<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03753139</url>
  </required_header>
  <id_info>
    <org_study_id>KUH507P002</org_study_id>
    <nct_id>NCT03753139</nct_id>
  </id_info>
  <brief_title>Identification of Arrhythmias With One-time ECG</brief_title>
  <official_title>Identification of Arrhythmias With One-time ECG</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Kuopio University Hospital</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>University of Eastern Finland</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Kuopio University Hospital</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      In Western countries, every sixth person in their lifetime and 15,000 people in Finland have&#xD;
      a new stroke each year. About every fourth stroke is based on cardiac embolism. Atrial&#xD;
      fibrillation causes formation of thrombi in the left atrium with ensuing embolization in the&#xD;
      cerebral and peripheral circulation.&#xD;
&#xD;
      This study investigates the suitability of measurement techniques and new calculation methods&#xD;
      used in sport/wellness technology for the screening and diagnosis of atrial fibrillation and&#xD;
      other arrhythmias. New measurement technologies, the one-time ECG measurement and pulse&#xD;
      wristband measurement, are studied for their characteristics, data quality and rhythm&#xD;
      recognition. Identifying latent arrhythmias with new self-monitoring technologies can&#xD;
      significantly reduce the number of strokes (the latent arrhythmias causes about 25% of&#xD;
      strokes).&#xD;
&#xD;
      The research will be accomplished in cooperation with the Kuopio University Hospital&#xD;
      Emergency Department, the Heart Center, the Department of Applied Physics of the University&#xD;
      of Eastern Finland and Heart2Save Ltd.&#xD;
&#xD;
      The results of the research project will be published in the scientific journals of medicine&#xD;
      and medical technology and will be presented at scientific conferences of the respective&#xD;
      fields. The research results of the project can be utilized by all companies in the medical&#xD;
      technology industry, in particular companies that produce ECG measuring instruments and&#xD;
      companies that produce rhythm recognition software.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The research aims to solve the following medical problems:&#xD;
&#xD;
        1. Identifying and defining the type of atrial fibrillation is problematic due to its&#xD;
           spontaneous occurrence and latency.&#xD;
&#xD;
        2. Measurement devices for screening atrial fibrillation are poorly available.&#xD;
&#xD;
      The main research questions are:&#xD;
&#xD;
        1. Can a single-lead ECG measurement be used to detect arrhythmias&#xD;
&#xD;
             1. Find out the measurement locations (ECG leads) for single-lead ECG self-monitoring&#xD;
                and evaluate the impact of measurement locations on usability and signal quality.&#xD;
&#xD;
             2. Find out the measurement locations for single-lead ECG monitoring when the&#xD;
                measurement is performed by another person (nurse or relative) and evaluate the&#xD;
                impact of measurement locations on usability and signal quality.&#xD;
&#xD;
             3. Find out the reliability of single-lead ECG measurement for the identification of&#xD;
                atrial fibrillation.&#xD;
&#xD;
             4. Find out the reliability of single-lead ECG measurement for identification of rapid&#xD;
                (tachycardia) and slow heart rate (bradycardia).&#xD;
&#xD;
        2. Can a wristband based on optical pulse registration be able to obtain reliable&#xD;
           information about arrhythmias&#xD;
&#xD;
             1. Find out the usability of the pulse wristband for the identification of atrial&#xD;
                fibrillation.&#xD;
&#xD;
             2. Find out the usability of the pulse wristband for the identification of rapid&#xD;
                (tachycardia) and slow heart rate (bradycardia).&#xD;
&#xD;
      The purpose of the study's method development is to evaluate reliability of heart rate&#xD;
      measurement in single-lead ECG and pulse wave measurement with healthy and patients with&#xD;
      heart problems. The study develops computing methods based on lightweight measurement&#xD;
      technology to reliably identify the most common cardiac arrhythmia, atrial fibrillation. The&#xD;
      diagnosis and treatment of atrial fibrillation are decisive factors for preventing strokes.&#xD;
&#xD;
      Research patients have already undergone a 12-channel clinical ECG registration included in&#xD;
      the normal treatment process. This 12-channel ECG is used for the identification of patients&#xD;
      suitable for research and dividing to subgroups (normal rhythm, atrial fibrillation, rapid or&#xD;
      slow rhythm).&#xD;
&#xD;
      In actual study measurements, a Holter-ECG device is attached on patient's chest using five&#xD;
      wet electrodes to be used as golden standard for rhythm monitoring. The lightweight&#xD;
      measurement methods are compared with the result of the Holter-ECG registration. In addition,&#xD;
      photopletysmogram is placed on patient's wrist for PPG registration. Figure 1 shows&#xD;
      illustrative examples of the study's measurements.&#xD;
&#xD;
      The researcher measures one minute recordings from two different measuring positions (flank&#xD;
      and chest) with a one-time ECG measuring device. After that, the patient performs one minute&#xD;
      self-monitoring measurements with one-time ECG device from the all three positions (thumbs,&#xD;
      flank and chest) and also from the chest with jewel-ECG.&#xD;
&#xD;
      The study compares the ability of these lightweight measurement methods to detect different&#xD;
      heart rhythms compared to the Holter registration.&#xD;
&#xD;
      The devices used for the measurement are:&#xD;
&#xD;
        1. Faros 360 EKG sensor with wet electrodes (figure 1, device 1) (Mega Elektroniikka,&#xD;
           http://www.megaemg.com/ Kuopio Suomi). Faros 360 Holter is CE and FDA 510(k) cleared&#xD;
           class 2a medical device, which is attached to the patient's chest with five single-use&#xD;
           wet electrodes.&#xD;
&#xD;
        2. Suunto Movesense one-time ECG device (Suunto Oy, http://www.suunto.com Vantaa Suomi).&#xD;
           Movesense is CE cleared consumer device, which is used with two dy electrodes to the ECG&#xD;
           measurement (Figure 1 devices 2 and 3). Movesense case; jewel and one-time ECG case.&#xD;
&#xD;
             1. In the previous study (Afib24h), Valvira was reported and the research received&#xD;
                permission for the clinical device study (Movesense + chest strap combination).&#xD;
&#xD;
             2. For this study, Valira is reported for a clinical device study (Movesense +&#xD;
                one-time ECG device combination)&#xD;
&#xD;
        3. Empatica E4 activity bracelet (Empatica Ltd http://www.empatica.com Milan Italia), which&#xD;
           is CE cleared consumer device. Empatica E4 is also a photopletysmogram, which measures&#xD;
           optically the amount of blood circulating in the blood vessel (Figure 1 device 4).&#xD;
&#xD;
        4. Samsung Gear S3 wearable (Samsung Electronics, Co., Ltd., www.samsung.com Soul&#xD;
           Etelä-Korea) which is CE cleared consumer device. Gear S3 is also a photopletysmogram,&#xD;
           which measures optically the amount of blood circulating in the blood vessel.&#xD;
&#xD;
      The researcher attaches devices to the patient. After that, the researcher starts a 10-minute&#xD;
      registration with Faros 360 (device 1) and Empatica E4 (device 4) devices. During a 10-minute&#xD;
      measurement, the researcher measures one minute recordings from two different measuring&#xD;
      positions with the Movesense ECG (device 2) 1. from the chest, sternum perpendicularly 2.&#xD;
      from the chest, along the sternum. The patient then carries out one minute self-monitoring&#xD;
      measurements 1. from the chest, sternum perpendicularly 2. from the chest, along the sternum&#xD;
      3. from the lower part of the flank 4. from thumbs and 5. from the chest with a jewel-ECG.&#xD;
&#xD;
      Heart rate detection by ECG measurement is most commonly done by the detection of QRS&#xD;
      complexes. Numerous of these QRS detectors have been developed in recent decades. ECG&#xD;
      measurement with dry electrodes involves considerably more movement disturbances, compared to&#xD;
      the wet electrode measurements, as even the small movements of the device induce major&#xD;
      changes to the ECG signal. In addition, especially when using thumbs as a measurement points,&#xD;
      the EMG noise from the muscles is remarkably high compared to the wet electrode measurements.&#xD;
&#xD;
      This project utilizes the methods developed in the earlier mobile-ECG-project for noise and&#xD;
      QRS detection to allow reliably detection of QRS complexes and heart rate irregularities in&#xD;
      the dry electrode measurements.&#xD;
&#xD;
      In this study, the previously developed heart rate detection methods are validated by study's&#xD;
      measurements of normal sinus rhythm, atrial fibrillation, and slow (bradycardia) and rapid&#xD;
      (tachycardia) heart rate.&#xD;
&#xD;
      This study examines capability of pulse detection in detection of atrial fibrillation. The&#xD;
      photopletysmogram measures the absorption of light in the tissue. The absorption of light&#xD;
      into the blood is greater than the absorption into the surrounding tissue. When the heart&#xD;
      beats, capillaries expand and contract based on blood volume changes. Photopletysmography&#xD;
      allows the heart rate measurement by detecting changes in absorption.&#xD;
&#xD;
      Photopletysmgram, like a mobile-ECG device, is particularly sensitive to motion, even the&#xD;
      small motion of led/photodiode induce major change in light intensity.&#xD;
&#xD;
      Also, physiological changes cause a disturbance in heart rate measurement, for example, as&#xD;
      the vascular elasticity changes, the pulse time changes, resulting a disturbance in&#xD;
      measurement.&#xD;
&#xD;
      Unlike the high-frequency pierced QRS complex, the pulse wave is a low-frequency up-down&#xD;
      variation, which causes its own challenges for accurate heart rate measurement.&#xD;
&#xD;
      The atriums work insufficiently in atrial fibrillation therefore the ventricles are not&#xD;
      completely filled with blood. In addition, atrial fibrillation causes the irregular&#xD;
      conduction of impulses from atriums to the ventricles leading to pulse irregularity. The&#xD;
      amount of blood pumped varies from one stroke to stroke, which makes the pulse wave detection&#xD;
      challenging.&#xD;
&#xD;
      This project develops methods for accurate heart rate measurement from a pulse wave series.&#xD;
&#xD;
      The method development aims to take account of disturbances due to the motion of the meter,&#xD;
      pulse wave irregularities typical of atrial fibrillation, and the challenges of slow&#xD;
      (bradycardia) and rapid (tachycardia) heart rate detection.&#xD;
&#xD;
      The main goal of the method development is to determine the pulse so precisely that pulse&#xD;
      irregularity due to atrial fibrillation can be distinguished from normal sinus rhythm and&#xD;
      reliably detect rapid and slow heart rhythms.&#xD;
&#xD;
      In atrial fibrillation, electrical impulses conduct randomly to the ventricles, causing the&#xD;
      heart rate to be irregular and uneven. A large campaign by the Heart Association &quot;Feel your&#xD;
      pulse - prevent the stroke&quot; is based on heart rate or pulse recognition. Pulse recognition is&#xD;
      of course the cheapest method to detect atrial fibrillation, but this method produces a large&#xD;
      number of false positives. By ECG measurement, the detection of atrial fibrillation is much&#xD;
      more reliable. Automated atrial fibrillation detection algorithms have been developed for&#xD;
      this purpose.&#xD;
&#xD;
      Identification of the atrium activation in long-term Holter-ECG measurements is generally&#xD;
      very challenging due to the poor signal-noise-ratio (motion, muscle-artefacts and partly&#xD;
      overlapping much stronger ventricular activity). For this reason, most atrial fibrillation&#xD;
      detection algorithms are based on the identification of pulse irregularity. For&#xD;
      parametrization of the irregularity of the heart rate (RR-interval) has been introduced&#xD;
      several relatively simple but reliable time-level methods. As an example, A RdR-based method&#xD;
      wherein the RR intervals (heart rate) are represented as a function of consecutive RR&#xD;
      interval changes (heart rate change) (Lian et al. 2011). The RdR-graph defines the&#xD;
      fragmentation of the pattern resulting from irregular heart rate changes. In addition, there&#xD;
      are methods that estimate RR time series internal coherence (Lee et al. 2011). Various&#xD;
      nonlinear methods have also been introduced to parametrization of the heart rate variation,&#xD;
      enabling the dynamics of the heart rate variation to be described more broadly (without&#xD;
      limitation of the linearity assumption). One class of nonlinear methods are different entropy&#xD;
      quantities, these are particularly interesting for the identification of atrial fibrillation&#xD;
      and the irregular heart rate. Entropy quantities can be used to estimate the regularity and&#xD;
      predictability of the RR time series. Typically, the reliable calculation of the entropy&#xD;
      quantities requires a relatively long measurement time, but also entropy quantities that are&#xD;
      suitable for the analysis of short measurements have been introduced (Lake &amp; Moorman 2011).&#xD;
&#xD;
      This research project develops new atrial fibrillation detection algorithms for the&#xD;
      mobile-ECG measurement and pulse wave measurement on the basis of already existing methods.&#xD;
      Algorithms must take into account atrial and ventricular premature complexes. Ignoring of&#xD;
      these increases the irregularity of the RR time series and thus increases the number of false&#xD;
      positive atrial fibrillation.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">November 28, 2018</start_date>
  <completion_date type="Actual">July 31, 2020</completion_date>
  <primary_completion_date type="Actual">February 28, 2020</primary_completion_date>
  <study_type>Observational</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <observational_model>Case-Control</observational_model>
    <time_perspective>Prospective</time_perspective>
  </study_design_info>
  <primary_outcome>
    <measure>Heart rhythm monitoring with single-lead ECG</measure>
    <time_frame>30 minutes</time_frame>
    <description>Sensitivity and Specificity for the atrial fibrillation detection</description>
  </primary_outcome>
  <number_of_groups>2</number_of_groups>
  <enrollment type="Actual">260</enrollment>
  <condition>Atrial Fibrillation</condition>
  <condition>Heart Rate Fast</condition>
  <condition>Heart Rate Low</condition>
  <arm_group>
    <arm_group_label>Atrial fibrillation</arm_group_label>
    <description>Patients with atrial fibrillation as recorded by Holter</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Sinus rhythm</arm_group_label>
    <description>Patients with sinus rhythm as recorded by Holter</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>Heart rhythm monitoring with portable device</intervention_name>
    <description>The study compares the ability of lightweight measurement methods to detect different heart rhythms compared to the Holter registration.&#xD;
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.&#xD;
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.&#xD;
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.&#xD;
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.</description>
    <arm_group_label>Atrial fibrillation</arm_group_label>
    <arm_group_label>Sinus rhythm</arm_group_label>
  </intervention>
  <eligibility>
    <study_pop>
      <textblock>
        A total of 300 patients will be recruited in the research: 100 patients with normal sinus&#xD;
        rhythm, 100 with atrial fibrillation, 50 with rapid rhythm (tachycardia, resting heart rate&#xD;
        &gt; 100 bpm) and 50 slow rhythms (bradycardia, resting heart rate&lt; 50bpm).&#xD;
      </textblock>
    </study_pop>
    <sampling_method>Non-Probability Sample</sampling_method>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Patients treated for any reason in the emergency department of Kuopio University&#xD;
             Hospital.&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  body mass index (BMI) over 35, implanted heart pacemaker device and a medical&#xD;
             condition requiring immediate treatment that would be delayed by the study&#xD;
             measurements.&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Tero J Martikainen, MD. PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>Kuopio University Hospital</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Kuopio university hospital</name>
      <address>
        <city>Kuopio</city>
        <state>Eastern-Finland</state>
        <zip>70029</zip>
        <country>Finland</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Finland</country>
  </location_countries>
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  <verification_date>April 2021</verification_date>
  <study_first_submitted>November 22, 2018</study_first_submitted>
  <study_first_submitted_qc>November 22, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">November 26, 2018</study_first_posted>
  <last_update_submitted>April 29, 2021</last_update_submitted>
  <last_update_submitted_qc>April 29, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">April 30, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Atrial fibrillation; detection; portable device; algorithms</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Atrial Fibrillation</mesh_term>
    <mesh_term>Tachycardia</mesh_term>
    <mesh_term>Bradycardia</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03753139
org study id: KUH507P002
nct id: NCT03753139
lead sponsor: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
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.
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.
observational model: Case-Control
time perspective: Prospective
measure: Heart rhythm monitoring with single-lead ECG
time frame: 30 minutes
description: Sensitivity and Specificity for the atrial fibrillation detection
arm group label: Atrial fibrillation
description: Patients with atrial fibrillation as recorded by Holter
arm group label: Sinus rhythm
description: Patients with sinus rhythm as recorded by Holter
intervention type: Device
intervention name: Heart rhythm monitoring with portable device
description: 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.
arm group label: Atrial fibrillation
arm group label: Sinus rhythm
study pop: 
sampling method: Non-Probability Sample
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: No
last name: Tero J Martikainen, MD. PhD
role: Principal Investigator
affiliation: Kuopio University Hospital
facility: 
country: Finland
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PMID: 28038729
responsible party type: Sponsor
mesh term: Atrial Fibrillation
mesh term: Tachycardia
mesh term: Bradycardia
sharing ipd: No