{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:21:59Z","timestamp":1763749319712,"version":"3.41.2"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T00:00:00Z","timestamp":1727395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The objective of this study is to present the automated pipeline developed to process signals and extract features from the Garmin Vivoactive 4 smartwatch, which has been chosen as the primary wearable device in the BRAINTEASER project. The proposed pipeline includes a signal processing step, which applies retiming, gap-filling, and denoising algorithms to enhance the quality of the data. The feature extraction step, on the other hand, utilizes clinical partners' knowledge and feedback to select the most relevant variables for analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Developed in Python, this pipeline can be used by researchers for automated signal processing and feature extraction from wearable devices. It can also be easily adapted or modified to suit the specific requirements of different scenarios.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2024.1402943","type":"journal-article","created":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T05:10:29Z","timestamp":1727413829000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated pipeline for denoising, missing data processing, and feature extraction for signals acquired via wearable devices in multiple sclerosis and amyotrophic lateral sclerosis applications"],"prefix":"10.3389","volume":"6","author":[{"given":"Luca","family":"Cossu","sequence":"first","affiliation":[]},{"given":"Giacomo","family":"Cappon","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Facchinetti","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,9,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.2174\/1568026620666200924114827","article-title":"Exploring Multiple Sclerosis (MS) and Amyotrophic Lateral Scler osis (ALS) as neurodegenerative diseases and their treatments: a review study","volume":"20","author":"Deeb","year":"2020","journal-title":"Curr Top Med Chem"},{"key":"B2","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1111\/biom.12002","article-title":"Estimating time to disease progression comparing transition models and survival methods\u2014an analysis of multiple sclerosis data","volume":"69","author":"Mandel","year":"2013","journal-title":"Biometrics"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1136\/jnnp-2015-312908","article-title":"A clinical tool for predicting survival in ALS","volume":"87","author":"Knibb","year":"2016","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.3390\/jpm11040280","article-title":"Multi-layer picture of neurodegenerative diseases: lessons from the use of big data through artificial intelligence","volume":"11","author":"Termine","year":"2021","journal-title":"J Pers Med"},{"key":"B5","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/EMBC.2015.7318499","article-title":"Preliminary analysis of the use of smartwatches for longitudinal health monitoring","volume-title":"2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Jovanov","year":"2015"},{"key":"B6","doi-asserted-by":"crossref","first-page":"3517","DOI":"10.1109\/IEMBS.2006.260547","article-title":"Signal processing and feature extraction for sleep evaluation in wearable devices","volume-title":"2006 International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Bianchi","year":"2006"},{"key":"B7","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/EUSIPCO.2015.7362418","article-title":"Opportunities and challenges for ultra low power signal processing in wearable healthcare","volume-title":"2015 23rd European Signal Processing Conference (EUSIPCO)","author":"Casson","year":"2015"},{"key":"B8","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s44247-024-00062-3","article-title":"NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour","volume":"2","author":"Beyer","year":"2024","journal-title":"BMC Digit Health"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2021.769823","article-title":"Reproducible analysis pipeline for data streams: open-source software to process data collected with mobile devices","volume":"3","author":"Vega","year":"2021","journal-title":"Front Digit Health"},{"key":"B10","doi-asserted-by":"publisher","first-page":"e19","DOI":"10.1017\/cts.2020.511","article-title":"The digital biomarker discovery pipeline: an open-source software platform for the development of digital biomarkers using mHealth and wearables data","volume":"5","author":"Bent","year":"2021","journal-title":"J Clin Transl Sci"},{"key":"B11","doi-asserted-by":"publisher","first-page":"106461","DOI":"10.1016\/j.cmpb.2021.106461","article-title":"FLIRT: a feature generation toolkit for wearable data","volume":"212","author":"F\u00f6ll","year":"2021","journal-title":"Comput Methods Programs Biomed"},{"key":"B12","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.msard.2018.06.012","article-title":"Heart rate variability analysis in patients with multiple sclerosis","volume":"24","author":"Damla","year":"2018","journal-title":"Mult Scler Relat Disord"},{"key":"B13","doi-asserted-by":"publisher","first-page":"363","DOI":"10.3109\/17482968.2011.584628","article-title":"Autonomic dysfunction in the early stage of ALS with bulbar involvement","volume":"12","author":"Merico","year":"2011","journal-title":"Amyotroph Lateral Scler"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3390\/jcm9020335","article-title":"Cardiac autonomic dysfunction in multiple sclerosis: a systematic review of current knowledge and impact of immunotherapies","volume":"9","author":"Findling","year":"2020","journal-title":"J Clin Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/1472-684X-11-26","article-title":"Using respiratory rate and thoracic movement to assess respiratory insufficiency in amyotrophic lateral sclerosis: a preliminary study","volume":"11","author":"Siirala","year":"2012","journal-title":"BMC Palliat Care"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2019.00109","article-title":"SVC is a marker of respiratory decline function, similar to FVC, in patients with ALS","volume":"10","author":"Pinto","year":"2019","journal-title":"Front Neurol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1016\/j.rmed.2015.01.018","article-title":"Respiratory dysfunction in multiple sclerosis","volume":"109","author":"Tzelepis","year":"2015","journal-title":"Respir Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1080\/17482960802382305","article-title":"Percutaneous nocturnal oximetry in amyotrophic lateral sclerosis: periodic desaturation","volume":"10","author":"Carvalho","year":"2009","journal-title":"Amyotroph Lateral Scler"},{"key":"B19","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1136\/jnnp-2017-316515","article-title":"Prevalence of sleep apnoea and capnographic detection of nocturnal hypoventilation in amyotrophic lateral sclerosis","volume":"89","author":"Boentert","year":"2018","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"B20","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1191\/135248506ms1320oa","article-title":"Sleep and fatigue in multiple sclerosis","volume":"12","author":"Stanton","year":"2006","journal-title":"Mult Scler"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-024-01025-8","article-title":"Modeling multiple sclerosis using mobile and wearable sensor data","volume":"7","author":"Gashi","year":"2024","journal-title":"npj Digit Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"e13433","DOI":"10.2196\/13433","article-title":"Objectively monitoring amyotrophic lateral sclerosis patient symptoms during clinical trials with sensors: observational study","volume":"7","author":"Garcia-Gancedo","year":"2019","journal-title":"JMIR Mhealth Uhealth"},{"key":"B23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41746-023-00778-y","article-title":"Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures","volume":"6","author":"Johnson","year":"2023","journal-title":"NPJ Digit Med"},{"key":"B24","doi-asserted-by":"publisher","first-page":"63","DOI":"10.2170\/physiolsci.RP005506","article-title":"Correlations between the poincar\u00e8; plot and conventional heart rate variability parameters assessed during paced breathing","volume":"57","author":"Guzik","year":"2007","journal-title":"J Physiol Sci"},{"key":"B25","doi-asserted-by":"publisher","first-page":"117","DOI":"10.2478\/slgr-2013-0031","article-title":"Poincar\u00e9 plots in analysis of selected biomedical signals","volume":"35","author":"Goli\u0144ska","year":"2013","journal-title":"Stud Log Grammar Rethoric"},{"year":"2023","key":"B26","article-title":"Neurokit2 package v0.2.1"},{"year":"2023","key":"B27","article-title":"HRVAnalysis package v1.0"},{"key":"B28","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1002\/mus.25573","article-title":"Reminder: RMSSD and SD1 are identical heart rate variability metrics","volume":"56","author":"Ciccone","year":"2017","journal-title":"Muscle Nerve"},{"key":"B29","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/S0165-1838(96)00112-9","article-title":"A new method of assessing cardiac autonomic function and its comparison with spectral analysis and coefficient of variation of R\u2013R interval","volume":"62","author":"Toichi","year":"1997","journal-title":"J Auton Nerv Syst"},{"key":"B30","doi-asserted-by":"crossref","first-page":"4563","DOI":"10.1109\/EMBC.2014.6944639","article-title":"Using lorenz plot and cardiac sympathetic index of heart rate variability for detecting seizures for patients with epilepsy","volume-title":"2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"Jeppesen","year":"2014"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s11517-011-0834-z","article-title":"Asymmetric properties of long-term and total heart rate variability","volume":"49","author":"Piskorski","year":"2011","journal-title":"Med Biol Eng Comput"},{"key":"B32","doi-asserted-by":"publisher","first-page":"S10","DOI":"10.1016\/S1389-9457(08)70011-X","article-title":"Measuring sleep quality","author":"Krystal","year":"2008","journal-title":"Sleep Med"},{"key":"B33","doi-asserted-by":"publisher","first-page":"5115","DOI":"10.1109\/EMBC44109.2020.9176081.","article-title":"Pulse oximetry at the wrist during sleep: performance, challenges and perspectives","volume":"2020","author":"Braun","year":"2020","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"B34","doi-asserted-by":"publisher","first-page":"504","DOI":"10.12968\/bjon.2019.28.8.504","article-title":"The importance of respiratory rate monitoring","volume":"28","author":"Rolfe","year":"2019","journal-title":"Br J Nurs"},{"key":"B35","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/TBME.2009.2033264","article-title":"An online self-tunable method to denoise CGM sensor data","volume":"57","author":"Facchinetti","year":"2010","journal-title":"IEEE Trans Biomed Eng"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2024.1402943\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,27]],"date-time":"2024-09-27T05:10:34Z","timestamp":1727413834000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2024.1402943\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,27]]},"references-count":35,"alternative-id":["10.3389\/fdgth.2024.1402943"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2024.1402943","relation":{},"ISSN":["2673-253X"],"issn-type":[{"type":"electronic","value":"2673-253X"}],"subject":[],"published":{"date-parts":[[2024,9,27]]},"article-number":"1402943"}}