Bed-based instrumentation for unobtrusive sleep quality assessment in severely disabled autistic children

Carlson, Charles; Suliman, Ahmad; Prakash, Punit; Thompson, David E.; Wang, Shangxian; Natarajan, Balasubramaniam; Warren, Steve

doi:10.1109/embc.2016.7591828

Cited by 12 publications

(8 citation statements)

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“…An early description of, and motivation behind, the design of the bed system used in this study can be found in [ 24 ]. The bed system has been used to compare BCG J-peak detection algorithms [ 1 ], and it was used to investigate the relationship between sleep and daytime behavior and cognitive function in severely disabled children with autism who reside at Heartspring [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…Our team at Kansas State University has created a bed-based BCG system designed to monitor and quantify the sleep of children who reside at Heartspring, a residential and day school facility in Wichita, KS dedicated to helping children with specialized needs and severe disabilities, including children who are non-verbal and who have disorders across the autism spectrum [ 24 ]. The goal of this paper, as a follow-on to this prior work, is two-fold: (1) to outline the bed system in detail, and (2) to provide a dataset to the research community that includes ballistocardiographic signals monitored by the bed system—signals that are synchronized with more traditional heart-related signals, where these data originate from adult participants with a variety of ages and health conditions.…”

Section: Introductionmentioning

confidence: 99%

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Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters

Carlson

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Suliman

et al. 2020

Sensors

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Background: The goal of this work was to create a sharable dataset of heart-driven signals, including ballistocardiograms (BCGs) and time-aligned electrocardiograms (ECGs), photoplethysmograms (PPGs), and blood pressure waveforms. Methods: A custom, bed-based ballistocardiographic system is described in detail. Affiliated cardiopulmonary signals are acquired using a GE Datex CardioCap 5 patient monitor (which collects ECG and PPG data) and a Finapres Medical Systems Finometer PRO (which provides continuous reconstructed brachial artery pressure waveforms and derived cardiovascular parameters). Results: Data were collected from 40 participants, 4 of whom had been or were currently diagnosed with a heart condition at the time they enrolled in the study. An investigation revealed that features extracted from a BCG could be used to track changes in systolic blood pressure (Pearson correlation coefficient of 0.54 +/− 0.15), dP/dtmax (Pearson correlation coefficient of 0.51 +/− 0.18), and stroke volume (Pearson correlation coefficient of 0.54 +/− 0.17). Conclusion: A collection of synchronized, heart-driven signals, including BCGs, ECGs, PPGs, and blood pressure waveforms, was acquired and made publicly available. An initial study indicated that bed-based ballistocardiography can be used to track beat-to-beat changes in systolic blood pressure and stroke volume. Significance: To the best of the authors’ knowledge, no other database that includes time-aligned ECG, PPG, BCG, and continuous blood pressure data is available to the public. This dataset could be used by other researchers for algorithm testing and development in this fast-growing field of health assessment, without requiring these individuals to invest considerable time and resources into hardware development and data collection.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters

Carlson

Turpin

Suliman

et al. 2020

Sensors

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“…BCGs were recorded using four Measurement Specialties FX1902 load cells positioned beneath the corner bedposts. The signal conditioning circuitry employed bandpass filters with corner frequencies of 0.05 and 35 Hz-these circuits are further described in [34]. ECGs were simultaneously recorded using a GE Datex-Ohmeda Cardiocap TM /5 patient monitor.…”

Section: A Data Recordingmentioning

confidence: 99%

Performance Comparison for Ballistocardiogram Peak Detection Methods

et al. 2019

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A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants. INDEX TERMS Ballistocardiogram, heartbeat, heartbeat interval, heart rate, heart rate variability, load cells, statistical signal analysis, wavelets.

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“…The most popular method for measuring the ballistocardiogram is by using a force plate or weighing scale and having the subject stand as still as they can to reduce motion artifacts. Other popular modalities include seat or chair-based systems [30], bed-based systems [24], [26], [31]- [35], and wearable systems [36], [37]. The bed-based modality makes the most sense for long-term monitoring and sleep quality assessment of severely disabled children.…”

Section: A Bcg Acquisition Systemmentioning

confidence: 99%

“…The virtual instrument does not apply any additional filtering before saving the data to a file on the PC. More details on the bed-system architecture under development at Kansas State University including a preliminary comparison of estimated BCG HBIs compared to the gold standard ECG R-R intervals can be found in [31] and [38]. This work focuses on utilizing the signals measured from the EMFi sensors to estimate the cardiac B-B intervals from a reconstructed BCG signal.…”

Section: A Bcg Acquisition Systemmentioning

confidence: 99%

Motion Artifact Detection and Reduction in Bed-Based Ballistocardiogram

et al. 2019

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Non-intrusive sleep monitoring is critical for certain populations such as severely disabled autistic children. Nocturnal disturbance analysis is an important diagnostic tool for assessing sleep issues. The objective of this paper is to detect and minimize the effects of motion artifacts in signals recorded via an unobtrusive electromechanical film-based ballistocardiogram (BCG) sensor integrated into a smart bed system. The goal is to have a reliable estimation of beat-to-beat (B-B) interval. The proposed algorithm includes two main stages: a motion detection algorithm followed by a motion artifact removal system. Motion detection involves a sequential detection algorithm in which successive data frames are compared to two thresholds: upper and lower thresholds. Each motion corrupted frame can then be reconstructed by an approach that relies on a parametric model of the BCG signal. Exploiting the fact that the underlying BCG parameters (J-peak-to-J-peak interval, J-peak-to-K-peak amplitude, and the most significant frequency component) change slowly and are correlated across time; an autoregressive model-based tracking and Wiener smoother based parameter estimation strategies are proposed. The experimental results are presented to demonstrate the effectiveness of the proposed motion artifact detection and removal algorithms. The probability of detection of 96% and high average coverage of 84% with over 90% precision for the estimated B-B intervals are achieved on recordings for 19 h of sleep from three subjects. Our novel motion detection and removal methods demonstrate the feasibility of using bed-based BCG signals for providing a reliable unobtrusive way to estimate the B-B interval in the presence of motion.INDEX TERMS Autism, autoregressive model, ballistocardiogram, motion artifact detection and removal, Wiener smoothing.

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Bed-based instrumentation for unobtrusive sleep quality assessment in severely disabled autistic children

Cited by 12 publications

References 12 publications

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters

Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters

Performance Comparison for Ballistocardiogram Peak Detection Methods

Motion Artifact Detection and Reduction in Bed-Based Ballistocardiogram

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