Abstract
Advances in implantable radio-telemetry or diverse biologging devices capable of acquiring high-resolution ambulatory electrocardiogram (ECG) or heart rate recordings facilitate comparative physiological investigations by enabling detailed analysis of cardiopulmonary phenotypes and responses in vivo. Two priorities guiding the meaningful adoption of such technologies are: (1) automation, to streamline and standardize large dataset analysis, and (2) flexibility in quality-control. The latter is especially relevant when considering the tendency of some fully automated software solutions to significantly underestimate heart rate when raw signals contain high-amplitude noise. We present herein moving average and standard deviation thresholding (MAST), a novel, open-access algorithm developed to perform automated, accurate, and noise-robust single-channel R-wave detection from ECG obtained in chronically instrumented mice. MAST additionally and automatically excludes and annotates segments where R-wave detection is not possible due to artefact levels exceeding signal levels. Customizable settings (e.g. window width of moving average) allow for MAST to be scaled for use in non-murine species. Two expert reviewers compared MAST’s performance (true/false positive and false negative detections) with that of a commercial ECG analysis program. Both approaches were applied blindly to the same random selection of 270 3-min ECG recordings from a dataset containing varying amounts of signal artefact. MAST exhibited roughly one quarter the error rate of the commercial software and accurately detected R-waves with greater consistency and virtually no false positives (sensitivity, Se: 98.48% ± 4.32% vs. 94.59% ± 17.52%, positive predictivity, +P: 99.99% ± 0.06% vs. 99.57% ± 3.91%, P < 0.001 and P = 0.0274 respectively, Wilcoxon signed rank; values are mean ± SD). Our novel, open-access approach for automated single-channel R-wave detection enables investigators to study murine heart rate indices with greater accuracy and less effort. It also provides a foundational code for translation to other mammals, ectothermic vertebrates, and birds.
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Availability of data and material
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Code availability
The code is available on Github at: https://github.com/NDomnik/MAST_QRS; https://doi.org/10.5281/zenodo.4287696.
References
Amann A, Tratnig R, Unterkofler K (2005) Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online 4:60. https://doi.org/10.1186/1475-925X-4-60
Arroyo-Carmona RE, López-Serrano AL, Albarado-Ibañez A, Mendoza-Lucero FM, Medel-Cajica D, López-Mayorga RM, Torres-Jácome J (2016) Heart rate variability as early biomarker for the evaluation of diabetes mellitus progress. J Diab Res 2016:8483537. https://doi.org/10.1155/2016/8483537
Babaeizadeh S, Gregg RE, Helfenbein ED, Lindauer JM, Zhou SH (2009) Improvements in atrial fibrillation detection for real-time monitoring. J Electrocardiol 42:522–526. https://doi.org/10.1016/j.jelectrocard.2009.06.006
Behar JA, Rosenberg AA, Weiser-Bitoun I, Shemla O, Alexandrovich A, Konyukhov E, Yaniv Y (2018) Cardiac electrophysiology. Front Physiol 9:1390. https://doi.org/10.3389/fphys.2018.01390
Billman GE, Harris WS (2011) Effect of dietary omega-3 fatty acids on the heart rate and the heart rate variability responses to myocardial ischemia or submaximal exercise. Am J Physiol Heart Circ Physiol 300:H2288-2299. https://doi.org/10.1152/ajpheart.00140.2011
Bjarnason Á, Gunnarsson A, Árnason T, Oddgeirsson M, Sigmarsson AB, Gunnarsson Á (2019) Validation of ECG-derived heart rate recordings in Atlantic cod (Gadus morhua L.) with an implantable data logging system. Anim Biotelem 7:13. https://doi.org/10.1186/s40317-019-0176-4
Camm AJ, Malik M, Bigger JT, Breithardt G, Cerutti S, Cohen RJ, Coumel P, Fallen EL, Kennedy HL, Kleiger RE et al (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043
Cao J, Gillberg JM, Swerdlow CD (2012) A fully automatic, implantable cardioverter-defibrillator algorithm to prevent inappropriate detection of ventricular tachycardia or fibrillation due to t-wave oversensing in spontaneous rhythm. Heart Rhythm off J Heart Rhythm Soc 9:522–530. https://doi.org/10.1016/j.hrthm.2011.11.023 (PMID: 22094074)
Carlson SH, Wyss JM (2000) Long-term telemetric recording of arterial pressure and heart rate in mice fed basal and high NaCl diets. Hypertension 35:E1-5. https://doi.org/10.1161/01.HYP.35.2.e1
Cerrone M, Colombi B, Santoro M, Di Barletta MR, Scelsi M, Villani L, Napolitano C, Priori SG (2005) Bidirectional ventricular tachycardia and fibrillation elicited in a knock-in mouse model carrier of a mutation in the cardiac ryanodine receptor. Circ Res 96:e77–e82. https://doi.org/10.1161/01.RES.0000169067.51055.72
Chaise L, Paterson W, Laske TG, Gallon SL, McCafferty DJ, Théry M et al (2017) Implantation of subcutaneous heart rate data loggers in southern elephant seals (Mirounga leonina). Polar Biol 40:2307–2312. https://doi.org/10.1007/s00300-017-2144-x
Champeroux P, Martel E, Jude S, Laigot C, Laveissière A, Weyn-Marotte AA, Fowler JSL, Maurin A, Richard S, Babuty D (2013) Power spectral analysis of heart rate variability in cynomolgus monkeys in safety pharmacology studies: comparative study with beagle dogs. J Pharmacol Toxicol Methods 68:166–174. https://doi.org/10.1016/j.vascn.2013.02.005
Chutkow WA, Pu J, Wheeler MT, Wada T, Makielski JC, Burant CF, McNally EM (2002) Episodic coronary artery vasospasm and hypertension develop in the absence of sur2 K(ATP) channels. J Clin Investig 110:203–208. https://doi.org/10.1172/JCI15672
Clark TD, Sandblom E, Hinch SG, Patterson DA, Frappell PB, Farrell AP (2010) Simultaneous biologging of heart rate and acceleration, and their relationships with energy expenditure in free-swimming sockeye salmon (Oncorhynchus nerka). J Comp Biol B 180:673–684. https://doi.org/10.1007/s00360-009-0442-5
Dash S, Raeder E, Merchant S, Chon K (2009) A statistical approach for accurate detection of atrial fibrillation and flutter. In: 36th annual computers in cardiology conference (CinC), Park City, UT, pp 137–140
Domnik NJ, Seaborn G, Vincent SG, Akl SG, Redfearn DP, Fisher JT (2012) OVA-induced airway hyperresponsiveness alters murine heart rate variability and body temperature. Front Physiol Respir Physiol 3:456. https://doi.org/10.3389/fphys.2012.00456
Domnik NJ, Polymeropoulos ET, Elliott NG, Frappell PB, Fisher JT (2016) Automated non-invasive video-microscopy of oyster spat heart rate during acute temperature change: impact of acclimation temperature. Front Physiol Aquat Physiol 236:236. https://doi.org/10.3389/fphys.2016.00236
Egginton S, May S, Deveci D, Hauton D (2013) Is cold acclimation of benefit to hibernating rodents? J Exp Biol 216:2140–2149. https://doi.org/10.1242/jeb.079160
Fahlman A, Aoki K, Bale G, Brijs J, Chon KH, Drummond CK, Føre M, Manteca X, McDonald BI, McKnight JC, Sakamoto KQ, Suzuki I, Jordana Rivero M, Ropert-Coudert Y, Wisniewska DM (2021) The new era of physio-logging and their grand challenges. Front Physiol. https://doi.org/10.3389/fphys.2021.669158
Fenske S, Pröbstle R, Auer F, Hassan S, Marks V, Pauza D, Biel M, Wahl-Schott C (2015) Comprehensive multilevel in vivo and in vitro analysis of heart rate fluctuations in mice by ECG telemetry and electrophysiology. Nat Protoc 11:61–86. https://doi.org/10.1038/nprot.2015.139
Fernandes S, Amirault JC, Lande G, Nguyen JM, Forest V, Bignolais O, Lamirault G, Heudes D, Orsonneau JL, Heymann MF et al (2006) Autologous myoblast transplantation after myocardial infarction increases the inducibility of ventricular arrhythmias. Cardiovasc Res 69:348–358. https://doi.org/10.1016/j.cardiores.2005.10.003
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Mietus JE, Moody GB, Peng C, Stanley HE (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101:e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215
Griffioen KJ, Wan R, Okun E, Wang X, Lovett-Barr MR, Li Y, Mughal MR, Mendelowitz D, Mattson MP (2011) GLP-1 receptor stimulation depresses heart rate variability and inhibits neurotransmission to cardiac vagal neurons. Cardiovasc Res 89:72–78. https://doi.org/10.1093/cvr/cvq271
Guzik P, Piskorski J, Krauze T, Wykretowicz A, Wysocki H (2006) Heart rate asymmetry by poincaré plots of RR intervals. Biomedizinische Technik (biomedical Engineering) 51:272–275. https://doi.org/10.1515/BMT.2006.054
Hermes RE, Geselowitz DB, Oliver GC (1980) Development, distribution, and use of the American heart association database for ventricular arrhythmia detector evaluation. Comput Cardiol 7:263–266 (ISSN: 02766574)
Hvas M, Folkedal O, Oppedal F (2020) Heart rate bio-loggers as welfare indicators in Atlantic salmon (Salmo salar) aquaculture. Aquaculture 529:735630. https://doi.org/10.1016/j.aquaculture.2020.735630
Kadambe S, Murray R, Boudreaux-Bartels GF (1999) Wavelet transform-based QRS complex detector. IEEE Trans Biomed Eng 46:838–848. https://doi.org/10.1109/10.771194
Kohler BU, Hennig C, Orglmeister R (2002) The principles of software QRS detection. IEEE Eng Med Biol Mag 21:42–57. https://doi.org/10.1109/51.993193
Kramer K, Van Acker SA, Voss HP, Grimbergen JA, Van der Vijgh WJ, Bast A et al (1993) Use of telemetry to record electrocardiogram and heart rate in freely moving mice. J Pharmacol Toxicol Methods 30:209. https://doi.org/10.1016/1056-8719(93)90019-B
Laguna P, Jané R, Caminal P et al (1994) Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput Biomed Res 27:45–60. https://doi.org/10.1006/cbmr.1994.1006
Li Y, Ristagno G, Guan J, Barbut D, Bisera J, Weil MH, Tang W (2012) Preserved heart rate variability during therapeutic hypothermia correlated to 96 h neurological outcomes and survival in a pig model of cardiac arrest. Crit Care Med 40:580–586. https://doi.org/10.1097/CCM.0b013e31822ef9e4
Lillywhite HB, Zippel KC, Farrell AP (1999) Resting and maximal heart rates in ectothermic vertebrates. Comp Biochem Physiol Mol Integr Physiol 124:369–382. https://doi.org/10.1016/s1095-6433(99)00129-4
Lin E, Shafaattalab S, Gill J, Al-Zeer B, Craig C, Lamothe M, Rayani K, Gunawan M, Yueh Li A, Hove-Madsen L, Tibbits GF (2020) Physiological phenotyping of the adult zebrafish heart. Mar Genom 49:100701. https://doi.org/10.1016/j.margen.2019.100701
Lipponen JA, Tarvainen MP (2019) A robust algorithm for HRV time series artefact correction using novel beat classification. J Med Eng Technol 43:173–181. https://doi.org/10.1080/03091902.2019.1640306
Malik M, Camm AJ (eds) (1995) Heart rate variability, 1st edn. Wiley, New York
Mangoni ME, Traboulsie A, Leoni AL, Couette B, Marger L, Le Quang K, Kupfer E, Cohen-Solal A, Vilar J, Shin HS et al (2006) Bradycardia and slowing of the atrioventricular conduction in mice lacking CaV3.1∕α1G T-type calcium channels. Circ Res 98:1422–1430. https://doi.org/10.1161/01.RES.0000225862.14314.49
Marshall DJ, Peter R, Chown SL (2004) Regulated bradycardia in the pulmonated limpet Siphonaria (Gastropoda: Mollusca) during pollutant exposure: implication for biomarker studies. Comp Biochem Physiol Mol Integr Physiol 139:309–316. https://doi.org/10.1016/j.cbpb.2004.09.014
Mehta SS, Lingayat NS (2008) Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM. Comput Biol Med 38:138–145. https://doi.org/10.1016/j.compbiomed.2007.08.003
Merentie M, Lipponen JA, Hedman M, Hedman A, Hartikainen J, Hussko J, Lottonen-Raikaslehto L, Parviainen V, Laidinen S, Karjalainen PA, Ylä-Herttuala S (2015) Mouse ECG findings in aging, with conduction system affecting drugs and in cardiac pathologies: Development and validation of ECG analysis algorithm in mice. Physiol Rep 3:e12639. https://doi.org/10.14814/phy2.12639
Mungrue IN, Gros R, You X, Pirani A, Azad A, Csont T, Schulz R, Butany J, Stewart DJ, Husain M et al (2002) Cardiomyocyte overexpression of iNOS in mice results in peroxynitrite generation, heart block, and sudden death. J Clin Investig 109:735–744. https://doi.org/10.1172/JCI13265
Nabil D, Reguig FB (2015) Ectopic beats detection and correction methods: a review. Biomed Signal Process Control 18:224–228. https://doi.org/10.1016/j.bspc.2015.01.008
Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng BME-32:230–236. https://doi.org/10.1109/TBME.1985.325532
Pei Y, Jiang R, Zou Y, Wang Y, Zhang S, Wang G, Zhao J, Song W (2016) Effects of fine particulate matter (PM2.5) on systemic oxidative stress and cardiac function in ApoE−/− mice. Int J Environ Res Public Health 13:484. https://doi.org/10.3390/ijerph13050484
Peter JC, Tugler J, Eftekhari P, Maurice D, Hoebeke J, Roegel JC (2005) Effects on heart rate of an anti-M2 acetylcholine receptor immune response in mice. FASEB J 19:943–949. https://doi.org/10.1096/fj.04-3042com
Poli R, Cagnoni S, Valli G (1995) Genetic design of optimum linear and nonlinear QRS detectors. IEEE Trans Biomed Eng 42:1137–1141. https://doi.org/10.1109/10.469381
Renwrantz L, Spielvogel F (2011) Heart rate and hemocyte number as stress indicators in disturbed hibernating vineyard snail, Helix pomatia. Comp Biochem Physiol Part Mol Integr Physiol 160:467–473. https://doi.org/10.1016/j.cbpa.2011.08.002
Ropert-Coudert T, Brooks L, Yamamoto M, Kato A (2009) ECG response of koalas to tourists proximity: a preliminary study. PLoS One 4:e7378. https://doi.org/10.1371/journal.pone.0007378
Sakamoto KQ, Takahashi A, Iwata T, Yamamoto T, Tamamoto M, Trathan PN (2013) Heart rate and estimated energy expenditure of flapping and gliding in black-browed albatrosses. J Exp Biol 216:3175–3182. https://doi.org/10.1242/jeb.079905
Sakamoto KQ, Miyayama M, Kinoshita C, Fukuoka T, Ishihara T, Sato K (2021) A non-invasive system to measure heart rate in hard-shelled sea turtles: potential for field applications. Philos Trans R Soc B. https://doi.org/10.1098/rstb.2020.0222
Sato S (2019) Multi-dry-electrode plate sensor for non-invasive electrocardiogram and heart rate monitoring for the assessment of drug responses in freely behaving mice. J Pharmacol Toxicol Methods 97:29–35. https://doi.org/10.1016/j.vascn.2019.02.009
Schmidt G, Malik M, Barthel P, Schneider R, Ulm K, Rolnitzky L, Camm AJ, Bigger JT, Schömig A (1999) Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 353:1390–1396. https://doi.org/10.1016/S0140-6736(98)08428-1
Shusterman V, Usiene I, Harrigal C, Lee JS, Kubota T, Feldman AM, London B (2002) Strain-specific patterns of autonomic nervous system activity and heart failure susceptibility in mice. Am J Physiol Heart Circ Physiol 282:H2076–H2083. https://doi.org/10.1152/ajpheart.00917.2001
Signer C, Ruf G, Schober F, Fluch G, Paumann T, Arnold W (2010) A versatile telemetry system for continuous measurement of heart rate, body temperature and locomotor activity in free-ranging ruminants. Methods Ecol Evol 1:75–85. https://doi.org/10.1111/j.2041-210X.2009.00010.x
Sood S, Chelu MG, van Oort RJ, Skapura D, Santonastasi M, Dobrev D, Wehrens XH (2008) Intracellular calcium leak due to FKBP12. 6 deficiency in mice facilitates the inducibility of atrial fibrillation. Heart Rhythm 5:1047–1054. https://doi.org/10.1016/j.hrthm.2008.03.030
Steijns F, Toth MI, Demolder A, Larsen LE, Desloovere J, Renard M, Raedt R, Segers P, De Backer J, Sips P (2020) Ambulatory electrocardiographic monitoring and ectopic beat detection in conscious mice. Sensors 20:3867. https://doi.org/10.3390/s20143867
Steinberg JS, Varma N, Cygankiewicz I, Aziz P, Balsam P, Baranchuck A, Cantillon DJ, Dilaveris P, Dubner SJ, El-Sherif N, Krol J, Kurpesa M, La Rovere MT, Lobodzinski SS, Locati ET, Mittal S, Olshansky B, Piotrowicz E, Saxon L, Stone PH, Tereshchenko L, Turitto G, Turakhia MP, Winner NJ, Verrier RL, Zareba W, Piotrowicz R (2017) 2017 ISHNE-HRS expert consensus statement on ambulatory ECG and external cardiac monitoring/telemetry. Heart Rhythm 14:e55–e96. https://doi.org/10.1016/j.hrthm.2017.03.038
Streicher JW, Cox CL, Birchard GF (2012) Non-linear scaling of oxygen consumption and heart rate in a very large cockroach species (Gromphadorhina portentosa): correlated changes with body size and temperature. J Exp Biol 215:1137–1143. https://doi.org/10.1242/jeb.061143
Thireau J, Zhang BL, Poisson D, Babuty D (2008) Heart rate variability in mice: a theoretical and practical guide. Exp Physiol 93:83–94. https://doi.org/10.1113/expphysiol.2007.040733
Tontodonati M, Fasdelli N, Dorigatti R (2011) An improved method of electrode placement in configuration Lead II for the reliable ECG recording by telemetry in the conscious rat. J Pharmacol Toxicol Methods 63:1–6. https://doi.org/10.1016/j.vascn.2010.03.001
Torbey S, Akl SG, Redfearn DP (2012) Multi-lead QRS detection using window pairs. Conference proceedings: annual international conference of the IEEE engineering in medicine and biology society 2012, pp 3143–3146. PMID: 23366592. https://doi.org/10.1109/EMBC.2012.6346631
Turbill C, Ruf T, Mang T, Arnold W (2011) Regulation of heart rate and rumen temperature in red deer: effects of season and food intake. J Exp Biol 214:963–970. https://doi.org/10.1242/jeb052282
van Acker FA, van Acker SA, Kramer K, Haenen GR, Bast A, van der Vijgh WJ (2000) 7-Monohydroxyethylrutoside protects against chronic doxorubicin-induced cardiotoxicity when administered only once per week. Clin Cancer Res 6:1337–1341 (PMID: 10778960)
Wakimoto H, Maguire CT, Sherwood MC, Vargas MM, Sarkar PS, Han J, Reddy S, Berul CI (2002) Characterization of cardiac conduction system abnormalities in mice with targeted disruption of Six5 gene. J Interv Card Electrophysiol 7:127–135. https://doi.org/10.1023/A:1020881520353
Wascher CA, Kotrschal K, Arnold W (2018) Free-living greylag geese adjust their heart rates and body core temperatures to season and reproductive context. Sci Rep 8:2142. https://doi.org/10.1038/s41598-018-20655-z
Wu Y, Temple J, Zhang R, Dzhura I, Zhang W, Trimble R, Roden DM, Passier R, Olson EN, Colbran RJ et al (2002) Calmodulin kinase II and arrhythmias in a mouse model of cardiac hypertrophy. Circulation 106:1288–1293. https://doi.org/10.1161/01.CIR.0000027583.73268.E7
Xue Q, Hu YH, Tompkins WJ (1992) Neural-network-based adaptive matched filtering for QRS detection. IEEE Trans Biomed Eng 39:317–329. https://doi.org/10.1109/10.126604
Yu BC, Liu CS, Lee M, Chen CY, Chian BN (1985) A nonlinear digital filter for cardiac QRS complex detection. J Clin Eng 10:193–201 (Issn Print: 0363-8855)
Acknowledgements
To Frapps (Peter Frappell), to whom this special edition is dedicated: our sincere gratitude for your unfailing support, passion, curiosity, and enthusiasm for your research and for your unparalleled ability to inspire the same in others. JTF and NJD send heartfelt thanks for an unforgettable research adventure in Hobart; it is one that they reminisce over both fondly and frequently, regaling any trainee who will listen about the wonders of the invertebrate heart. Thank you for welcoming us to your lab, your campus, your wonderful extended research community, and (along with Deirdre) your home. You have touched our lives in so many ways. The authors also wish to thank Alexandra McCartney (Queen’s University) for her support in performing literature searches and her contributions towards the citations contained in this manuscript.
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Research was supported by Canadian Institutes of Health Research (JTF: MOP81211), a Personnel Award from the Heart and Stroke Foundation of Canada (ST), a Natural Science and Engineering Research Council Alexander Graham Bell CGS Scholarship (NJD) and Queen’s University’s Faculty of Arts and Sciences (GEJS).
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All authors contributed to study conception and design, drafting, and editing of the manuscript and approved its final version. NJD conducted the radio-telemetry experiments. ST and GEJS wrote the algorithm code. NJD and GEJS analyzed resulting data with the reported code as well as a commercially available software solution.
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Experiments were approved by the Queen’s Animal Care Committee (protocol #: 2016-1632 “The Role of Muscarinic receptor subtypes in the cardiopulmonary phenotype of the OVA sensitized allergic airway hyperresponsive (AHR) murine model”) and performed according to the guidelines of the Canadian Council of Animal Care.
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Domnik, N.J., Torbey, S., Seaborn, G.E.J. et al. Moving average and standard deviation thresholding (MAST): a novel algorithm for accurate R-wave detection in the murine electrocardiogram. J Comp Physiol B 191, 1071–1083 (2021). https://doi.org/10.1007/s00360-021-01389-3
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DOI: https://doi.org/10.1007/s00360-021-01389-3