Vol 13, No 2 (2018)
Cardiology Investigation
Published online: 2018-05-30
Is it possible to take an ECG photo and analyse it automatically with a smartphone?
DOI: 10.5603/FC.2018.0036
Folia Cardiologica 2018;13(2):190-195.
Abstract
Introduction. Nowadays there are no tools enabling fast digitalization and supporting interpretation of paper ECG recordings. Popularity and availability of devices like smartphone could be used in clinical practice as tools enhancing the interpretation of ECG recordings. The aim of the study is to determine possibilities of using free mobile application and smartphone in assessment of QRS frequency and electrical axis of the heart as a basis for more advanced analysis.
Materials and methods. Fifty recordings of 12-lead ECG at 25 mm/s generated by devices of various producers were qualified for the analysis. Each of the recordings was assessed as diagnostic by two cardiologists, who also measured the frequency of QRS complexes, electrical axis of the heart, duration of QT, amplitude and duration of QRS complexes and T waves. Afterwards, automatic interpretation of ECG recordings was performed with educational mobile application eEKG (available for free at AppStore and Google Play) and iPhone 5s. The pictures of ECG waveforms were taken according to instruction of the application. The results of expert assessment and automatic interpretation were compared. The 10% acceptable margin of error was established for assessment of frequency of QRS complexes by the application.
Results. Six hundred ECG waveforms (12 leads in every ECG recording) were analysed for frequency of QRS complexes and 50 ECG recordings were analyzed for electrical axis of the heart. The application qualified as diagnostic 573 (95.5%) attempts of QRS frequency assessment and 26 (52%) attempts of electrical axis of the heart assessment. The assessment was accurate in 82% of attempts for QRS complexes frequency assessment and in 96% of attempts for electrical axis of the heart assessment. Significant correlation was proven between QT duration, T wave amplitude, ratio of amplitudes of QRS complexes and T waves and effectiveness of automatic interpretation of ECG waveform.
Conclusions. Effective digitalisation and automatic interpretation of ECG recording in assessment of frequency of QRS complexes and electrical axis of the heart is possible with mobile application and smartphone type device.
Keywords: ECG interpretationsmartphonepicture recognition
References
- Urząd Komunikacji Elektronicznej, Raport o stanie rynku telekomunikacyjnego w 2016 roku, Warszawa, czerwiec 2017, https://www.uke.gov.pl/download/gfx/uke/pl/defaultaktualnosci/36/16/1/raport_o_stanie_rynku_telekomunikacyjnego_w_2016_roku_www.pdf (10.03.2018 r.).
- Meyer C, Fernández Gavela J, Harris M. Combining algorithms in automatic detection of QRS complexes in ECG signals. IEEE Trans Inf Technol Biomed. 2006; 10(3): 468–475.
- de Champlain F, Boothroyd LJ, Vadeboncoeur A, et al. Computerized interpretation of the prehospital electrocardiogram: predictive value for ST segment elevation myocardial infarction and impact on on-scene time. CJEM. 2014; 16(2): 94–105.
- Zidelmal Z, Amirou A, Adnane M, et al. QRS detection based on wavelet coefficients. Comput Methods Programs Biomed. 2012; 107(3): 490–496.
- Medscape Report. http://www.medscape.com/features/slideshow/public/technology-in-medicine#page=23 (10.03.2018 r.).
- International Standard ICE 60601-2-51, Medical electrical equipment. Part 2-51: Particular requirements for safety, including essential performance, of recording and analysing single channel and multichannel electrocardiographs. International Electrotechnical Commission, Genewa, 2003.
- de Lange FJ, Moya A, Sutton R, et al. Task Force for the Diagnosis and Management of Syncope, European Society of Cardiology (ESC), European Heart Rhythm Association (EHRA), Heart Failure Association (HFA); Heart Rhythm Society (HRS). Guidelines for the diagnosis and management of syncope (version 2009). Eur Heart J. 2009; 30(21): 2631–2671.
- Hwan Bae M, Hoon Lee J, Heon Yang D, et al. Erroneous computer electrocardiogram interpretation of atrial fibrillation and its clinical consequences. Clin Cardiol. 2012; 35(6): 348–353.
- Grabowski M, Cacko A, Opolski G. Telemedycyna w optymalizacji diagnostyki i leczenia chorych z ostrym zespołem wieńcowym — projekt Kardionet. In: Piotrowicz R, Różyłło W. ed. Telekardiologia, cz. 1.: Wybrane zastosowania kliniczne. TeleInterMed, Warszawa 2014: 39–46.
- Clark E, Sejersten M, Clemmensen P, et al. Effectiveness of electrocardiogram interpretation programs in the ambulance setting. Computers in Cardiology. 2009; 36: 117–120.
- Leutheuser H, Gradl S, Kugler P, et al. Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices. Conf Proc IEEE Eng Med Biol Soc. 2014; 2014: 2690–2693.
- Haeberlin A, Roten L, Schilling M, et al. Software-based detection of atrial fibrillation in long-term ECGs. Heart Rhythm. 2014; 11(6): 933–938.
- Hillson SD, Connelly DP, Liu Y. The effects of computer-assisted electrocardiographic interpretation on physicians' diagnostic decisions. Med Decis Making. 1995; 15(2): 107–112.
- Bogun F, Anh D, Kalahasty G, et al. Misdiagnosis of atrial fibrillation and its clinical consequences. Am J Med. 2004; 117(9): 636–642.
- Shameer K, Johnson KW, Glicksberg BS, et al. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018 [Epub ahead of print].
- Waldmann V, Bougouin W, Karam N, et al. Paris-SDEC investigators. Characteristics and clinical assessment of unexplained sudden cardiac arrest in the real-world setting: focus on idiopathic ventricular fibrillation. Eur Heart J. 2018 [Epub ahead of print].
- Jeroudi OM, Christakopoulos G, Christopoulos G, et al. Accuracy of remote electrocardiogram interpretation with the use of Google Glass technology. Am J Cardiol. 2015; 115(3): 374–377.
- Schaer R, Salamin F, Jimenez Del Toro OA, et al. Live ECG readings using Google Glass in emergency situations. Conf Proc IEEE Eng Med Biol Soc. 2015; 2015: 315–318.
- Rymuza B, Grodecki K, Kamiński J, et al. Holographic imaging during transcatheter aortic valve implantation procedure in bicuspid aortic valve stenosis. Kardiol Pol. 2017; 75(10): 1056.
