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As the leader in wearable biosensor technology, Vital Connect offers devices for remote patients and wireless hospital monitoring. It uses its expertise in mobile and cloud software, data analytics, bioengineering, and chip design in providing technology for decision-making paradigms to realize successful health and economic results. Vital Connect offers the VitalPatch sensor and HealthPatch MD biosensor. They are used for in inpatient and outpatient settings in hospitals, cardiac monitoring, post discharge care, and pharmaceutical solutions. Powered by the VitalCore processor, the HealthPatch MD is the best in its class, while VitalPatch has the same powerful continuous measurements of its predecessor in a single-use, peel-and-stick device.
VitalPatch is an adhesive, wireless, and lightweight patch that monitors and records the heart rate, ECG, respiratory rate, heart rate variability, posture, skin temperature, and fall and step count detection with clinical accuracy. It can be integrated with the Qolty app, and because of the Virtual Connect platform, care teams can receive notifications and accurate data to make timely interventions and health decisions.
As a wireless data collection system, the Vital Connect Platform has different subsystems. It consists of an adhesive patch and a sensor module. The disposable adhesive patch is the sensors interface to the patient’s body while the sensor module collects physiologic data. The latter also provides bi-directional communication with the software library, which manages communication with the server. The Relay Software Library is part of Vital Connect’s relay device.
The Vital Connect Platform is most useful to healthcare professionals who must collect physiological data of patients either in the home or hospital settings. It can notify them when certain physiological changes occur.
The Vital Connect has two parts: the adhesive path and the electronics module. The adhesive patch is disposable and houses the battery and ECG electrodes. The electronics module houses the BLE transceiver, tri-axial accelerometer, and the embedded processor.
A patient wears the adhesive patch in any of the three locations (Fig.1)
- on the left mid-clavicular line over second intercostal space
- over the upper sternum
on the left mid-clavicular line over sixth intercostal space
The adhesive patch has a coin-cell battery. The ECG electrodes are at the bottom of the patch and can record single-lead ECG.
Within the electronics module, the tri-axial accelerometer records the acceleration. The module, in turn, processes the incoming signals then transmits the data to a smartphone that displays and stores the data. The smartphone has the capability to relay the information to the cloud server for more processing. The server also sends the information to the caregivers. Lastly, it can also track trends.
The ECG waveforms detect the QRS complexes through a wavelength algorithm. The system then computes the R-R intervals for use in the HRV computation. It also computes the instantaneous heart rate using the reciprocal of the R-R intervals. This instantaneous heart rate passes through a 10-beat low pass filter to generate the smooth heart rate profile.
The two ECG-derived respiratory signals (QRS amplitude and respiratory sinus arrhythmia) and the accelerometer signal produce the respiration rate. The quality metric Q is the estimate of the peak regularity of each signal. It helps to compute the weighted average of each respiratory rate to generate the final breathing rate estimate.
The tri-axial accelerometer data determines the fall detection, using the impact or free fall, change in thoracic posture from vertical to horizontal, large differences in acceleration over a smaller time frame, and low activity after a change in posture for a specified duration.
The Vital Connect can also detect changes in posture. The change, recorded within 5 seconds, can be detected as upright, lying, running, leaning, or walking. It can detect the static posture based on the individual’s thoracic angle. The threshold of acceleration in the vertical axis and steps detection helps assessing running and walking.
Energy Expenditure Assessment
Selvaraj and Doan (2014) used the wireless HealthPatch sensor in their assessment of the energy expenditure (EE) rate and total daily energy expenditure (TEE). The results proved that a HealthPatch sensor is an accurate tool for measuring EE and TEE. The experiment involved 32 individuals, aged 21 – 72, and represented balanced gender and a wide range of body mass index (BMI).
Long-term Remote Monitoring of Vital Signs, Heart Rate, Respiration, Activity, and Falls
Selvaraj (2014) used the wireless patch sensor to assess the performance and compliance of the gadget in measuring vital signs. He gathered 76 senior participants and ran the experiment for 3603 days. The results encouraged the usability and wearability of the HealthPatch sensor at home. Usually, experiments targeted young participants and some ADLs in assessing the specificity and sensitivity of fall detection systems. The Selvaraj experiment showed the suitability of Vital Connect Platform in elderly participants for long-term monitoring at home.
Chan, Selvaraj, Ferdosi, and Narasimhan (2013) compared the accuracy of the wireless patch sensor with other medical devices. They discovered that the results of the HealthPatch in measuring activity and vital signs are comparable with these other larger and traditional medical devices.
Automated Prediction of Apnea-Hypopnea Index and Detection of Sleep Apnea
Sleep Apnea Syndrome (SAS) is the complete or partial blockage of the upper airway when an individual is asleep. The Apnea-Hypopnea Index measures the severity of this disorder. It is the average number of times a person experiences apnea or hypopnea per hour. Usually, between 2% and 5% of adult women and 3% to 7% of adult men have sleep apnea syndrome.
Selvaraj and Narasimhan (2013) used HealthPatch sensor in estimating the apnea-hypopnea index. They developed an algorithm that uses the features based on the statistical and filtering dispersion of the nasal airflow respiration signal. This new algorithm was able to detect apnea or hypopnea occurrences on a per-second basis.
The researchers then compared this algorithm with the gold standard in quantifying apnea-hypopnea index, the polysomnography (PSG). They discovered that the adhesive HealthPatch is comparable in accuracy in estimating the apnea-hypopnea index values with the PSG. As such, the HealthPatch sensor can be used in sleep apnea syndrome screening as an inexpensive and convenient wireless solution for recurrent sleep apnea evaluation.
Ambulatory Respiratory Rate Detection using a Tri-Axial Accelerometer and ECG
Chan, Ferdosi, and Narasimhan (2013) used the HealthPatch sensor in describing a low complexity algorithm to determine respiratory rates for remote patient monitoring and respiratory diseases screening. They validated the algorithm using 15 elderly participants performing the different daily living activities.
Skin-Contact Sensor for Automatic Fall Detection
Narasimhan (2012) used the HealthPatch sensor to detect human falls automatically. The tri-axial accelerometer, Bluetooth Low Energy transceiver, and microcontroller found in the sensor can detect a possible fall if there is impact and the person lies horizontally after the fall. Furthermore, the activity after the fall must be below the threshold. The study used 15 elderly volunteers to perform activities of daily living (ADL) and 10 volunteers to perform intentional falls using a gymnastics mat. The algorithm developed offered 100% specificity and 99% sensitivity.
Strengths and Limitations
The ease of use, optimization of clinical workflows, and minimal instances of patient’s discomfort due to the unobtrusive nature are one of the stellar advantages of the Vital Connect patches. Furthermore, cross-contamination is nonexistent because the patch is disposable, and there are no reusable components to manage.
The present solutions for long-term remote patient monitoring include electronic textile technologies that allow the patient to attach wearable body electronics on their body. This technology has issues on lack of standards, mass production, 24-hour monitoring capability, reliability, comfort, and accuracy. However, the HealthPatch sensor is an effective device for 24-hour monitoring. Moreover, it overcomes many of these limitations.
VitalPatch is an inexpensive wearable biosensor that can improve patient outcomes. Caregivers and medical practitioners can monitor the patient wearing the VitalPatch irrespective of any location. They can detect patient deterioration early and can propose an immediate intervention to care teams.
The HealthPatch sensor can transmit notifications about abnormal changes in vital signs, no or unusual activity, long-term trending of vital signs, and falls. Information from the device can be valuable for patient care management either in the home or hospital settings. The wireless biosensor reduces medical costs and promotes effective routine consultations by offering objective physiological trends to the principal care physician.
Ronald Rosenberg (2015) listed short battery life (two to four days) as the disadvantage of the HealthPatch.
Chan, A.M., Selvaraj, N., Ferdosi, N., & Narasimhan, R. (2013). Wireless Patch sensor for Remote Monitoring of Heart Rate, Respiration, Activity, and Falls. 35th Annual International Conference of the IEEE EMBS.
Chan, A.M., Ferdosi, N., & Narasimhan, R. (2013). Ambulatory Respiratory Rate Detection using ECG and a Tri-Axial Accelerometer. 35th Annual International Conference of the IEEE EMBS.
Narasimhan, R. (2012). Skin-Contact Sensor for Automatic Fall Detection. 34th Annual International Conference of the IEEE EMBS.
Rosenberg, R. (2015). Early inroads for wearable devices in clinical trials. The CenterWatch Monthly, 22(8).
Selvaraj, N., & Doan, T. (2014). Performance of energy expenditure assessment using a chest-worn wireless patch sensor. MobiHealth. DOI 10.4108/icst.mobihealth.2014.257292
Selvaraj, N. (2014). Long-term Remote Monitoring of Vital Signs using a Wireless Patch Sensor. 2014 Health Innovations and Point-of-Care Technologies Conference.
Selvaraj, N., & Narasimhan, R. (2013). Detection of Sleep Apnea on a Per-Second Basis Using Respiratory Signals. 35th Annual International Conference of the IEEE EMBS.