{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T15:06:52Z","timestamp":1775747212898,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,2,9]],"date-time":"2015-02-09T00:00:00Z","timestamp":1423440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Thailand Research Fund (TRF), under Royal Golden Jubilee Ph.D.","award":["PHD\/0225\/2551"],"award-info":[{"award-number":["PHD\/0225\/2551"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>False alarms in cardiac monitoring affect the quality of medical care, impacting on both patients and healthcare providers. In continuous cardiac monitoring using wireless Body Sensor Networks (BSNs), the quality of ECG signals can be deteriorated owing to several factors, e.g., noises, low battery power, and network transmission problems, often resulting in high false alarm rates. In addition, body movements occurring from activities of daily living (ADLs) can also create false alarms. This paper presents a two-phase framework for false arrhythmia alarm reduction in continuous cardiac monitoring, using signals from an ECG sensor and a 3D accelerometer. In the first phase, classification models constructed using machine learning algorithms are used for labeling input signals. ECG signals are labeled with heartbeat types and signal quality levels, while 3D acceleration signals are labeled with ADL types. In the second phase, a rule-based expert system is used for combining classification results in order to determine whether arrhythmia alarms should be accepted or suppressed. The proposed framework was validated on datasets acquired using BSNs and the MIT-BIH arrhythmia database. For the BSN dataset, acceleration and ECG signals were collected from 10 young and 10 elderly subjects while they were performing ADLs. The framework reduced the false alarm rate from 9.58% to 1.43% in our experimental study, showing that it can potentially assist physicians in diagnosing a vast amount of data acquired from wireless sensors and enhance the performance of continuous cardiac monitoring.<\/jats:p>","DOI":"10.3390\/s150203952","type":"journal-article","created":{"date-parts":[[2015,2,9]],"date-time":"2015-02-09T10:17:04Z","timestamp":1423477024000},"page":"3952-3974","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information"],"prefix":"10.3390","volume":"15","author":[{"given":"Tanatorn","family":"Tanantong","sequence":"first","affiliation":[{"name":"School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ekawit","family":"Nantajeewarawat","sequence":"additional","affiliation":[{"name":"School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Surapa","family":"Thiemjarus","sequence":"additional","affiliation":[{"name":"National Electronics and Computer Technology Center, Pathum Thani 12120, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,9]]},"reference":[{"key":"ref_1","unstructured":"W.H.F. World Heart Day. Available Online: http:\/\/www.world-heart-federation.org\/what-we-do\/awareness\/world-heart-day\/one-heart\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1088\/0967-3334\/29\/11\/R01","article-title":"Wireless body sensor networks for health-monitoring applications","volume":"29","author":"Hao","year":"2008","journal-title":"Physiol. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMCC.2009.2032660","article-title":"A survey on wearable sensor-based systems for health monitoring and prognosis","volume":"40","author":"Pantelopoulos","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1109\/JBHI.2014.2311232","article-title":"Implementation of a wireless ECG acquisition SoC for IEEE 802.15.4 (ZigBee) applications","volume":"19","author":"Lee","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1109\/TBME.2004.827359","article-title":"Automatic classification of heartbeats using ECG morphology and heartbeat interval features","volume":"51","author":"Chazal","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1109\/TBME.2006.883802","article-title":"A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features","volume":"53","author":"Chazal","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1109\/TBME.2010.2068048","article-title":"Heartbeat classification using feature selection driven by database generalization criteria","volume":"58","author":"Llamedo","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.compbiomed.2010.11.003","article-title":"A multi-stage automatic arrhythmia recognition and classification system","volume":"41","author":"Kutlu","year":"2011","journal-title":"Comput. Biol. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/TBME.2011.2171037","article-title":"Weighted conditional random fields for supervised interpatient heartbeat classification","volume":"59","author":"Lannoy","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17472","DOI":"10.3390\/s131217472","article-title":"Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges","volume":"13","author":"Banaee","year":"2013","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1109\/TITB.2010.2047865","article-title":"A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing","volume":"14","author":"Oresko","year":"2010","journal-title":"IEEE Trans. Inf. Technol. B"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/TITB.2010.2047401","article-title":"An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation","volume":"14","author":"Lin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ijcard.2010.08.038","article-title":"A new telemonitoring system intended for chronic heart failure patients using mobile telephone technology\u2014Feasibility study","volume":"153","author":"Winkler","year":"2011","journal-title":"Int. J. Cardiol."},{"key":"ref_14","first-page":"369","article-title":"SPINE-HRV: A BSN-based toolkit for heart rate variability analysis in the time-domain","volume":"75","author":"Andreoli","year":"2010","journal-title":"Wearable Auton. Biomed. Devices Syst. Smart Environ. (Lect. Notes Electr. Eng.)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/TBME.2011.2175729","article-title":"Automatic motion and noise artifact detection in holter ECG data using empirical mode decomposition and statistical approaches","volume":"59","author":"Lee","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TBME.2007.891950","article-title":"Transition detection in body movement activities for wearable ECG","volume":"54","author":"Pawar","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TBME.2006.889186","article-title":"Body movement activity recognition for ambulatory cardiac monitoring","volume":"54","author":"Pawar","year":"2007","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1097\/00003246-199406000-00017","article-title":"Crying wolf: False alarms in a pediatric intensive care unit","volume":"22","author":"Lawless","year":"1994","journal-title":"Crit. Care. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1097\/00003246-199704000-00010","article-title":"Poor prognosis for existing monitors in the intensive care unit","volume":"25","author":"Tsien","year":"1997","journal-title":"Crit. Care Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1186\/cc12525","article-title":"Patient monitoring alarms in the ICU and in the operating room","volume":"17","author":"Schmid","year":"2013","journal-title":"Crit. Care"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/51.932724","article-title":"The impact of the MIT-BIH arrhythmia database","volume":"20","author":"George","year":"2001","journal-title":"IEEE Eng. Med. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Clifford, G.D., and Moody, G.B. (2012). Signal quality in cardiorespiratory monitoring. Physiol. Meas., 33.","DOI":"10.1088\/0967-3334\/33\/9\/E01"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1088\/0967-3334\/33\/9\/1449","article-title":"QRS detection based ECG quality assessment","volume":"33","author":"Hayn","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1088\/0967-3334\/33\/9\/1419","article-title":"Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms","volume":"33","author":"Clifford","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.jbi.2008.03.003","article-title":"Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform","volume":"41","author":"Aboukhalil","year":"2008","journal-title":"J. Biomed. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.jelectrocard.2012.07.015","article-title":"Signal quality and data fusion for false alarm reduction in the intensive care unit","volume":"45","author":"Li","year":"2012","journal-title":"J. Electrocardiol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, G.Z. (2006). Body Sensor Networks, Springer-Verlag.","DOI":"10.1007\/1-84628-484-8"},{"key":"ref_28","unstructured":"(1999). ANSI\/AAMI\/ISO EC57:1998\/(R)2008 Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms, Association for the Advancement of Medical Instrumentation."},{"key":"ref_29","first-page":"251","article-title":"Electrocardiogram monitoring","volume":"46","author":"Dash","year":"2002","journal-title":"Indian J. Anaesth."},{"key":"ref_30","unstructured":"Barill, T.P. (2005). The Six Second ECG: A Practical Guidebook to Basic ECG Interpretation, SkillStat Learning Inc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Simon, F., Martinez, J.P., Laguna, P., Grinsven, B.V., Rutten, C., and Houben, R. (2007, January 23\u201326). Impact of sampling rate reduction on automatic ECG delineation. Lyon, France.","DOI":"10.1109\/IEMBS.2007.4352858"},{"key":"ref_32","unstructured":"Rooijakkers, M.J. (2010, January 23). Design space exploration for scalable R-peak detection. Eindhoven, The Netherlands."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/LSP.2013.2254475","article-title":"An ultra-low power QRS complex detection algorithm based on down-sampling wavelet transform","volume":"20","author":"Zou","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_34","unstructured":"Clifford, G.D., Azuaje, F., and McSharry, P. (2006). Advanced Methods and Tools for ECG Data Analysis, Artech House."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Chen, H.C., and Chen, S.W. (2003, January 21\u201324). A moving average based filtering system with its application to real-time QRS detection. Chalkidiki, Greece.","DOI":"10.1109\/CIC.2003.1291223"},{"key":"ref_36","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2005). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann\/Elsevier. [3rd ed.]."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"258","DOI":"10.4015\/S101623720500038X","article-title":"A novel QRS detection algorithm applied to the analysis for heart rate variability of patients with sleep apnea","volume":"17","author":"Lee","year":"2005","journal-title":"Biomed. Eng. Appl. Basic Commun."},{"key":"ref_38","unstructured":"So, H.H., and Chan, K.L. (November, January 30). Development of QRS detection method for real-time ambulatory cardiac monitor. Chicago, IL, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A real-time QRS detection algorithm","volume":"32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.artmed.2004.03.007","article-title":"An arrhythmia classification system based on the RR-interval signal","volume":"33","author":"Tsipouras","year":"2005","journal-title":"Artif. Intell. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"391","DOI":"10.3233\/BME-130823","article-title":"Toward continuous ambulatory monitoring using a wearable and wireless ECG-recording system: A study on the effects of signal quality on arrhythmia detection","volume":"24","author":"Tanantong","year":"2014","journal-title":"Bio-Med. Mater. Eng."},{"key":"ref_42","first-page":"158","article-title":"Automatic signal appraisal for unobtrusive ECG measurements","volume":"12","author":"Schumm","year":"2010","journal-title":"Int. J. Bioelectromagn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MPRV.2011.40","article-title":"ECG monitoring in an airplane seat: Appraising the signal quality","volume":"11","author":"Schumm","year":"2012","journal-title":"IEEE Pervasive Comput."},{"key":"ref_44","first-page":"2377","article-title":"A hybrid framework for real-time continuous monitoring with body sensor networks","volume":"9","author":"Theekakul","year":"2013","journal-title":"Int. J. Innov. Comput. Inf. Control."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1088\/0967-3334\/26\/1\/011","article-title":"Premature ventricular contraction classification by the kth nearest-neighbours rule","volume":"26","author":"Christov","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"876","DOI":"10.1016\/j.medengphy.2005.12.010","article-title":"Comparative study of morphological and time-frequency ECG descriptors for heartbeat classification","volume":"28","author":"Chiristov","year":"2006","journal-title":"Med. Eng. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Begum, S., Islam, M.S., Ahmed, M.U., and Funk, P. (2011, January 14\u201317). K-NN based interpolation to handle artifacts for heart rate variability analysis. Bilbao, Spain.","DOI":"10.1109\/ISSPIT.2011.6151593"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/TBCAS.2011.2160540","article-title":"Sensor positioning for activity recognition using wearable accelerometers","volume":"5","author":"Atallah","year":"2011","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/TBME.2014.2307069","article-title":"Feature selection and activity recognition system using a single triaxial accelerometer","volume":"61","author":"Gupta","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1093\/eurheartj\/ehi708","article-title":"Heart rate response during exercise test and cardiovascular mortality in middle-aged men","volume":"27","author":"Savonen","year":"2006","journal-title":"Eur. Heart J."},{"key":"ref_51","unstructured":"Clifford, G.D., Lopez, D., Li, Q., and Rezek, I. (2011, January 18\u201321). Signal quality indices and data fusion for determinig acceptability of eletrocardiograms collected in noisy ambulatory environments. Hangzhou, China."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2748","DOI":"10.1109\/TBME.2010.2093898","article-title":"Life-threatening arrhythmia verification in ICU patients using the joint cardiovascular dynamical model and a Bayesian filter","volume":"58","author":"Sayadi","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.jelectrocard.2014.07.016","article-title":"False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal","volume":"47","author":"Bai","year":"2014","journal-title":"J. Electrocardiol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","article-title":"Multiparameter intelligent monitoring in intensive care II (MIMICII): A public-access ICU database","volume":"39","author":"Saeed","year":"2011","journal-title":"Crit. Care Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"12844","DOI":"10.3390\/s120912844","article-title":"A real-time cardiac arrhythmia classification system with wearable sensor networks","volume":"12","author":"Hu","year":"2012","journal-title":"Sensors"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.1109\/JBHI.2013.2282934","article-title":"Quality of the wireless electrocardiogram signal during physical exercise in different age groups","volume":"18","author":"Takalokastari","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/3952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:42:28Z","timestamp":1760215348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/3952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,9]]},"references-count":56,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,2]]}},"alternative-id":["s150203952"],"URL":"https:\/\/doi.org\/10.3390\/s150203952","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,2,9]]}}}