Quality of the Wireless Electrocardiogram Signal During Physical Exercise in Different Age Groups

Takalokastari, Tiina; Alasaarela, Esko; Jämsä, Timo

doi:10.1109/jbhi.2013.2282934

Cited by 17 publications

(13 citation statements)

References 27 publications

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“…The ECG signal at position LS on the treadmill was acceptable in the range between 58.88 % and 62.18 %, and on the cycle ergometer signal was acceptable in the range between 75.92 % and 87.79 %. These results show significant improvement of the results presented in the study by Takalokastari et al [26], where it was shown that on the treadmill during running 60 % of the signal was of moderate or good quality, while on the cycle ergometer 73 % of the signal was of moderate or good quality. They reported that 79 % of the ECG signal on the treadmill was of moderate or good quality in the Nordic walking test.…”

Section: Discussionsupporting

confidence: 77%

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Siraiy

Trobec

Ilić

2019

Measurement Science Review

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The aim of this study was to evaluate the quality of the ECG signal, obtained from a telemetric body-sensor device during a maximum stress test on an ergometer. Twenty-three subjects, 13 males, were included in the study (20.56±1.19 years). Two different sensor positions were verified on each subject by the concurrent use of two ECG sensors. Each subject participated in four exercise stress tests: two on a treadmill and two on a cycle ergometer. In the first test, both sensors were attached to self-adhesive skin electrodes on the body, while in the second test the sensors were additionally fixed with self-adhesive tapes. The measurements were compared on both ergometers, in terms of the ECG sensors’ positions and the methods used for the sensors’ fixation. The results showed a significant difference in the running speed that provides an assessable ECG signal between the non-fixed and the fixed sensors at position left inferior (p = 0.000), as well as between the positions left inferior and left superior in the first (p = 0.019), and in the second test (p = 0.000) on the treadmill. On the cycle ergometer the differences were significant between the positions left inferior and left superior in the first (p = 0.000), and the second test (p = 0.003), and between the tests with fixed and non-fixed sensors in the position left superior (p = 0.011). The study confirms that ECG sensors could be used for maximal exercise stress tests in laboratories, especially on a cycle ergometer, and that they present a great potential for future use of ECG sensors during physical activity.

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Section: Discussionsupporting

confidence: 77%

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Siraiy

Trobec

Ilić

2019

Measurement Science Review

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“…In this study, a wireless EKG device was chosen to accommodate the motion inherent in the exercise protocol we employed. However, wireless EKG devices are inclined to have increased signaling noise as activity level increases, with increased errors seen during accelerated movements [31]. Although our EKG data were fairly noise-free, a small percentage of data were filtered out to decrease any motion-related artifacts.…”

Section: Discussionmentioning

confidence: 99%

Validation of a Self‐Monitoring Tool for Use in Exercise Therapy

Powierza

Clark

Hughes

et al. 2017

PM&R

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Background Aerobic exercise at a sub-symptom heart rate has been recommended as therapy for Post-Concussion Syndrome (PCS). Assessing adherence with an accurate heart rate monitoring instrument is difficult limiting the proliferation of large-scale randomized controlled trials. Objective To evaluate the validity of the Fitbit Charge HR against electrocardiogram (EKG) to monitor heart rate during a treadmill-based exercise protocol. Design A methods comparison study. Setting Sports medicine research center within a tertiary care institution. Participants A convenience sample of 22 healthy participants (12 female) aged 18–26 years (mean age: 22 ± 2 years). Methods Fitbit Charge HR heart rate measurements were compared to EKG data concurrently collected while participants completed the Buffalo Concussion Treadmill Test (BCTT). Main Outcome Measures Agreement between Fitbit Charge HR and EKG was assessed by intraclass correlation coefficients (ICC3,1), Bland-Altman limits of agreement, and percent error. Results We observed a strong single-measure absolute agreement between Fitbit Charge HR and EKG (ICC3,1 = 0.83; 95% CI: 0.67 – 0.90). Fitbit Charge HR underestimated heart rate compared to EKG (mean difference = −6.04 beats per min (bpm); SD = 10.40 bpm; Bland-Altman 95% limits of agreement = −26.42 to 14.35 bpm). 69.9% of Fitbit heart rate measurements were within 10% error compared to EKG, and 91.5% of all heart rate measurements were within 20% error. Conclusions While the mean bias in measuring heart rate was relatively small, the limits of agreement between the Fitbit Charge HR and EKG were broad. Thus, the Fitbit Charge HR would not be a suitable option for monitoring heart rate within a narrow range. For the purposes of post-concussion exercise therapy, the relatively inexpensive cost, easy implementation, and low maintenance make Fitbit Charge HR a viable option for assessing adherence to an exercise program when expensive clinical equipment is unavailable.

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“…The study, however, only reported arrhythmia classification results on 16 ECG recordings selected from the MIT-BIH arrhythmia database, and did not demonstrate the actual use of acceleration signals in the arrhythmia classification experiment. In [ 56 ], Takalokastari et al reported a significant correlation between noises in ECG signals and levels of acceleration signals based on an analysis of ECG and 3D acceleration signals acquired from 30 subjects during 3 types of ADLs, i.e. , running, biking, and walking.…”

Section: Related Workmentioning

confidence: 99%

False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information

Tanantong

Nantajeewarawat

Thiemjarus

2015

Sensors

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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.

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Quality of the Wireless Electrocardiogram Signal During Physical Exercise in Different Age Groups

Cited by 17 publications

References 27 publications

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Validation of a Self‐Monitoring Tool for Use in Exercise Therapy

False Alarm Reduction in BSN-Based Cardiac Monitoring Using Signal Quality and Activity Type Information

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