tsflex: Flexible time series processing &amp; feature extraction

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

doi:10.1016/j.softx.2021.100971

Cited by 12 publications

(8 citation statements)

References 12 publications

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“…This processing step was performed prior to computing the interval data ratios and the AI ABS . In order to efficiently compute the AI ABS , NumPy functions were leveraged through the tsflex library (Harris et al., 2020 ; Van der Donckt et al., 2022b ).…”

Section: Methodsmentioning

confidence: 99%

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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%

“…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).…”

Section: Analysis and Statisticsmentioning

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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“…We utilized Braindecode and MNE [ 42 , 54 ] for FT Surrogate augmentation, which is a vital step in augmenting our dataset. Feature extraction, a critical pre-processing step in machine learning, was performed using tsflex [ 55 ], scipy [ 56 ], and scikit-learn [ 57 ]. These libraries provide a comprehensive set of methods for extracting meaningful features from our time-series data.…”

Section: Methodsmentioning

confidence: 99%

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Al-Hussaini

Mitchell

2023

Bioengineering

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This work presents SeizFt—a novel seizure detection framework that utilizes machine learning to automatically detect seizures using wearable SensorDot EEG data. Inspired by interpretable sleep staging, our novel approach employs a unique combination of data augmentation, meaningful feature extraction, and an ensemble of decision trees to improve resilience to variations in EEG and to increase the capacity to generalize to unseen data. Fourier Transform (FT) Surrogates were utilized to increase sample size and improve the class balance between labeled non-seizure and seizure epochs. To enhance model stability and accuracy, SeizFt utilizes an ensemble of decision trees through the CatBoost classifier to classify each second of EEG recording as seizure or non-seizure. The SeizIt1 dataset was used for training, and the SeizIt2 dataset for validation and testing. Model performance for seizure detection was evaluated using two primary metrics: sensitivity using the any-overlap method (OVLP) and False Alarm (FA) rate using epoch-based scoring (EPOCH). Notably, SeizFt placed first among an array of state-of-the-art seizure detection algorithms as part of the Seizure Detection Grand Challenge at the 2023 International Conference on Acoustics, Speech, and Signal Processing (ICASSP). SeizFt outperformed state-of-the-art black-box models in accurate seizure detection and minimized false alarms, obtaining a total score of 40.15, combining OVLP and EPOCH across two tasks and representing an improvement of ~30% from the next best approach. The interpretability of SeizFt is a key advantage, as it fosters trust and accountability among healthcare professionals. The most predictive seizure detection features extracted from SeizFt were: delta wave, interquartile range, standard deviation, total absolute power, theta wave, the ratio of delta to theta, binned entropy, Hjorth complexity, delta + theta, and Higuchi fractal dimension. In conclusion, the successful application of SeizFt to wearable SensorDot data suggests its potential for real-time, continuous monitoring to improve personalized medicine for epilepsy.

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“…We calculate a set of 131 features per window, these features are multi-domain (extracted from time and frequency domain) and multi-resolution (calculated on multiple window sizes). We utilize tsflex to realize this stridedwindow feature extraction [58]. Table 1 lists the feature functions that are applied to the data.…”

Section: Feature Extractionmentioning

confidence: 99%