Identification of Arrhythmias With One-time ECG

In Western countries, every sixth person in their lifetime and 15,000 people in Finland have a new stroke each year. About every fourth stroke is based on cardiac embolism. Atrial fibrillation causes formation of thrombi in the left atrium with ensuing embolization in the cerebral and peripheral circulation.

This study investigates the suitability of measurement techniques and new calculation methods used in sport/wellness technology for the screening and diagnosis of atrial fibrillation and other arrhythmias. New measurement technologies, the one-time ECG measurement and pulse wristband measurement, are studied for their characteristics, data quality and rhythm recognition. Identifying latent arrhythmias with new self-monitoring technologies can significantly reduce the number of strokes (the latent arrhythmias causes about 25% of strokes).

The research will be accomplished in cooperation with the Kuopio University Hospital Emergency Department, the Heart Center, the Department of Applied Physics of the University of Eastern Finland and Heart2Save Ltd.

The results of the research project will be published in the scientific journals of medicine and medical technology and will be presented at scientific conferences of the respective fields. The research results of the project can be utilized by all companies in the medical technology industry, in particular companies that produce ECG measuring instruments and companies that produce rhythm recognition software.

Study Overview

• Status: Completed
• Conditions: Atrial Fibrillation; Heart Rate Fast; Heart Rate Low
• Intervention / Treatment: Device: Heart rhythm monitoring with portable device

Detailed Description

The research aims to solve the following medical problems:

1. Identifying and defining the type of atrial fibrillation is problematic due to its spontaneous occurrence and latency.
2. Measurement devices for screening atrial fibrillation are poorly available.

The main research questions are:

1. Can a single-lead ECG measurement be used to detect arrhythmias

   1. Find out the measurement locations (ECG leads) for single-lead ECG self-monitoring and evaluate the impact of measurement locations on usability and signal quality.
   2. Find out the measurement locations for single-lead ECG monitoring when the measurement is performed by another person (nurse or relative) and evaluate the impact of measurement locations on usability and signal quality.
   3. Find out the reliability of single-lead ECG measurement for the identification of atrial fibrillation.
   4. Find out the reliability of single-lead ECG measurement for identification of rapid (tachycardia) and slow heart rate (bradycardia).

2. Can a wristband based on optical pulse registration be able to obtain reliable information about arrhythmias

   1. Find out the usability of the pulse wristband for the identification of atrial fibrillation.
   2. Find out the usability of the pulse wristband for the identification of rapid (tachycardia) and slow heart rate (bradycardia).

The purpose of the study's method development is to evaluate reliability of heart rate measurement in single-lead ECG and pulse wave measurement with healthy and patients with heart problems. The study develops computing methods based on lightweight measurement technology to reliably identify the most common cardiac arrhythmia, atrial fibrillation. The diagnosis and treatment of atrial fibrillation are decisive factors for preventing strokes.

Research patients have already undergone a 12-channel clinical ECG registration included in the normal treatment process. This 12-channel ECG is used for the identification of patients suitable for research and dividing to subgroups (normal rhythm, atrial fibrillation, rapid or slow rhythm).

In actual study measurements, a Holter-ECG device is attached on patient's chest using five wet electrodes to be used as golden standard for rhythm monitoring. The lightweight measurement methods are compared with the result of the Holter-ECG registration. In addition, photopletysmogram is placed on patient's wrist for PPG registration. Figure 1 shows illustrative examples of the study's measurements.

The researcher measures one minute recordings from two different measuring positions (flank and chest) with a one-time ECG measuring device. After that, the patient performs one minute self-monitoring measurements with one-time ECG device from the all three positions (thumbs, flank and chest) and also from the chest with jewel-ECG.

The study compares the ability of these lightweight measurement methods to detect different heart rhythms compared to the Holter registration.

The devices used for the measurement are:

1. Faros 360 EKG sensor with wet electrodes (figure 1, device 1) (Mega Elektroniikka, http://www.megaemg.com/ Kuopio Suomi). Faros 360 Holter is CE and FDA 510(k) cleared class 2a medical device, which is attached to the patient's chest with five single-use wet electrodes.

2. Suunto Movesense one-time ECG device (Suunto Oy, http://www.suunto.com Vantaa Suomi). Movesense is CE cleared consumer device, which is used with two dy electrodes to the ECG measurement (Figure 1 devices 2 and 3). Movesense case; jewel and one-time ECG case.

   1. In the previous study (Afib24h), Valvira was reported and the research received permission for the clinical device study (Movesense + chest strap combination).
   2. For this study, Valira is reported for a clinical device study (Movesense + one-time ECG device combination)
3. Empatica E4 activity bracelet (Empatica Ltd http://www.empatica.com Milan Italia), which is CE cleared consumer device. Empatica E4 is also a photopletysmogram, which measures optically the amount of blood circulating in the blood vessel (Figure 1 device 4).
4. Samsung Gear S3 wearable (Samsung Electronics, Co., Ltd., www.samsung.com Soul Etelä-Korea) which is CE cleared consumer device. Gear S3 is also a photopletysmogram, which measures optically the amount of blood circulating in the blood vessel.

The researcher attaches devices to the patient. After that, the researcher starts a 10-minute registration with Faros 360 (device 1) and Empatica E4 (device 4) devices. During a 10-minute measurement, the researcher measures one minute recordings from two different measuring positions with the Movesense ECG (device 2) 1. from the chest, sternum perpendicularly 2. from the chest, along the sternum. The patient then carries out one minute self-monitoring measurements 1. from the chest, sternum perpendicularly 2. from the chest, along the sternum 3. from the lower part of the flank 4. from thumbs and 5. from the chest with a jewel-ECG.

Heart rate detection by ECG measurement is most commonly done by the detection of QRS complexes. Numerous of these QRS detectors have been developed in recent decades. ECG measurement with dry electrodes involves considerably more movement disturbances, compared to the wet electrode measurements, as even the small movements of the device induce major changes to the ECG signal. In addition, especially when using thumbs as a measurement points, the EMG noise from the muscles is remarkably high compared to the wet electrode measurements.

This project utilizes the methods developed in the earlier mobile-ECG-project for noise and QRS detection to allow reliably detection of QRS complexes and heart rate irregularities in the dry electrode measurements.

In this study, the previously developed heart rate detection methods are validated by study's measurements of normal sinus rhythm, atrial fibrillation, and slow (bradycardia) and rapid (tachycardia) heart rate.

This study examines capability of pulse detection in detection of atrial fibrillation. The photopletysmogram measures the absorption of light in the tissue. The absorption of light into the blood is greater than the absorption into the surrounding tissue. When the heart beats, capillaries expand and contract based on blood volume changes. Photopletysmography allows the heart rate measurement by detecting changes in absorption.

Photopletysmgram, like a mobile-ECG device, is particularly sensitive to motion, even the small motion of led/photodiode induce major change in light intensity.

Also, physiological changes cause a disturbance in heart rate measurement, for example, as the vascular elasticity changes, the pulse time changes, resulting a disturbance in measurement.

Unlike the high-frequency pierced QRS complex, the pulse wave is a low-frequency up-down variation, which causes its own challenges for accurate heart rate measurement.

The atriums work insufficiently in atrial fibrillation therefore the ventricles are not completely filled with blood. In addition, atrial fibrillation causes the irregular conduction of impulses from atriums to the ventricles leading to pulse irregularity. The amount of blood pumped varies from one stroke to stroke, which makes the pulse wave detection challenging.

This project develops methods for accurate heart rate measurement from a pulse wave series.

The method development aims to take account of disturbances due to the motion of the meter, pulse wave irregularities typical of atrial fibrillation, and the challenges of slow (bradycardia) and rapid (tachycardia) heart rate detection.

The main goal of the method development is to determine the pulse so precisely that pulse irregularity due to atrial fibrillation can be distinguished from normal sinus rhythm and reliably detect rapid and slow heart rhythms.

In atrial fibrillation, electrical impulses conduct randomly to the ventricles, causing the heart rate to be irregular and uneven. A large campaign by the Heart Association "Feel your pulse - prevent the stroke" is based on heart rate or pulse recognition. Pulse recognition is of course the cheapest method to detect atrial fibrillation, but this method produces a large number of false positives. By ECG measurement, the detection of atrial fibrillation is much more reliable. Automated atrial fibrillation detection algorithms have been developed for this purpose.

Identification of the atrium activation in long-term Holter-ECG measurements is generally very challenging due to the poor signal-noise-ratio (motion, muscle-artefacts and partly overlapping much stronger ventricular activity). For this reason, most atrial fibrillation detection algorithms are based on the identification of pulse irregularity. For parametrization of the irregularity of the heart rate (RR-interval) has been introduced several relatively simple but reliable time-level methods. As an example, A RdR-based method wherein the RR intervals (heart rate) are represented as a function of consecutive RR interval changes (heart rate change) (Lian et al. 2011). The RdR-graph defines the fragmentation of the pattern resulting from irregular heart rate changes. In addition, there are methods that estimate RR time series internal coherence (Lee et al. 2011). Various nonlinear methods have also been introduced to parametrization of the heart rate variation, enabling the dynamics of the heart rate variation to be described more broadly (without limitation of the linearity assumption). One class of nonlinear methods are different entropy quantities, these are particularly interesting for the identification of atrial fibrillation and the irregular heart rate. Entropy quantities can be used to estimate the regularity and predictability of the RR time series. Typically, the reliable calculation of the entropy quantities requires a relatively long measurement time, but also entropy quantities that are suitable for the analysis of short measurements have been introduced (Lake & Moorman 2011).

This research project develops new atrial fibrillation detection algorithms for the mobile-ECG measurement and pulse wave measurement on the basis of already existing methods. Algorithms must take into account atrial and ventricular premature complexes. Ignoring of these increases the irregularity of the RR time series and thus increases the number of false positive atrial fibrillation.

• Study Type: Observational
• Enrollment (Actual): 260

Contacts and Locations

Study Locations

• Finland
  • Eastern-Finland
    • Kuopio, Eastern-Finland, Finland, 70029
      • Kuopio University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

A total of 300 patients will be recruited in the research: 100 patients with normal sinus rhythm, 100 with atrial fibrillation, 50 with rapid rhythm (tachycardia, resting heart rate > 100 bpm) and 50 slow rhythms (bradycardia, resting heart rate< 50bpm).

Description

Inclusion Criteria:

• Patients treated for any reason in the emergency department of Kuopio University Hospital.

Exclusion Criteria:

• body mass index (BMI) over 35, implanted heart pacemaker device and a medical condition requiring immediate treatment that would be delayed by the study measurements.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Atrial fibrillation; Patients with atrial fibrillation as recorded by Holter	Device: Heart rhythm monitoring with portable device; The study compares the ability of lightweight measurement methods to detect different heart rhythms compared to the Holter registration.; Faros 360 ECG sensor with wet electrodes. Faros 360 Holter is CE and FDA 510 cleared class 2a medical device, which is attached to the patient's chest with five single-use wet electrodes.; Suunto Movesense one-time ECG device (Suunto Oy, http://www.suunto.com Vantaa Finland). Movesense is CE cleared consumer device, which is used with two dry electrodes to the ECG measurement.; Empatica E4 activity bracelet (Empatica Ltd http://www.empatica.com Milan, Italy), which is CE cleared consumer device. Empatica E4 is a photopletysmogram which measures optically the amount of blood circulating in the blood vessel.; Samsung Gear S3 wearable (Samsung Electronics, Co., Soul, South Korea) which is CE cleared consumer device. Gear S3 is a photopletysmogram, which measures optically the amount of blood circulating in the blood vessel.
Sinus rhythm; Patients with sinus rhythm as recorded by Holter	Device: Heart rhythm monitoring with portable device; The study compares the ability of lightweight measurement methods to detect different heart rhythms compared to the Holter registration.; Faros 360 ECG sensor with wet electrodes. Faros 360 Holter is CE and FDA 510 cleared class 2a medical device, which is attached to the patient's chest with five single-use wet electrodes.; Suunto Movesense one-time ECG device (Suunto Oy, http://www.suunto.com Vantaa Finland). Movesense is CE cleared consumer device, which is used with two dry electrodes to the ECG measurement.; Empatica E4 activity bracelet (Empatica Ltd http://www.empatica.com Milan, Italy), which is CE cleared consumer device. Empatica E4 is a photopletysmogram which measures optically the amount of blood circulating in the blood vessel.; Samsung Gear S3 wearable (Samsung Electronics, Co., Soul, South Korea) which is CE cleared consumer device. Gear S3 is a photopletysmogram, which measures optically the amount of blood circulating in the blood vessel.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Heart rhythm monitoring with single-lead ECG	Sensitivity and Specificity for the atrial fibrillation detection	30 minutes

Collaborators and Investigators

• Sponsor: Kuopio University Hospital
• Collaborators: University of Eastern Finland
• Investigators: Principal Investigator: Tero J Martikainen, MD. PhD, Kuopio University Hospital

Publications and helpful links

General Publications

• Amann A, Tratnig R, Unterkofler K. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators. Biomed Eng Online. 2005 Oct 27;4:60. doi: 10.1186/1475-925X-4-60.
• Barro S, Ruiz R, Cabello D, Mira J. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J Biomed Eng. 1989 Jul;11(4):320-8. doi: 10.1016/0141-5425(89)90067-8.
• Cabello D, Barro S, Salceda JM, Ruiz R, Mira J. Fuzzy K-nearest neighbor classifiers for ventricular arrhythmia detection. Int J Biomed Comput. 1991 Feb;27(2):77-93. doi: 10.1016/0020-7101(91)90089-w.
• Chen SW. A two-stage discrimination of cardiac arrhythmias using a total least squares-based prony modeling algorithm. IEEE Trans Biomed Eng. 2000 Oct;47(10):1317-27. doi: 10.1109/10.871404.
• al-Fahoum AS, Howitt I. Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Med Biol Eng Comput. 1999 Sep;37(5):566-73. doi: 10.1007/BF02513350.
• Fuster V, Ryden LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, Halperin JL, Le Heuzey JY, Kay GN, Lowe JE, Olsson SB, Prystowsky EN, Tamargo JL, Wann S; Task Force on Practice Guidelines, American College of Cardiology/American Heart Association; Committee for Practice Guidelines, European Society of Cardiology; European Heart Rhythm Association; Heart Rhythm Society. ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation-executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Revise the 2001 Guidelines for the Management of Patients with Atrial Fibrillation). Eur Heart J. 2006 Aug;27(16):1979-2030. doi: 10.1093/eurheartj/ehl176. No abstract available. Erratum In: Eur Heart J. 2007 Aug;28(16):2046.
• Ge D, Srinivasan N, Krishnan SM. Cardiac arrhythmia classification using autoregressive modeling. Biomed Eng Online. 2002 Nov 13;1:5. doi: 10.1186/1475-925x-1-5.
• Jekova I. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. Physiol Meas. 2000 Nov;21(4):429-39. doi: 10.1088/0967-3334/21/4/301.
• Lake DE, Moorman JR. Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. Am J Physiol Heart Circ Physiol. 2011 Jan;300(1):H319-25. doi: 10.1152/ajpheart.00561.2010. Epub 2010 Oct 29.
• Lee J, Nam Y, McManus DD, Chon KH. Time-varying coherence function for atrial fibrillation detection. IEEE Trans Biomed Eng. 2013 Oct;60(10):2783-93. doi: 10.1109/TBME.2013.2264721. Epub 2013 May 22.
• Li C, Zheng C, Tai C. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng. 1995 Jan;42(1):21-8. doi: 10.1109/10.362922.
• Lian J, Wang L, Muessig D. A simple method to detect atrial fibrillation using RR intervals. Am J Cardiol. 2011 May 15;107(10):1494-7. doi: 10.1016/j.amjcard.2011.01.028. Epub 2011 Mar 17.
• Lipponen JA, Tarvainen MP, Laitinen T, Lyyra-Laitinen T, Karjalainen PA. A principal component regression approach for estimation of ventricular repolarization characteristics. IEEE Trans Biomed Eng. 2010 May;57(5):1062-9. doi: 10.1109/TBME.2009.2037492. Epub 2010 Feb 5.
• Lipponen JA, Kemppainen J, Karjalainen PA, Laitinen T, Mikola H, Karki T, Tarvainen MP. Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects. Physiol Meas. 2011 Jun;32(6):649-60. doi: 10.1088/0967-3334/32/6/003. Epub 2011 Apr 20.
• Lipponen JA, Tarvainen MP. Principal component model for maternal ECG extraction in fetal QRS detection. Physiol Meas. 2014 Aug;35(8):1637-48. doi: 10.1088/0967-3334/35/8/1637. Epub 2014 Jul 29.
• Meretoja A, Roine RO, Kaste M, Linna M, Juntunen M, Erila T, Hillbom M, Marttila R, Rissanen A, Sivenius J, Hakkinen U. Stroke monitoring on a national level: PERFECT Stroke, a comprehensive, registry-linkage stroke database in Finland. Stroke. 2010 Oct;41(10):2239-46. doi: 10.1161/STROKEAHA.110.595173. Epub 2010 Aug 26.
• Syvaoja S, Castren M, Mantyla P, Rissanen TT, Kivela A, Uusaro A, Jantti H. The feasibility of recognizing the heart rhythm with an automated external defibrillator from an area the size of a mobile phone. Eur J Emerg Med. 2016 Apr;23(2):102-7. doi: 10.1097/MEJ.0000000000000214.
• Tarvainen MP, Ranta-Aho PO, Karjalainen PA. An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng. 2002 Feb;49(2):172-5. doi: 10.1109/10.979357.
• Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-Aho PO, Karjalainen PA. Kubios HRV--heart rate variability analysis software. Comput Methods Programs Biomed. 2014;113(1):210-20. doi: 10.1016/j.cmpb.2013.07.024. Epub 2013 Aug 6.
• Thakor NV, Zhu YS, Pan KY. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm. IEEE Trans Biomed Eng. 1990 Sep;37(9):837-43. doi: 10.1109/10.58594.
• Zhang XS, Zhu YS, Thakor NV, Wang ZZ. Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans Biomed Eng. 1999 May;46(5):548-55. doi: 10.1109/10.759055.
• Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, Castella M, Diener HC, Heidbuchel H, Hendriks J, Hindricks G, Manolis AS, Oldgren J, Alexandru Popescu B, Schotten U, Van Putte B, Vardas P. 2016 ESC Guidelines for the Management of Atrial Fibrillation Developed in Collaboration With EACTS. Rev Esp Cardiol (Engl Ed). 2017 Jan;70(1):50. doi: 10.1016/j.rec.2016.11.033. No abstract available. Erratum In: Rev Esp Cardiol (Engl Ed). 2017 Nov;70(11):1031. English, Spanish.
• Valiaho ES, Kuoppa P, Lipponen JA, Hartikainen JEK, Jantti H, Rissanen TT, Kolk I, Pohjantahti-Maaroos H, Castren M, Halonen J, Tarvainen MP, Santala OE, Martikainen TJ. Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection. Front Physiol. 2021 May 7;12:654555. doi: 10.3389/fphys.2021.654555. eCollection 2021.
• Santala OE, Lipponen JA, Jäntti H, Rissanen TT, Halonen J, Kolk I, Pohjantähti-Maaroos H, Tarvainen MP, Väliaho ES, Hartikainen J, Martikainen T. Necklace-embedded electrocardiogram for the detection and diagnosis of atrial fibrillation. Clin Cardiol. 2021 May;44(5):620-626. doi: 10.1002/clc.23580. Epub 2021 Feb 25.

Study record dates

Study Major Dates

• Study Start (Actual): November 28, 2018
• Primary Completion (Actual): February 28, 2020
• Study Completion (Actual): July 31, 2020

Study Registration Dates

• First Submitted: November 22, 2018
• First Submitted That Met QC Criteria: November 22, 2018
• First Posted (Actual): November 26, 2018

Study Record Updates

• Last Update Posted (Actual): April 30, 2021
• Last Update Submitted That Met QC Criteria: April 29, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Keywords: Atrial fibrillation; detection; portable device; algorithms
• Additional Relevant MeSH Terms: Pathologic Processes; Heart Diseases; Cardiovascular Diseases; Arrhythmias, Cardiac; Cardiac Conduction System Disease; Atrial Fibrillation; Bradycardia; Tachycardia
• Other Study ID Numbers: KUH507P002

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No