There is an unmet clinical need for a low cost and easy to use wearable devices for continuous cardiovascular health monitoring. A flexible and wearable wristband, based on microelectromechanical sensor (MEMS) elements array was developed to support this need. The performance of the device in cardiovascular monitoring was investigated by (i) comparing the arterial pressure waveform recordings to the gold standard, invasive catheter recording (
n
= 18), (ii) analyzing the ability to detect irregularities of the rhythm (
n
= 7), and (iii) measuring the heartrate monitoring accuracy (
n
= 31). Arterial waveforms carry important physiological information and the comparison study revealed that the recordings made with the wearable device and with the gold standard device resulted in almost identical (
r
= 0.9–0.99) pulse waveforms. The device can measure the heart rhythm and possible irregularities in it. A clustering analysis demonstrates a perfect classification accuracy between atrial fibrillation (AF) and sinus rhythm. The heartrate monitoring study showed near perfect beat-to-beat accuracy (sensitivity = 99.1%, precision = 100%) on healthy subjects. In contrast, beat-to-beat detection from coronary artery disease patients was challenging, but the averaged heartrate was extracted successfully (95% CI: −1.2 to 1.1 bpm). In conclusion, the results indicate that the device could be useful in remote monitoring of cardiovascular diseases and personalized medicine.
Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-ofconcept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.
Atrial fibrillation (AFib) is the most common cardiac arrhythmia, affecting eventually up to a quarter of the population. The purpose of this small scale clinical study was to validate the usability of MEMS accelerometer based bedsensor for detection of AFib. A Murata accelerometer based ballistocardiogram bedsensor was attached under the hospital bed magnetically and measurement data was recorded from 20 AFib patients and 15 healthy volunteers, mainly females. The recording time was up 30 minutes. The sensor built-in algorithms automatically extracted features such as heart rate (HR), heart rate variability (HRV), relative stroke volume (SVOL), signal strength (SS) and whether the patient is in bed or not. We calculated median values for each feature HR, HRV, SVOL and SS, and investigated whether it is possible to separate AFib from healthy with these features or their combinations. Areas under the curve (AUC) were 0.98 for full length signals and 0.85 for 3 min signal segments using random forest (RF) classifier corresponding to sensitivity and specificity of 100% and 93.3% for full length signals and 90% and 80% for 3 min signals. We conclude, that based on our pilot results, the Murata bedsensor is able to detect AFib, and seems to be a promising technology for long-term monitoring of AFib at home settings as it requires only one-time installation and operational time can be up to years and even tens of years.
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