Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms

Hurnanen, Tero; Lehtonen, Eero; Tadi, Mojtaba Jafari; Kuusela, Tom; Kiviniemi, Tuomas; Saraste, Antti; Vasankari, Tuija; Airaksinen, Juhani; Koivisto, Tero; Pänkäälä, Mikko

doi:10.1109/jbhi.2016.2621887

Cited by 75 publications

(64 citation statements)

References 35 publications

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“…In addition to human studies on healthy populations, there were several studies that focused on the application of SCG in patients with cardiovascular disease. SCG signals were used for diagnosis and monitoring of different clinical conditions such as atrial fibrillation [47,48,68,70], atrial flutter [55], heart valve disease [37,44,80], coronary artery disease and ischemia [9,10,48], myocardial infarction [126], heart failure [75,78,82,100,119], structural heart disease [80], and heart stress testing [58].…”

Section: Scg In Patients With Cardiac Conditionsmentioning

confidence: 99%

Recent Advances in Seismocardiography

et al. 2019

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Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

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Section: Scg In Patients With Cardiac Conditionsmentioning

confidence: 99%

Recent Advances in Seismocardiography

et al. 2019

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“…In [ 11 ], the heart condition is estimated from the learned morphology of seismocardiography. In [ 24 ], SCG based automated detection of atrial fibrillation is demonstrated. In [ 25 ], a comparative study on pulse transit time measurement using seismocardiography, photoplethysmography and acoustic recordings is carried out.…”

Section: Related Workmentioning

confidence: 99%

“…To the best of our knowledge, the state-of-art recent studies [ 6 , 8 , 23 ] exclusively focus on separate or combined annotation of ECG and SCG or focus on the annotation based disease-specific cardiac function monitoring [ 9 , 24 ]. However, mere annotation of morphologies may not provide the powerful insight unless the comprehensive theoretical analysis is explored.…”

Section: Related Workmentioning

confidence: 99%

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Sahoo

Thakkar

Lin

et al. 2018

Sensors

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Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

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“…Moreover, such approach enables to involve the various recording methods which can provide signals in which the heartbeats can be detected. Beside electrocardiogram, such signals include photoplethysmogram [22][23][24] or seismocardiogram [25]. Using a photoelectric sensor is attractive in case of home telecare as long-term recording should be accomplished by instrumentations being minimally troublesome and inconvenient to the patient [26,27].…”

Section: Introductionmentioning

confidence: 99%

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

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Automated Detection of Atrial Fibrillation Based on Time–Frequency Analysis of Seismocardiograms

Cited by 75 publications

References 35 publications

Recent Advances in Seismocardiography

Recent Advances in Seismocardiography

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

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