A respiratory latent variable model for mechanically measured heartbeats

Paalasmaa, Joonas

doi:10.1088/0967-3334/31/10/003

Cited by 30 publications

(19 citation statements)

References 24 publications

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“…We model the BCG signal as a summation (essentially concatenation) of heartbeat shape vectors [5], [6], [15]. In other words, the signal consists of superimposed shapes of the individual heartbeats ( Fig.…”

Section: B Generative Model For a Bcg Signalmentioning

confidence: 99%

“…where vector µ is a heartbeat shape template (see below) and z i is the amplitude (a scalar) of the ith heartbeat [6]. In our implementation of the algorithm, the length of the heartbeat shape vector µ is fixed to 2 seconds.…”

Section: Respiratory Variation Of Heartbeat Shapesmentioning

confidence: 99%

“…For example, the shape is different depending on whether the person is lying prone or supine. Second, there is short-term variability in the heartbeat shape that is caused by the mixing of adjacent heartbeats [5] and by respiration [6].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Paalasmaa

Toivonen

Partinen

2015

IEEE J. Biomed. Health Inform.

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112

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We present a method for measuring beat-to-beat heart rate from ballistocardiograms acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from the signal using hierarchical clustering. Then, beat-to-beat intervals are detected by finding positions where the heartbeat shape best fits the signal. The method was validated with overnight recordings from 46 subjects in varying setups (sleep clinic, home, single bed, double bed, two sensor types). The mean beat-to-beat interval error was 13 ms and on an average 54% of the beat-to-beat intervals were detected. The method is part of a home-use e-health system for an unobtrusive sleep measurement.

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Section: B Generative Model For a Bcg Signalmentioning

confidence: 99%

Section: Respiratory Variation Of Heartbeat Shapesmentioning

confidence: 99%

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Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Paalasmaa

Toivonen

Partinen

2015

IEEE J. Biomed. Health Inform.

Self Cite

112

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“…where µ represents the mean heartbeat shape, w is the direction of variation (a vector), and z i is the magnitude of variation (a scalar) [20]. The variation of the heartbeat shape is caused mainly by respiration.…”

Section: B Signal Analysismentioning

confidence: 99%

Unobtrusive online monitoring of sleep at home

Paalasmaa

Waris

Toivonen

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-We describe an online sleep monitoring service, based on unobtrusive ballistocardiography (BCG) measurement in an ordinary bed. The novelty of the system is that the sleep tracking web application is based on measurements from a fully unobtrusive sensor. The BCG signal is measured with a piezoelectric film sensor under the mattress topper, and sent to the web server for analysis. Heart rate and respiratory variation, activity, sleep stages, and stress reactions are inferred based on the signal. The sleep information is presented to the user along with measurements of the sleeping environment (temperature, noise, luminosity) and user-logged tags (e.g. stress, alcohol, exercise). The approach is designed for longterm use at home, allowing users to follow the development of their sleep over months and years. The service has also a medical use, as sleep disorder patients can be measured for long periods before and after interventions.

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“…A variety of sensors and methods rely on mechanical (ballistocardiography-BCG) or audible cardiac activity (phonocardiography-PCG). Brief overviews of such sensors and methodologies are given in [2]- [6].…”

mentioning

confidence: 99%

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

Šprager

Zazula

2014

IEEE J. Biomed. Health Inform.

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This paper deals with an optimized heartbeat detection multimethod by using maximum a posteriori probability (MAP). The approach was derived for unobtrusive fiber-optic measurements of cardiac activity. Multiple independent detection methods were selected and characterized by their delays and variability referring to cardiac electrical excitation (R waves). Validation of the approach was performed in two experiments involving 24 participants: 10 interferometric signals were recorded at rest, 14 with variable heart rate after physical exercise. The proposed MAP heartbeat detection was assessed by a cross validation in 250 iterations. Obtained results show the overall efficiency, which was estimated by a product of the sensitivity, precision, and variability of heartbeat detections, yields 97.04 ± 3.36% for the experiment with physical exercise and 97.07 ± 4.49% at rest. The method's accuracy guarantees that the heartbeat detections differ for 22 ± 5 ms and 22 ± 3 ms from the ECG reference in the two types of experiments, respectively.

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A respiratory latent variable model for mechanically measured heartbeats

Cited by 30 publications

References 24 publications

Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms

Unobtrusive online monitoring of sleep at home

Optimization of Heartbeat Detection in Fiber-Optic Unobtrusive Measurements by Using Maximum A Posteriori Probability Estimation

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