Visualizing physiological signals in real-time

Sepúlveda, Sebastián; Reyes, Pablo; Weinstein, Alejandro

doi:10.25080/majora-7b98e3ed-01c

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…There are only a very few software tools that allow a multimodal visualization, such as RTGraph ( 24 ) (physiological signals), CAPTIV ( 25 ) (video, motion curve, audio, and hypnogram) or Natus NeuroWorks (EEG, video) ( 26 ). Nevertheless, they do not integrate an annotation tool.…”

Section: Methodsmentioning

confidence: 99%

“…The drawback is that they are designed to visualize only audio-visual recordings, so we cannot combine it with the visualization of physiological signals. Regarding software distribution, RTGraph ( 24 ) is the only one being open source. Open source distribution has many advantages such as: being available to everyone, not depending on a third party software requiring a license and allowing collaborative work for improving the software and adding features.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Cailleau¹,

Weber

Cabon

et al. 2020

Front. Pediatr.

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Background: Sleep is an important determinant of brain development in preterm infants. Its temporal organization varies with gestational age (GA) and post-menstrual age (PMA) but little is known about how sleep develops in very preterm infants. The objective was to study the correlation between the temporal organization of quiet sleep (QS) and maturation in premature infants without severe complications during their neonatal hospitalization. Methods: Percentage of time spent in QS and average duration of time intervals (ADI) spent in QS were analyzed from a cohort of newborns with no severe complications included in the Digi-NewB prospective, multicentric, observational study in 2017–19. Three groups were analyzed according to GA: Group 1 (27–30 weeks), Group 2 (33–37 weeks), Group 3 (>39 weeks). Two 8-h video recordings were acquired in groups 1 and 2: after birth (T1) and before discharge from hospital (T2). The annotation of the QS phases was performed by analyzing video recordings together with heart rate and respiratory traces thanks to a dedicated software tool of visualization and annotation of multimodal long-time recordings, with a double expert reading. Results are expressed as median (interquartile range, IQR). Correlations were analyzed using a linear mixed model. Results: Five newborns were studied in each group (160 h of recording). Median time spent in QS increased from 13.0% [IQR: 13–20] to 28.8% [IQR: 27–30] and from 17.0% [IQR: 15–21] to 29.6% [IQR: 29.5–31.5] in Group 1 and 2, respectively. Median ADI increased from 54 [IQR: 53–54] to 288 s [IQR: 279–428] and from 90 [IQR: 84–96] to 258 s [IQR: 168–312] in Group 1 and 2. Both groups reach values similar to that of group 3, respectively 28.2% [IQR: 24.5–31.3] and 270 s [IQR: 210–402]. The correlation between PMA and time spent in QS or ADI were, respectively 0.73 ( p < 10 −4 ) and 0.46 ( p = 0.06). Multilinear analysis using temporal organization of QS gave an accurate estimate of PMA ( r 2 = 0.87, p < 0.001). Conclusion: The temporal organization of QS is correlated with PMA in newborns without severe complication. An automated standardized continuous behavioral quantification of QS could be interesting to monitor during the hospitalization stay in neonatal units.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Cailleau¹,

Weber

Cabon

et al. 2020

Front. Pediatr.

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“…There are also many data acquisition software applications developed using different platforms to acquire data from Arduino. Instrumentino (Koenka et al, 2014) and RTGraph (Sepúlveda et al, 2015) are python-based applications. ArduinoScope (Ishikawa & Maruta, 2010) is a processing based tool.…”

Section: Related Workmentioning

confidence: 99%

MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments

Chand

Khosla

2022

Journal of Information Technology Research

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AfDaq is an open-source, plug and play, MATLAB based tool that offers the capabilities of multi-channel real-time data acquisition, visualization, manipulation, and local saving of data for offline analysis. The MATLAB Arduino package suffers from serious timing jitter during real-time data acquisition. This timing jitter associated with four main commands (Analog Read, Digital Read, Digital Write and PWM Set) available in MATLAB Arduino package is statistically analyzed and a simple post-hoc timing jitter correction mechanism is proposed to acquire data points with high timing accuracy. The benchmark of the final program is conducted at various sampling rates for multichannel acquisition with 10 Hz comes as the maximum sampling rate for 5 channel recording. In the end, a use case of the developed tool for physiological data acquisition in multimodal biofeedback is presented. The software tool, data, and analysis scripts that support the findings of this study are released as an open-source project to support the replicability and reproducibility of the research.

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“…Its community maintains a lot of official packages, including libraries (e.g., Numpy, Scipy, Matplotlib) providing scientific computing tools that are comparable to those equipping dedicated software, such as Matlab and R. Thanks to a fast learning curve, its rapid prototyping features and intuitive readability of the code, the Python community now also includes users paying particular attention to the interaction aspects of their software, such as academics [5,6] employing Python as a teaching-by-examples tool [7]. Despite this quest for interactivity, the use of Python in real-time applications is testified by exceptions: RTGraph, for instance, instantaneously processes physiological signals and then displays the results through the Qt framework [8].…”

Section: Introductionmentioning

confidence: 99%

Programming Real-Time Sound in Python

Pra

Fontana

2020

Applied Sciences

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For its versatility, Python has become one of the most popular programming languages. In spite of its possibility to straightforwardly link native code with powerful libraries for scientific computing, the use of Python for real-time sound applications development is often neglected in favor of alternative programming languages, which are tailored to the digital music domain. This article introduces Python as a real-time software programming tool to interested readers, including Python developers who are new to the real time or, conversely, sound programmers who have not yet taken this language into consideration. Cython and Numba are proposed as libraries supporting agile development of efficient software running at machine level. Moreover, it is shown that refactoring few critical parts of the program under these libraries can dramatically improve the performances of a sound algorithm. Such improvements can be directly benchmarked within Python, thanks to the existence of appropriate code parsing resources. After introducing a simple sound processing example, two algorithms that are known from the literature are coded to show how Python can be effectively employed to program sound software. Finally, issues of efficiency are mainly discussed in terms of latency of the resulting applications. Overall, such issues suggest that the use of real-time Python should be limited to the prototyping phase, where the benefits of language flexibility prevail on low latency requirements, for instance, needed during computer music live performances.

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Visualizing physiological signals in real-time

Cited by 6 publications

References 4 publications

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

Quiet Sleep Organization of Very Preterm Infants Is Correlated With Postnatal Maturation

MATLAB-Based Real-Time Data Acquisition Tool for Multimodal Biofeedback and Arduino-Based Instruments

Programming Real-Time Sound in Python

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