On the Performance of Bed-Integrated Ballistocardiography in Long-Term Heart Rate Monitoring of Vascular Patients

On the Performance of Bed-Integrated Ballistocardiography in Long-Term Heart Rate Monitoring of Vascular Patients Christoph Hoog Antink1, Yen Mai2, Jukka Ranta3, Adrian Tarniceriu4, Christoph Bruser¨ 1, Steffen Leonhardt1, Niku Oksala2;5, Antti Vehkaoja2 1Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany 2Faculty of Medicine and Health Technology, Tampere University, Finland 3Emfit Oy, Vaajakoski, Finland, 4PulseOn SA, Neuchatel, Switzerland 5Vascular and Interventional Radiology Centre, Tampere University Hospital, Tampere, Finland Abstract sparse or unsystematic, and is mainly performed visually or with intermittent measurements. Continuous monitor- The application of ballistocardiography (BCG) for un- ing of hospital patients would be beneficial in the detection obtrusive monitor of (beat-to-beat) heart rate in sleep- of deterioration of a patient’s condition, which, if achieved ing subjects has seen increased attention in recent years. early, could enable preventive measures. While most studies are performed on healthy volunteers, The BCG technique has been proven adequately accu- some studies have included subjects with sleep-related rate for heart rate variability measurement with healthy problems. Even so, little attention has been payed to pa- subjects and bed-integrated [1–3] as well as wearable seis- tients suffering from cardiovascular diseases. In addition, mocardiography (SCG) sensors [4]. Bruser¨ et al. achieved most studies are either limited to short laboratory mea- 0.61 % relative error E¯rel with 85 % coverage in beat-to- surements or overnight recordings of sleeping subjects. beat interval (BBI) estimation with eight healthy subjects. In this work, we present preliminary results on beat- Corresponding numbers in the study by Kortelainen et al. to-beat interval estimation of long-term (> 21 h) BCG were 0.4 % and 88 % with six subjects. The same studies recordings in a hospital environment. Measurements also included groups of patients suffering from insomnia or were obtained from five subjects after peripheral vascu- other sleep related disorders. In these groups, the perfor- lar surgery or endovascular interventions and compared mance of BCG monitoring was decreased to 1.8 % relative to Holter recordings. The commercially available sensor error and 80 % coverage with 28 subjects [2] and 1.61 % Emfit QS as well an augmented version of the CLIE inter- relative error and 69 % coverage with 25 subjects [1]. For val estimation algorithm were used. Using the proposed a detailed review of BCG and SCG applications as well as approach, an average relative beat-to-beat interval error differences between the methods, we refer to [5]. of 3.8 % at a coverage of 61.5 % was achieved. Error and In high-risk cardiovascular patients, the technology may coverage were found to vary from subject to subject as face further reduction in accuracy, as the BCG signal com- well as time of day. On average, findings are comparable plex induced by a heartbeat is often weaker and less con- though inferior to results reported on insomniac subjects sistent in shape. Kaisti et al. [6] reported over ten-fold in- in a sleep laboratory environment. crease in RMS error from 5.6 ms to 60.3 ms in study groups consisting of 29 healthy subjects and 12 coronary artery 1. Introduction disease patients with a wearable seismocardiographic measurement method using a 6 degree of freedom motion mea- Unobtrusive night-time monitoring with bed-integrated surement unit. The BBI signal coverage was 93.4 % and ballistocardiographic (BCG) sensors has gained interest in 92.6 % for the two groups, respectively. wellness domain as it enables easy-to-use self-monitoring In this work, we studied the performance of BBI esti- of sleep. So far, the use of BCG monitoring in clinical mation from BCG signals recorded with the commercially applications has been limited. A potential clinical applica- available Emfit QS monitoring system (Emfit Oy, Finland) tion is unobtrusive monitoring of patients who are staying in five patients who underwent endovascular interventions in beds in the regular hospital ward. or open vascular surgical procedures. Typically, these pa- Currently, monitoring of such patients is relatively tients have several comorbidities (such as diabetes, hyper- Computing in Cardiology 2019; Vol 46 Page 1 ISSN: 2325-887X DOI: 10.22489/CinC.2019.167 tension, dyslipidemia, coronary artery disease, cerebrovas- as valid intervals, increasing accuracy while reducing tem- cular disease) and may have reduced cardiac function. For poral coverage. However, in the present full-day data of all patients, long-term recordings (> 21 h) including day- partially awake, non-healthy subjects, significant outliers and nighttime were acquired. For BBI estimation, the pre- were found and the algorithm was modified to increase ro- viously developed CLIE algorithm [1] was used and aug- bustness at the cost of generality. mented with an iterative estimation approach. To remove outliers in the beat-to-beat interval estimates, a straight-forward thresholding algorithm was applied. If 2. Data beats exhibited a relative deviation from the median estimation bigger than ∆max, the detected interval was con- The data of five post-operative vascular patients was se- sidered an outlier and removed from analysis. lected for the analysis. The patient recordings were made As show in previous work, the inclusion of an adaptive in the vascular surgery ward of the Tampere University prior could improve beat-to-beat estimation in low-quality Hospital. The subjects were monitored for approximately signals [8]. Thus, a similar approach was applied. In this 24 hours with an EMFit QS bed sensor. A five-lead Holter work, an iterative estimation approach was developed: In monitor, Faros 360 manufactured by Bittium Biosignals, the first pass, CLIE was used to estimated beat-to-beat in- was used to record the reference ECG signal. The bed sen- tervals without prior assumption about the heart rate. Next, sor was placed between the hospital bed mattress and the the detected intervals ηopt;i were median filtered with a fil- bed frame. The sampling rate of the BCG signal obtained ter of width nmedian, with the Emfit QS system was 100 Hz and the signal was bandlimited from 1 to 5 Hz. Ambu Blue sensor L-00-S ηôpt;i = medianfilter (ηopt;i; nmedian) (3) electrodes were used for the ECG recording. A sampling rate of 1 kHz was used with ECG. In the second pass, an adaptive prior was used in the interval estimation, The average age of the subjects was 65.0 years. Four of the subjects were male. The subjects had undergone jη−ηôpt;ij 1 − S [η; k; η^ ] = e k ; (4) different vascular and endovascular procedures: one lower prior opt;i 2k limb endografting, one abdominal aortic endografting, two ∗ S [i; η; k; ηôpt;i] =Sf [i; η] · Sprior[η; k; ηôpt;i]: (5) carotid endarterectomies, and one femoropopliteal bypass f surgery. A favorable statement was obtained from the Re- Based on previous observations [9], a Laplacian distribu- gional Ethical Committee of Pirkanmaa Hospital District tion with variable scale k was chosen. (R17027) for the study. 4. Results and Discussion 3. Algorithm Without post processing, a mean absolute estimation The Continuous Local Interval Estimator approach error over all patients of 46.36 ms at a temporal cover- used in this paper is described in detail in [1]. In age of 47.87 % was observed for a fixed quality thresh- short, self-similarity of consecutive heartbeats is as- old qth = 0:4 as used in previous studies. Thus, iterative sessed using lag-adaptive versions of the short-term au- estimation and outlier detection as described above were tocorrelation SLASTA[i; η], the maximum amplitude pairs applied. The concept is visualized for an excerpt of one SMAP[i; η] and the average magnitude difference function recording in Figure 1. SAMDF[i; η]. For each window i of the BCG-signal, all As can be seen from Figure 1, using the adaptive prior functions give a probability that a candidate shift η of the significantly reduces outliers so that only few intervals are signal corresponds to the actual BBI, ηopt;i. All three func- excluded in the thresholding process. Nevertheless, de- tions are fused using a Bayesian approach, tailed analysis shows that some ground truth intervals, pre- sumably from areas of non-sinus rhythm are excluded in Sf [i; η] =SLASTA[i; η] · SMAP[i; η] · SAMDF[i; η]; (1) the thresholding process. ηopt;i = arg max [Sf [i; η]] : (2) Figure 2 presents an analysis of the algorithms sensi- η tivity towards a variation of the outlier threshold ∆max and the scale of the Laplacian prior k for a fixed quality Thus, the CLIE algorithm makes no prior assumptions threshold. As expected, decreasing ∆ decreases both about BBIs and was proven to be capable of process- max coverage and mean absolute error. A similar behaviour is ing even severely arrhythmic data [7]. The only tunable observed for k: The smaller k, the higher both error and parameter of the original CLIE is the threshold of self- coverage. This can be explained by the fact that a small similarity, q . Only candidate intervals with a enough th scale of the prior distribution leads the algorithm, given a P self-similarity Sf [ηopt;i]= η Sf [η] > qth are accepted fixed qth, to be more confident about any detected interval. Page 2 1500 The number of beats used to calculate the median interval nmedian was varied from 51 to 301 and was found to have 1000 little influence on coverage and error. The patient data was processed using the parameter set marked in Figure 2 (∆ = 10%, k = 0:18). Table 1 500 max shows the results for each subject in terms of coverage and 4 6 8 10 12 error as well as the usable duration, i.e.

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