Motion Detection in Bed-Based Ballistocardiogram to Quantify Sleep Quality

Alivar, Alaleh; Carlson, Charles; Suliman, Ahmad; Warren, Steve; Prakash, Punit; Thompson, D. J.; Natarajan, Balasubramaniam

doi:10.1109/glocom.2017.8255014

Cited by 10 publications

(8 citation statements)

References 20 publications

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“…The bed sensor is based on the ballistocardiographic technology. In the literature, ballistocardiographic sensors are compared with the gold standard PSG in the measurement of REM, non-REM, and AWAKE phases with a total accuracy of 76.81% ± 7.51% using a time-variant autoregressive model and quadratic classifier to extract features [9] and movements with 95% and 96% probability of detection and 94% and 95.2% accuracy in sleep and restlessness state identification, respectively [10].…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, the PIR sensor is perfectly fitted for sleep monitoring of a user at home, in hospital, or in a nursing home, in respect to the important privacy issue aspects [8]. The correlation between the PIR sensor and the reference ballistocardiographic bed sensor, in the monitoring of sleep phases, was computed aware that bed sensors have less accuracy in relation to the gold standard techniques, as reported in the literature [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Casaccia

Braccili

Scalise

et al. 2019

Sensors

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A simple sleep monitoring measurement method is presented in this paper, based on a simple, non-invasive motion sensor, the Passive InfraRed (PIR) motion sensor. The easy measurement set-up proposed is presented and its performances are compared with the ones provided by a commercial, ballistocardiographic bed sensor, used as reference tool. Testing was conducted on 25 nocturnal acquisitions with a voluntary, healthy subject, using the PIR-based proposed method and the reference sensor, simultaneously. A dedicated algorithm was developed to correlate the bed sensor outputs with the PIR signal to extract sleep-related features: sleep latency (SL), sleep interruptions (INT), and time to wake (TTW). Such sleep parameters were automatically identified by the algorithm, and then correlated to the ones computed by the reference bed sensor. The identification of these sleep parameters allowed the computation of an important, global sleep quality parameter: the sleep efficiency (SE). It was calculated for each nocturnal acquisition and then correlated to the SE values provided by the reference sensor. Results show the correlation between the SE values monitored with the PIR and the bed sensor with a robust statistic confidence of 4.7% for the measurement of SE (coverage parameter k = 2), indicating the validity of the proposed, unobstructive approach, based on a simple, small, and low-cost sensor, for the assessment of important sleep-related parameters.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Casaccia

Braccili

Scalise

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Alivar et al [73] proposed the use of several electromechanical film sensors to assess the periodic movements during sleep and, therefore, estimate the quality of sleep. Highest accuracy in the detection of motion artifacts (95%) was achieved using a sequential detection rule formulation to determine if a sample has motion artifacts by computing the log-likelihood ratio of the binary hypothesis (sample with movement artifacts or sample without artifacts) and compare the results with a threshold to classify the sample.…”

Section: E Non Sleep Structure Featuresmentioning

confidence: 99%

A Review of Approaches for Sleep Quality Analysis

Mendonça¹,

Mostafa²,

Morgado‐Dias³

et al. 2019

IEEE Access

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Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term ''sleep quality'' still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject's home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, ''what methods for sleep quality assessment have been developed'' and ''what kind of measures are employed by the devices that have been developed to estimate sleep quality.'' The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art. INDEX TERMS Sleep quality analysis, monitoring devices, home detection.

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“…The BCG signals are first analyzed and motion corrupted frames are identified. Motion detection is based on a sequential detection algorithm where a sequential hypothesis test procedure is repeated until a decision is reached [27]. Using practical test data, we characterize the performance (probability of false alarm: P FA , probability of detection: P D ) of our motion artifact detection method.…”

Section: B Contributionsmentioning

confidence: 99%

Motion Artifact Detection and Reduction in Bed-Based Ballistocardiogram

et al. 2019

Self Cite

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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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Motion Detection in Bed-Based Ballistocardiogram to Quantify Sleep Quality

Cited by 10 publications

References 20 publications

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

Experimental Assessment of Sleep-Related Parameters by Passive Infrared Sensors: Measurement Setup, Feature Extraction, and Uncertainty Analysis

A Review of Approaches for Sleep Quality Analysis

Motion Artifact Detection and Reduction in Bed-Based Ballistocardiogram

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