporate ECG results, such as a recent one
performed measuring cognitive workloads of participants during a competitive
golf putting tournament. The researchers set about to compare whether kinematic
or psychological factors were more important for participant’s putting
performance. Since putting requires more delicacy and precision on the part of
the golfer, they hypothesized that psychological factors would be just as
important --if not more-- than kinematic factors. The participants were divided
into three groups, arranged by skill level, and given the CSAI-2 test, an exam
that scored the participants’ self-confidence in the competitive environment.
The groups then performed in 8 tournaments of putting 2.1m from the hole. The
researchers used a BIOPAC ECG module to record and analyze heart rate and HRV,
which they ranked as either high or low. They found that the winners of the
tournament had a lower HRV frequency, which was associated with a lower mental
workload. There were also big differences in self-confidence scores on the
CSAI-2 between the winners and losers of the groups (specifically of the highest
skill level group). The results indicate that participants with lower mental
stress performed better, meaning that psychological factors are important to
putting ability. Although more research needs to be done, the results seem to
indicate that psychological factors seem to be more important to a golfer’s
short game.
Logging Physiological Data | Data Acquisition
Logging subject
data has never been easier than with the advent of wireless subject recording
devices. Quality wireless products allow for accurate readings on a subject’s
physiology in ways tethered devices cannot. Now with products like the Mobita
wearable biopotential system, data logging is simpler than ever before. Mobita
is a physiological signal amplifier system that can record up to 32 channels of
high-fidelity wireless biopotential data, including ECG, EEG, EGG, EMG, and EOG
data. The Mobita also contains an onboard accelerometer that allows for, along
with AcqKnowledge’s Actigraphy feature, evaluation of a subject’s
activity levels.
Together with AcqKnowledge software, the system can be
quickly configured to do the work of multiple systems without the added cost of
multiple amplifiers. Simply disconnect one header and
snap on a new configuration for a completely different application. The
system has the option to either log data locally for later download or
telemeter back to a computer running AcqKnowledge for real-time display.
The Mobita can easily switch between either live or logged mode to suit your
research protocol. Using the built-in WiFi telemetry allows for a wide range of
mobile subject recordings. The system has flash disk recording for up to 16 GB
allowing large chunks of data to be stored and kept for back-up. Mobita is also
a very flexible, wearable system due to its technical power and small size. Don’t let the system’s small size and rechargeable battery power operation fool
you—the Mobita has the power to record up to 32 channels at up to 2K s/s and is
fully integrated with AcqKnowledge. The system features a rugged
construction making it well suited for tough and demanding measurement
situations. The Mobita system is sturdy, dustproof, and most importantly is
kept safe in its waterproof enclosure. The Mobita is the premier wireless
biopotential system that is uniquely suited for a variety of applications such
as psychology, neuromarketing, sports, ambulatory testing, and many more. See
Mobita Systems Biopotentials