Validation of an Accelerometer Based BCG Method for Sleep Analysis

Sleep problems are one of the most common medical complaints today. Polysomnography (PSG) as the current standard for sleep analysis is expensive, intrusive and complex. Thus, finding a reliable and unobtrusive method for longer-term home use is important. Ballistocardiography (BCG) based methods have shown potential in sleep analysis recently. The usability and performance of a BCG based method in qualitative and quantitative analysis of sleep was evaluated. The method was validated in a clinical test on 20 subjects using PSG as a reference. Heart rate (HR), heart rate variability (HRV), respiratory rate (RR), respiratory rate variability (RRV), respiratory depth (Rdepth) and movement were utilized for sleep stage detection. The BCG parameter accuracy was presented as the mean error from PSG with 95% confidence interval. The errors were -0.1 ± 4.4 beats per minute for HR, -0.9 ± 14.7 ms for high frequency (HF) HRV, -3.0 ± 29.9 ms for low frequency (LF) HRV, 0.3 ± 4.5 breaths per minute for RR and -40 ± 424 ms for RRV respectively. Correlation coefficient was 0.97 for HR, 0.67 for HF HRV, 0.71 for LF HRV, 0.54 for RR and 0.49 for RRV. HR, RRV and Rdepth were typically at an increased level in REM sleep and wakefulness and decreased in deep sleep. RRV was at its highest during wakefulness. HRV was at a decreased level in REM and wakefulness and increased in deep sleep. Movement was higher during wakefulness than in sleep

Clinical assessment of a non-invasive wearable MEMS pressure sensor array for monitoring of arterial pulse waveform, heart rate and detection of atrial fibrillation

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

Unianalyysiteknologian hyödyntäminen työssä käyvien stressin tunnistamisessa

TYÖPERÄINEN STRESSI on nykyisin yksi työelämän suurimmista haasteista. Tarvitaan uusia menetelmiä ja työskentelytapoja, jotta haasteeseen voidaan tarttua ajoissa, ennen kuin ihmiset jäävät sairauslomalle. Unella ja stressillä on yhtymäkohtia, ja ne vaikuttavat toisiinsa. ’Iällä ei ole väliä’ -hankkeen Metropolian osuudessa sovelletaan jo olemassa olevaa unianalyysiteknologiaa ja kartoitetaan, voisiko tämän tyyppisen teknologian avulla saada tukea työperäisen stressin varhaiseen tunnistamiseen. Pilotissa testattiin Muratan unianalyysiteknologiaa työelämässä olevan henkilöryhmän kanssa sekä tutkittiin, miten Muratan unianturilla objektiivisesti mitattu uni yhdistyy subjektiivisesti raportoituun stressiin, unihäiriöihin ja muihin terveyteen liittyviin indikaattoreihin tässä ryhmässä. Yksinään subjektiivinen tieto tai objektiivinen, fysiologisiin oireisiin perustuva tieto ei aina riitä työperäisen stressitilan tunnistamisessa. Näitä olisi sen sijaan hyvä yhdistää, jotta arvio olisi mahdollisimman luotettava, mikä käy myös ilmi tämän pilotin alustavista tuloksista. Tässä pilotissa käytetyssä teknologiassa on potentiaalia varhaisen stressin tunnistamiseen, mutta laitteisto ei pilotissa käytetyssä muodossaan ole täysin valmis käytettäväksi esimerkiksi terveydenhuollossa. Teknologian ympärille tulisi tuotteistaa pilottia selvästi käyttäjäystävällisempi ratkaisu. Vaikka teknologia kehittyy, tuloksia on edelleen tärkeä tulkita yhdessä keskustellen

Computing in Cardiology

Measuring the arterial waveform in real-time using wearable devices mounted directly on skin holds promise in assessing cardiovascular health status and detecting an early onset of cardiovascular disease. We report the use of modern high performance MEMS pressure sensors for wearable health monitoring. The low-cost sensor elements were incorporated onto a flexible wristband for radial artery pulse measurement. These sensor elements were configured as an array and attached to a wristband. The device operation was tested on 13 healthy subjects and from each subject we successfully derived the average arterial waveform, located the diastolic and systolic peaks together with Dicrotic notch and calculated the heart rate. In the future, the MEMS pressure sensors might be employed for mobile and remote cardiovascular health monitoring.</p

CinC 2019

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.</p