@article{soriano2020,
	title = {Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017},
	volume = {8},
	issn = {22132600},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2213260020301053},
	doi = {10.1016/S2213-2600(20)30105-3},
	shorttitle = {Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017},
	pages = {585--596},
	number = {6},
	journaltitle = {The Lancet Respiratory Medicine},
	author = {Soriano, Joan B and Kendrick, Parkes J and Paulson, Katherine R and Gupta, Vinay and Abrams, Elissa M and Adedoyin, Rufus Adesoji and Adhikari, Tara Ballav and Advani, Shailesh M and Agrawal, Anurag and Ahmadian, Elham and Alahdab, Fares and Aljunid, Syed Mohamed and Altirkawi, Khalid A and Alvis-Guzman, Nelson and Anber, Nahla Hamed and Andrei, Catalina Liliana and Anjomshoa, Mina and Ansari, Fereshteh and Antó, Josep M and Arabloo, Jalal and Athari, Seyyede Masoume and Athari, Seyyed Shamsadin and Awoke, Nefsu and Badawi, Alaa and Banoub, Joseph Adel Mattar and Bennett, Derrick A and Bensenor, Isabela M and Berfield, Kathleen S Sachiko and Bernstein, Robert S and Bhattacharyya, Krittika and Bijani, Ali and Brauer, Michael and Bukhman, Gene and Butt, Zahid A and Cámera, Luis Alberto and Car, Josip and Carrero, Juan J and Carvalho, Felix and Castañeda-Orjuela, Carlos A and Choi, Jee-Young Jasmine and Christopher, Devasahayam J and Cohen, Aaron J and Dandona, Lalit and Dandona, Rakhi and Dang, Anh Kim and Daryani, Ahmad and De Courten, Barbora and Demeke, Feleke Mekonnen and Demoz, Gebre Teklemariam and De Neve, Jan-Walter and Desai, Rupak and Dharmaratne, Samath Dhamminda and Diaz, Daniel and Douiri, Abdel and Driscoll, Tim Robert and Duken, Eyasu Ejeta and Eftekhari, Aziz and Elkout, Hajer and Endries, Aman Yesuf and Fadhil, Ibtihal and Faro, Andre and Farzadfar, Farshad and Fernandes, Eduarda and Filip, Irina and Fischer, Florian and Foroutan, Masoud and Garcia-Gordillo, M.A. and Gebre, Abadi Kahsu and Gebremedhin, Ketema Bizuwork and Gebremeskel, Gebreamlak Gebremedhn and Gezae, Kebede Embaye and Ghoshal, Aloke Gopal and Gill, Paramjit Singh and Gillum, Richard F and Goudarzi, Houman and Guo, Yuming and Gupta, Rajeev and Hailu, Gessessew Bugssa and Hasanzadeh, Amir and Hassen, Hamid Yimam and Hay, Simon I and Hoang, Chi Linh and Hole, Michael K and Horita, Nobuyuki and Hosgood, H Dean and Hostiuc, Mihaela and Househ, Mowafa and Ilesanmi, Olayinka Stephen and Ilic, Milena D and Irvani, Seyed Sina Naghibi and Islam, Sheikh Mohammed Shariful and Jakovljevic, Mihajlo and Jamal, Amr A and Jha, Ravi Prakash and Jonas, Jost B and Kabir, Zubair and Kasaeian, Amir and Kasahun, Gebremicheal Gebreslassie and Kassa, Getachew Mullu and Kefale, Adane Teshome and Kengne, Andre Pascal and Khader, Yousef Saleh and Khafaie, Morteza Abdullatif and Khan, Ejaz Ahmad and Khan, Junaid and Khubchandani, Jagdish and Kim, Young-Eun and Kim, Yun Jin and Kisa, Sezer and Kisa, Adnan and Knibbs, Luke D and Komaki, Hamidreza and Koul, Parvaiz A and Koyanagi, Ai and Kumar, G Anil and Lan, Qing and Lasrado, Savita and Lauriola, Paolo and La Vecchia, Carlo and Le, Tham Thi and Leigh, James and Levi, Miriam and Li, Shanshan and Lopez, Alan D and Lotufo, Paulo A and Madotto, Fabiana and Mahotra, Narayan B and Majdan, Marek and Majeed, Azeem and Malekzadeh, Reza and Mamun, Abdullah A and Manafi, Navid and Manafi, Farzad and Mantovani, Lorenzo Giovanni and Meharie, Birhanu Geta and Meles, Hagazi Gebre and Meles, Gebrekiros Gebremichael and Menezes, Ritesh G and Mestrovic, Tomislav and Miller, Ted R and Mini, Gk and Mirrakhimov, Erkin M and Moazen, Babak and Mohammad, Karzan Abdulmuhsin and Mohammed, Shafiu and Mohebi, Farnam and Mokdad, Ali H and Molokhia, Mariam and Monasta, Lorenzo and Moradi, Masoud and Moradi, Ghobad and Morawska, Lidia and Mousavi, Seyyed Meysam and Musa, Kamarul Imran and Mustafa, Ghulam and Naderi, Mehdi and Naghavi, Mohsen and Naik, Gurudatta and Nair, Sanjeev and Nangia, Vinay and Nansseu, Jobert Richie and Nazari, Javad and Ndwandwe, Duduzile Edith and Negoi, Ruxandra Irina and Nguyen, Trang Huyen and Nguyen, Cuong Tat and Nguyen, Huong Lan Thi and Nixon, Molly R and Ofori-Asenso, Richard and Ogbo, Felix Akpojene and Olagunju, Andrew T and Olagunju, Tinuke O and Oren, Eyal and Ortiz, Justin R and Owolabi, Mayowa O and P A, Mahesh and Pakhale, Smita and Pana, Adrian and Panda-Jonas, Songhomitra and Park, Eun-Kee and Pham, Hai Quang and Postma, Maarten J and Pourjafar, Hadi and Poustchi, Hossein and Radfar, Amir and Rafiei, Alireza and Rahim, Fakher and Rahman, Mohammad Hifz Ur and Rahman, Muhammad Aziz and Rawaf, Salman and Rawaf, David Laith and Rawal, Lal and Reiner Jr., Robert C and Reitsma, Marissa Bettay and Roever, Leonardo and Ronfani, Luca and Roro, Elias Merdassa and Roshandel, Gholamreza and Rudd, Kristina E and Sabde, Yogesh Damodar and Sabour, Siamak and Saddik, Basema and Safari, Saeed and Saleem, Komal and Samy, Abdallah M and Santric-Milicevic, Milena M and Sao Jose, Bruno Piassi and Sartorius, Benn and Satpathy, Maheswar and Savic, Miloje and Sawhney, Monika and Sepanlou, Sadaf G and Shaikh, Masood Ali and Sheikh, Aziz and Shigematsu, Mika and Shirkoohi, Reza and Si, Si and Siabani, Soraya and Singh, Virendra and Singh, Jasvinder A and Soljak, Michael and Somayaji, Ranjani and Soofi, Moslem and Soyiri, Ireneous N and Tefera, Yonatal Mesfin and Temsah, Mohamad-Hani and Tesfay, Berhe Etsay and Thakur, Jarnail Singh and Toma, Alemayehu Toma and Tortajada-Girbés, Miguel and Tran, Khanh Bao and Tran, Bach Xuan and Tudor Car, Lorainne and Ullah, Irfan and Vacante, Marco and Valdez, Pascual R and Van Boven, Job F M and Vasankari, Tommi Juhani and Veisani, Yousef and Violante, Francesco S and Wagner, Gregory R and Westerman, Ronny and Wolfe, Charles D A and Wondafrash, Dawit Zewdu and Wondmieneh, Adam Belay and Yonemoto, Naohiro and Yoon, Seok-Jun and Zaidi, Zoubida and Zamani, Mohammad and Zar, Heather J and Zhang, Yunquan and Vos, Theo},
	urldate = {2025-06-24},
	date = {2020-06},
	langid = {english},
}

@article{contreras-briceno2024,
	title = {Estimation of ventilatory thresholds during exercise using respiratory wearable sensors},
	volume = {7},
	rights = {https://creativecommons.org/licenses/by/4.0},
	issn = {2398-6352},
	url = {https://www.nature.com/articles/s41746-024-01191-9},
	doi = {10.1038/s41746-024-01191-9},
	abstract = {{AbstractVentilatory} thresholds ({VTs}) are key physiological parameters used to evaluate physical performance and determine aerobic and anaerobic transitions during exercise. Current assessment of these parameters requires ergospirometry, limiting evaluation to laboratory or clinical settings. In this work, we introduce a wearable respiratory system that continuously tracks breathing during exercise and estimates {VTs} during ramp tests. We validate the respiratory rate and {VTs} predictions in 17 healthy adults using ergospirometry analysis. In addition, we use the wearable system to evaluate {VTs} in 107 recreational athletes during ramp tests outside the laboratory and show that the mean population values agree with physiological variables traditionally used to exercise prescription. We envision that respiratory wearables can be useful in determining aerobic and anaerobic parameters with promising applications in health telemonitoring and human performance.},
	number = {1},
	journaltitle = {npj Digital Medicine},
	shortjournal = {npj Digit. Med.},
	author = {Contreras-Briceño, Felipe and Cancino, Jorge and Espinosa-Ramírez, Maximiliano and Fernández, Gonzalo and Johnson, Vader and Hurtado, Daniel E.},
	urldate = {2025-07-22},
	date = {2024-07-26},
	langid = {english},
	note = {Publisher: Springer Science and Business Media {LLC}},
}

@article{harbour2022,
	title = {Breath Tools: A Synthesis of Evidence-Based Breathing Strategies to Enhance Human Running},
	volume = {13},
	rights = {https://creativecommons.org/licenses/by/4.0/},
	issn = {1664-042X},
	url = {https://www.frontiersin.org/articles/10.3389/fphys.2022.813243/full},
	doi = {10.3389/fphys.2022.813243},
	shorttitle = {Breath Tools},
	abstract = {Running is among the most popular sporting hobbies and often chosen specifically for intrinsic psychological benefits. However, up to 40\% of runners may experience exercise-induced dyspnoea as a result of cascading physiological phenomena, possibly causing negative psychological states or barriers to participation. Breathing techniques such as slow, deep breathing have proven benefits at rest, but it is unclear if they can be used during exercise to address respiratory limitations or improve performance. While direct experimental evidence is limited, diverse findings from exercise physiology and sports science combined with anecdotal knowledge from Yoga, meditation, and breathwork suggest that many aspects of breathing could be improved via purposeful strategies. Hence, we sought to synthesize these disparate sources to create a new theoretical framework called “Breath Tools” proposing breathing strategies for use during running to improve tolerance, performance, and lower barriers to long-term enjoyment.},
	journaltitle = {Frontiers in Physiology},
	shortjournal = {Front. Physiol.},
	author = {Harbour, Eric and Stöggl, Thomas and Schwameder, Hermann and Finkenzeller, Thomas},
	urldate = {2025-07-22},
	date = {2022-03-17},
	note = {Publisher: Frontiers Media {SA}},
}

@article{fincham2023,
	title = {Effect of breathwork on stress and mental health: A meta-analysis of randomised-controlled trials},
	volume = {13},
	rights = {https://creativecommons.org/licenses/by/4.0},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-022-27247-y},
	doi = {10.1038/s41598-022-27247-y},
	shorttitle = {Effect of breathwork on stress and mental health},
	abstract = {{AbstractDeliberate} control of the breath (breathwork) has recently received an unprecedented surge in public interest and breathing techniques have therapeutic potential to improve mental health. Our meta-analysis primarily aimed to evaluate the efficacy of breathwork through examining whether, and to what extent, breathwork interventions were associated with lower levels of self-reported/subjective stress compared to non-breathwork controls. We searched {PsycInfo}, {PubMed}, {ProQuest}, Scopus, Web of Science, {ClinicalTrials}.gov and {ISRCTN} up to February 2022, initially identifying 1325 results. The primary outcome self-reported/subjective stress included 12 randomised-controlled trials (k = 12) with a total of 785 adult participants. Most studies were deemed as being at moderate risk of bias. The random-effects analysis yielded a significant small-to-medium mean effect size,g = − 0.35 [95\% {CI} − 0.55, − 0.14],z = 3.32,p = 0.0009, showing breathwork was associated with lower levels of stress than control conditions. Heterogeneity was intermediate and approaching significance,χ211 = 19,p = 0.06,I2 = 42\%. Meta-analyses for secondary outcomes of self-reported/subjective anxiety (k = 20) and depressive symptoms (k = 18) showed similar significant effect sizes:g = − 0.32,p {\textless} 0.0001, andg = − 0.40,p {\textless} 0.0001, respectively. Heterogeneity was moderate and significant for both. Overall, results showed that breathwork may be effective for improving stress and mental health. However, we urge caution and advocate for nuanced research approaches with low risk-of-bias study designs to avoid a miscalibration between hype and evidence.},
	number = {1},
	journaltitle = {Scientific Reports},
	shortjournal = {Sci Rep},
	author = {Fincham, Guy William and Strauss, Clara and Montero-Marin, Jesus and Cavanagh, Kate},
	urldate = {2025-07-22},
	date = {2023-01-09},
	langid = {english},
	note = {Publisher: Springer Science and Business Media {LLC}},
}

@article{lusk2023,
	title = {An automated respiratory data pipeline for waveform characteristic analysis},
	volume = {601},
	issn = {0022-3751, 1469-7793},
	url = {https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP284363},
	doi = {10.1113/JP284363},
	abstract = {Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high‐throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open‐source pipeline for processing raw recordings and associated metadata into operative outcomes, publication‐worthy graphs and robust statistical analyses including {QQ} and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2‐year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open‐source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. Key points: Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face‐value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication‐worthy graphs with statistics. We validate Breathe Easy with a terabyte‐scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.},
	pages = {4767--4806},
	number = {21},
	journaltitle = {The Journal of Physiology},
	shortjournal = {The Journal of Physiology},
	author = {Lusk, Savannah and Ward, Christopher S. and Chang, Andersen and Twitchell‐Heyne, Avery and Fattig, Shaun and Allen, Genevera and Jankowsky, Joanna L. and Ray, Russell S.},
	urldate = {2025-07-04},
	date = {2023-11},
	langid = {english},
}

@article{bishop2022,
	title = {An open-source tool for automated analysis of breathing behaviors in common marmosets and rodents},
	volume = {11},
	issn = {2050-084X},
	url = {https://elifesciences.org/articles/71647},
	doi = {10.7554/eLife.71647},
	abstract = {The respiratory system maintains homeostatic levels of oxygen (O2) and carbon dioxide ({CO}2) in the body through rapid and efficient regulation of breathing frequency and depth (tidal volume). The commonly used methods of analyzing breathing data in behaving experimental animals are usually subjective, laborious, and time-consuming. To overcome these hurdles, we optimized an analysis toolkit for the unsupervised study of respiratory activities in animal subjects. Using this tool, we analyzed breathing behaviors of the common marmoset (Callithrix jacchus), a New World non-human primate model. Using whole-body plethysmography in room air as well as acute hypoxic (10\% O2) and hypercapnic (6\% {CO}2) conditions, we describe breathing behaviors in awake, freely behaving marmosets. Our data indicate that marmosets’ exposure to acute hypoxia decreased metabolic rate and increased sigh rate. However, the hypoxic condition did not augment ventilation. Hypercapnia, on the other hand, increased both the frequency and depth (i.e., tidal volume) of breathing.},
	pages = {e71647},
	journaltitle = {{eLife}},
	author = {Bishop, Mitchell and Weinhold, Maximilian and Turk, Ariana Z and Adeck, Afuh and {SheikhBahaei}, Shahriar},
	urldate = {2025-06-30},
	date = {2022-01-20},
	langid = {english},
}

@article{brammer2020,
	title = {biopeaks: a graphical user interface for feature extraction from heart- and breathing biosignals},
	volume = {5},
	rights = {http://creativecommons.org/licenses/by/4.0/},
	issn = {2475-9066},
	url = {https://joss.theoj.org/papers/10.21105/joss.02621},
	doi = {10.21105/joss.02621},
	shorttitle = {biopeaks},
	pages = {2621},
	number = {54},
	journaltitle = {Journal of Open Source Software},
	shortjournal = {{JOSS}},
	author = {Brammer, Jan},
	urldate = {2025-06-30},
	date = {2020-10-27},
}

@article{makowski2021,
	title = {{NeuroKit}2: A Python toolbox for neurophysiological signal processing},
	volume = {53},
	rights = {https://www.springer.com/tdm},
	issn = {1554-3528},
	url = {https://link.springer.com/10.3758/s13428-020-01516-y},
	doi = {10.3758/s13428-020-01516-y},
	shorttitle = {{NeuroKit}2},
	pages = {1689--1696},
	number = {4},
	journaltitle = {Behavior Research Methods},
	shortjournal = {Behav Res},
	author = {Makowski, Dominique and Pham, Tam and Lau, Zen J. and Brammer, Jan C. and Lespinasse, François and Pham, Hung and Schölzel, Christopher and Chen, S. H. Annabel},
	urldate = {2025-07-25},
	date = {2021-08},
	langid = {english},
	note = {Publisher: Springer Science and Business Media {LLC}},
}

@article{vanegas2020,
	title = {Sensing Systems for Respiration Monitoring: A Technical Systematic Review},
	volume = {20},
	issn = {1424-8220},
	doi = {10.3390/s20185446},
	shorttitle = {Sensing Systems for Respiration Monitoring},
	abstract = {Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.},
	pages = {5446},
	number = {18},
	journaltitle = {Sensors (Basel, Switzerland)},
	shortjournal = {Sensors (Basel)},
	author = {Vanegas, Erik and Igual, Raul and Plaza, Inmaculada},
	date = {2020-09-22},
	pmid = {32972028},
	pmcid = {PMC7570710},
	keywords = {Algorithms, breathing sensor, comprehensive review, Humans, Monitoring, Physiologic, Respiration, respiration sensor, respiratory monitoring, sensor comparison, Software, systematic review, technical review},
}

@article{germain2023,
	title = {Unsupervised classification of plethysmography signals with advanced visual representations},
	volume = {14},
	issn = {1664-042X},
	url = {https://www.frontiersin.org/articles/10.3389/fphys.2023.1154328/full},
	doi = {10.3389/fphys.2023.1154328},
	abstract = {Ventilation is a simple physiological function that ensures the vital supply of oxygen and the elimination of {CO}2. The recording of the airflow through the nostrils of a mouse over time makes it possible to calculate the position of critical points, based on the shape of the signals, to compute the respiratory frequency and the volume of air exchanged. These descriptors only account for a part of the dynamics of respiratory exchanges. In this work we present a new algorithm that directly compares the shapes of signals and considers meaningful information about the breathing dynamics omitted by the previous descriptors. The algorithm leads to a new classification of inspiration and expiration, which reveals that mice respond and adapt differently to inhibition of cholinesterases, enzymes targeted by nerve gas, pesticide, or drug intoxication.},
	pages = {1154328},
	journaltitle = {Frontiers in Physiology},
	shortjournal = {Front. Physiol.},
	author = {Germain, Thibaut and Truong, Charles and Oudre, Laurent and Krejci, Eric},
	urldate = {2025-06-30},
	date = {2023-05-23},
}

@article{criee2011,
	title = {Body plethysmography – Its principles and clinical use},
	volume = {105},
	rights = {https://www.elsevier.com/tdm/userlicense/1.0/},
	issn = {09546111},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0954611111000552},
	doi = {10.1016/j.rmed.2011.02.006},
	pages = {959--971},
	number = {7},
	journaltitle = {Respiratory Medicine},
	shortjournal = {Respiratory Medicine},
	author = {Criée, C.P. and Sorichter, S. and Smith, H.J. and Kardos, P. and Merget, R. and Heise, D. and Berdel, D. and Köhler, D. and Magnussen, H. and Marek, W. and Mitfessel, H. and Rasche, K. and Rolke, M. and Worth, H. and Jörres, R.A.},
	urldate = {2025-07-04},
	date = {2011-07},
	langid = {english},
}

@article{holm2024,
	title = {{BreathFinder}: A Method for Non-Invasive Isolation of Respiratory Cycles Utilizing the Thoracic Respiratory Inductance Plethysmography Signal},
	volume = {16},
	issn = {1179-1608},
	doi = {10.2147/NSS.S468431},
	shorttitle = {{BreathFinder}},
	abstract = {{INTRODUCTION}: The field of automatic respiratory analysis focuses mainly on breath detection on signals such as audio recordings, or nasal flow measurement, which suffer from issues with background noise and other disturbances. Here we introduce a novel algorithm designed to isolate individual respiratory cycles on a thoracic respiratory inductance plethysmography signal using the non-invasive signal of the respiratory inductance plethysmography belts.
{PURPOSE}: The algorithm locates breaths using signal processing and statistical methods on the thoracic respiratory inductance plethysmography belt and enables the analysis of sleep data on an individual breath level.
{PATIENTS} {AND} {METHODS}: The algorithm was evaluated against a cohort of 31 participants, both healthy and diagnosed with obstructive sleep apnea. The dataset consisted of 13 female and 18 male participants between the ages of 20 and 69. The algorithm was evaluated on 7.3 hours of hand-annotated data from the cohort, or 8782 individual breaths in total. The algorithm was specifically evaluated on a dataset containing many sleep-disordered breathing events to confirm that it did not suffer in terms of accuracy when detecting breaths in the presence of sleep-disordered breathing. The algorithm was also evaluated across many participants, and we found that its accuracy was consistent across people. Source code for the algorithm was made public via an open-source Python library.
{RESULTS}: The proposed algorithm achieved an estimated 94\% accuracy when detecting breaths in respiratory signals while producing false positives that amount to only 5\% of the total number of detections. The accuracy was not affected by the presence of respiratory related events, such as obstructive apneas or snoring.
{CONCLUSION}: This work presents an automatic respiratory cycle algorithm suitable for use as an analytical tool for research based on individual breaths in sleep recordings that include respiratory inductance plethysmography.},
	pages = {1253--1266},
	journaltitle = {Nature and Science of Sleep},
	shortjournal = {Nat Sci Sleep},
	author = {Holm, Benedikt and Borsky, Michal and Arnardottir, Erna S. and Serwatko, Marta and Mallett, Jacky and Islind, Anna Sigridur and Óskarsdóttir, María},
	date = {2024},
	pmid = {39189036},
	pmcid = {PMC11345460},
	keywords = {breath detection algorithm, breath segmentation, respiratory analysis, respiratory cycle isolation, sleep analysis},
}