Plotly-Resampler: Effective Visual Analytics for Large Time Series

Donckt, Jonas Van Der; Donckt, Jeroen Van Der; Deprost, Emiel; Hoecke, Sofie Van

doi:10.1109/vis54862.2022.00013

Cited by 15 publications

(8 citation statements)

References 19 publications

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“…Statistical testing was performed using the SciPy package (Virtanen et al., 2020 ). For exploratory data analysis of the raw wearable data, the plotly‐resampler tool was used (Van der Donckt et al., 2022a ). Via this exploratory analysis, we assessed whether the time‐ranges of the headache intervals overlapped with typical sleep periods.…”

Section: Methodsmentioning

confidence: 99%

Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks

Vandenbussche,

Van Der Donckt,

De Brouwer

et al. 2024

Brain and Behavior

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ObjectiveTo investigate the changes in activity energy expenditure (AEE) throughout daytime cluster headache (CH) attacks in patients with chronic CH and to evaluate the usefulness of actigraphy as a digital biomarker of CH attacks.BackgroundCH is a primary headache disorder characterized by attacks of severe to very severe unilateral pain (orbital, supraorbital, temporal, or in any combination of these sites), with ipsilateral cranial autonomic symptoms and/or a sense of restlessness or agitation. We hypothesized increased AEE from hyperactivity during attacks measured by actigraphy.MethodsAn observational study including patients with chronic CH was conducted. During 21 days, patients wore an actigraphy device on the nondominant wrist and recorded CH attack‐related data in a dedicated smartphone application. Accelerometer data were used for the calculation of AEE before and during daytime CH attacks that occurred in ambulatory settings, and without restrictions on acute and preventive headache treatment. We compared the activity and movements during the pre‐ictal, ictal, and postictal phases with data from wrist‐worn actigraphy with time‐concordant intervals during non‐headache periods.ResultsFour patients provided 34 attacks, of which 15 attacks met the eligibility criteria for further analysis. In contrast with the initial hypothesis of increased energy expenditure during CH attacks, a decrease in movement was observed during the pre‐ictal phase (30 min before onset to onset) and during the headache phase. A significant decrease (p < .01) in the proportion of high‐intensity movement during headache attacks, of which the majority were oxygen‐treated, was observed. This trend was less present for low‐intensity movements.ConclusionThe unexpected decrease in AEE during the pre‐ictal and headache phase of daytime CH attacks in patients with chronic CH under acute and preventive treatment in ambulatory settings has important implications for future research on wrist actigraphy in CH.

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Section: Methodsmentioning

confidence: 99%

Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks

Vandenbussche,

Van Der Donckt,

De Brouwer

et al. 2024

Brain and Behavior

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“…In patients who met the inclusion criteria and after the informed consent had been signed, we measured and recorded weight once a month during 6 months, in the morning and without clothing. At the last weight measurement, we administered the MNA-SF and classified patients into three categories: normal nutritional status (MNA-SF score of [12][13][14], risk of malnutrition (score of [8][9][10][11] or malnutrition (score of 0-7) [9]. We referred patients with a result on the MNA-SF showing a risk of malnutrition or malnutrition to their GP.…”

Section: Data Collectionmentioning

confidence: 99%

“…We categorized body mass index (BMI) according to the limit values for people over 65 of the Flemish Institute for Healthy Living [13]. In this study, we defined polypharmacy as 5 different chronic medications, without over the counter supplements [14].…”

Section: Data Analysis and Statisticsmentioning

confidence: 99%

The predictive value of weight evolution in screening for malnutrition in community-dwelling older persons (70+) in Antwerp

Offenwert

Schoenmakers

2023

BMC Prim. Care

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Background Experience and research show that screening for malnutrition in primary care mainly takes place by monitoring the weight parameter and that validated screening instruments are hardly used. In this study we examined the effectiveness and predictive value of weight evolution in screening for (risk of) malnutrition in older people living at home, in comparison with a validated screening tool, namely the Mini Nutritional Assessment Short Form (MNA-SF). Methods This project was a prospective, longitudinal study with quantitative data that took place in the province of Antwerp (Belgium) from December 2020 until June 2021. The target group of this study consisted of people over 70 living at home who were visited by a home nurse on a regular basis (at least once a month). The outcome measure was the weight evolution over six months compared with the score on the MNA-SF at month six. Weight was measured and recorded once a month during 6 months. At the last weight measurement, the MNA-SF was administered. In order to assess their own nutritional state, three additional questions were asked after taking the MNA-SF. Results A total of 143 patients gave consent to participate, of which 89 were women and 54 men. The mean age was 83.7 years (SD6.62) with a range of 70 to 100 years. Based on the MNA-SF score measured after six months, 53.1% (76/143) of participants had a normal nutritional status, 37.8% (54/143) scored risk of malnutrition and 4.9% (7/ 143) was malnourished. In order to detect people with (risk of) malnutrition, a PPV of 78.6%, a NPV of 60.7%, a sensitivity of 19.3% and a specificity of 96.0% were established with a weight evolution of ≥ 5% weight loss at six months. To detect malnutrition, our results showed respectively 33.3%, 98.4%, 71.4% and 92.3%. Conclusion In this study, weight evolution has a low sensitivity in screening for (risk of) malnutrition in people over 70 living at home compared to the MNA-SF. However, in order to detect people with malnutrition, this study demonstrated a sensitivity of 71.4% and a specificity of 92.3% for a weight loss of ≥ 5% at six months.

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“…Plotly is a free and open-source Python module that can generate several graphs, including line plots, scatter plots, bar graphs, histograms, area plots, and many more. No prior knowledge of JavaScript is necessary to utilize it alongside languages like R, Python, Java, and others [46]. Additionally, it supports dynamic charts and animations as a first principle, which is a key difference between other visualization libraries like matplotlib or seaborn [47].…”

Section: Plotlymentioning

confidence: 99%

Assessing the Performance of Python Data Visualization Libraries: A Review

Lavanya,

Gaurav,

Sindhuja

et al. 2023

IJCERT

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Data visualization tools have revolutionized processing, analysing, and communicating data. With the increasing amount of data available, it has become increasingly important to present data in an easily understandable and visually appealing way. As such, data visualization tools have become essential to data analysis and decisionmaking processes in various fields, including business, healthcare, social sciences, and engineering. This review paper aims to provide an overview of the various data visualization tools available and their features, strengths, and weaknesses. We begin by introducing the concept of data visualization and its importance in the data analysis process. We then provide a brief history of data visualization, highlighting its evolution over time from static charts to interactive and dynamic visualizations. We then discuss the available data visualization tools, including bar charts, line graphs, scatter plots, heat maps, tree maps, and network diagrams. For each type of visualization, we provide examples of when and how they can be used to present and analyse data effectively. Next, we examine the features and functionalities of popular data visualization tools, such as Tableau, Power BI, Google Data Studio, D3.js, and Python libraries like Matplotlib, Seaborn, and Plotly. We discuss the strengths and weaknesses of each tool and provide examples of realworld applications. In addition, we highlight the importance of choosing the right visualization tool based on the data type, audience, and purpose. We also discuss best practices for creating effective data visualizations, such as choosing the right colour scheme, designing for accessibility, and avoiding common pitfalls. Finally, we discuss future trends and developments in data visualization, such as using augmented and virtual reality for data visualization and incorporating machine learning and artificial intelligence in data visualization tools. In conclusion, data visualization tools have become an essential part of the data analysis process. This review paper overviews the available data visualization tools and their features, strengths, and weaknesses. By understanding the strengths and limitations of different visualization tools, researchers and analysts can effectively present and analyse data, leading to better decision-making and insights.

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Plotly-Resampler: Effective Visual Analytics for Large Time Series

Cited by 15 publications

References 19 publications

Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks

Patients with chronic cluster headache may show reduced activity energy expenditure on ambulatory wrist actigraphy recordings during daytime attacks

The predictive value of weight evolution in screening for malnutrition in community-dwelling older persons (70+) in Antwerp

Assessing the Performance of Python Data Visualization Libraries: A Review

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