A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors

Ravanshad, Nassim; Rezaee-Dehsorkh, Hamidreza; Lotfi, Reza; Lian, Yong

doi:10.1109/jbhi.2013.2274809

Cited by 143 publications

(61 citation statements)

References 28 publications

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“…A low power high resolution Thoracic Impedance Variance and ECG monitoring has been developed and incorporated in a compact plaster sensor form for wearable low cost cardiac healthcare [33]. An asynchronous analogto-information conversion system has been introduced for measuring the RR intervals of the ECG signals [34]. The system contains a modified level-crossing analog-to-digital converter and a novel algorithm for detecting the R-peaks from the level-crossing sampled data in a compressed volume of data [34].…”

Section: Sensors For Human Activity Monitoringmentioning

confidence: 99%

Wearable Sensors for Human Activity Monitoring: A Review

2015

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An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies, embedded systems, wireless communication technologies, nano technologies, and miniaturization makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This paper reviews the latest reported systems on activity monitoring of humans based on wearable sensors and issues to be addressed to tackle the challenges.Index Terms-Wearable sensors, smart sensors, sensor networks, wireless sensor networks, body sensor networks, body area networks, activity monitoring, assisted living, smart home, physiological parameters monitoring.1530-437X He is currently a Professor of Sensing Technology with the School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand. He has over 25 years of teaching and research experiences.His fields of interest include sensors and sensing technology, instrumenta-

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Section: Sensors For Human Activity Monitoringmentioning

confidence: 99%

Wearable Sensors for Human Activity Monitoring: A Review

2015

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“…Because of the above mentioned, many researches are devoted to the QRS complex identification [9][10][11][12]. The QRS detection algorithms are already reviewed in Ref.…”

Section: Ectopic Beats Detection and Classificationmentioning

confidence: 99%

Ectopic beats detection and correction methods: A review

Dib

Reguig

2015

Biomedical Signal Processing and Control

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“…Although more robust heart beat detectors exist [38,39], this simple approach is used to compare the performance of this basic algorithm with the methods proposed in this paper. After heart beat detection, the algorithm must calculate the heart rhythm for each of the three sources.…”

Section: Parameters Extraction 31 Heart Beat Detectionmentioning

confidence: 99%

Sensor fusion methods for reducing false alarms in heart rate monitoring

Borges

Brusamarello

2015

J Clin Monit Comput

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Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

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A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors

Cited by 143 publications

References 28 publications

Wearable Sensors for Human Activity Monitoring: A Review

Wearable Sensors for Human Activity Monitoring: A Review

Ectopic beats detection and correction methods: A review

Sensor fusion methods for reducing false alarms in heart rate monitoring

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