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

Paalasmaa, Joonas; Toivonen, Hannu; Partinen, Markku

doi:10.1109/jbhi.2014.2314144

Cited by 112 publications

(72 citation statements)

References 26 publications

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“…The demographic of the subjects were as follows (min-max, mean, standard deviation): age (23- 16:00 pm on weekdays. Subjects were asked to relax for 2-3 minutes before the data acquisition.…”

Section: A Human Participants Protocolmentioning

confidence: 99%

Seismocardiography: Toward heart rate variability (HRV) estimation

Tadi

Lehtonen

Koivisto

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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Heart rate variability (HRV), the variation in the beat-to-beat heart rate, is a key indicator of the cardiovascular condition of an individual. The purpose of this study was to crossvalidate the beat-by-beat time variations in seismocardiography (SCG) with electrocardiography (ECG) for determining ultrashort term HRV indices. Twenty healthy young volunteers were examined in this study by performing an ultra-short term data acquisition protocol. Kubios HRV software was utilized to assess the HRV parameters. The HRV indices were analyzed in both time-domain and frequency-domain processes. High linear relationship (r>0.98) and agreement was observed between the HRV indexes calculated from SCG and ECG data. In conclusion, SCG and ECG HRV indices were found to be statistically close enough to warrant the use of SCG for estimating HRV.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6477-2/15/$31.00 ©2015 IEEE

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Section: A Human Participants Protocolmentioning

confidence: 99%

Seismocardiography: Toward heart rate variability (HRV) estimation

Tadi

Lehtonen

Koivisto

et al. 2015

2015 IEEE International Symposium on Medical Measurements and Applications (MeMeA) Proceedings

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“…We have described the sleep analysis methods in detail elsewhere [Paalasmaa et al 2011;Paalasmaa et al 2015].…”

Section: Sleep Musicalizationmentioning

confidence: 99%

“…Using signal analysis methods described elsewhere [Paalasmaa et al 2011;Paalasmaa et al 2015], we detect the heart rate, the respiration rate, and variability in both of them. Sleeper movements are detected by analyzing individual abrupt changes in the force signal.…”

Section: Sleep Analysismentioning

confidence: 99%

Data Musicalization

Tulilaulu

Nelimarkka

Paalasmaa³

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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Data musicalization is the process of automatically composing music based on given data, as an approach to perceptualizing information artistically. The aim of data musicalization is to evoke subjective experiences in relation to the information, rather than merely to convey unemotional information objectively. This paper is written as a tutorial for readers interested in data musicalization. We start by providing a systematic characterization of musicalization approaches, based on their inputs, methods and outputs. We then illustrate data musicalization techniques with examples from several applications: one that perceptualizes physical sleep data as music, several that artistically compose music inspired by the sleep data, one that musicalizes on-line chat conversations to provide a perceptualization of liveliness of a discussion, and one that uses musicalization in a game-like mobile application that allows its users to produce music. We additionally provide a number of electronic samples of music produced by the different musicalization applications.

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“…The trace of the detection function is determined by software implementing a cascade of digital filters. Band-pass filter, quadratic function, and low-pass filter in this sequence are used [26], [27].…”

Section: A Detection Functionmentioning

confidence: 99%

A Method of Detecting Heartbeat Locations in the Ballistocardiographic Signal From the Fiber-Optic Vital Signs Sensor

Krej

Dziuda

Skibniewski

2015

IEEE J. Biomed. Health Inform.

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We present a flexible, easy-to-expand digital signal processing method for detecting heart rate (HR) for cardiac vibration signals of fiber Bragg grating (FBG) sensor. The FBG-based method of measuring HR is possible to use during the magnetic resonance imaging procedure, which is its unique advantage. Our goal was to design a detection method with plurality of parameters and to subject these parameters to genetic algorithm optimization technique. In effect, we arrived at a method that is well able to deal with much distorted signals with low SNR. We proved that the method we developed allows automatic adjustment to the shape of the waves of signal carrying useful information about the moments of heartbeat. Thus, we can easily adapt our technique to the analysis of signals, which contains information on HR, from sensors employing different techniques of strain detection. The proposed method has the capabilities of analyzing signals in semi-real-time (online) with beat-to-beat resolution, significantly low delay, and negligible computational power requirements. We verified our method on recordings in a group of seven subjects. Verification included over 6000 heartbeats (82 min 47 s of recordings). The root-mean-square error of our method does not exceed 6.0 bpm.

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

Cited by 112 publications

References 26 publications

Seismocardiography: Toward heart rate variability (HRV) estimation

Seismocardiography: Toward heart rate variability (HRV) estimation

Data Musicalization

A Method of Detecting Heartbeat Locations in the Ballistocardiographic Signal From the Fiber-Optic Vital Signs Sensor

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