Applying the EFuNN Evolving Paradigm to the Recognition of Artefactual Beats in Continuous Seismocardiogram Recordings

Malcangi, Mario; Hao, Qian; Vaini, Emanuele; Lombardi, Prospero; Rienzo, Marco Di

doi:10.1007/978-3-319-65172-9_22

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Therefore, techniques that can remove noise from ambulatory SCG are essential. One study [98] used an evolving fuzzy neural network algorithm to identify the SCG cycles polluted by movement artefacts and remove them from the SCG signal. In another effort [72], a normalized least-mean-square (NLMS) adaptive filter was utilized to cancel the motion noise from SCG of ambulatory subjects.…”

Section: Noise Reductionmentioning

confidence: 99%

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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: Noise Reductionmentioning

confidence: 99%

“…Other studies [40,65] sought to classify SCG signals according to the lung volume phases as opposed to inspiration/expiration. Classification methods were also utilized to help identify fiducial points on the SCG signals [61], artefact presence in the SCG [98], and identification of the sensor location [41,82].…”

Section: Scg Features Referencementioning

confidence: 99%

Recent Advances in Seismocardiography

et al. 2019

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“…However, current algorithms are complex [38,39,40], or require a large amount of initial reference data [41], which renders them infeasible for everyday use. Algorithms that rely on training data sets [42,43] are limited in versatility, customizability, and efficiency. A common workaround for motion artifact cancellation in VCG is by correlation with ECG [44,45,46,47,48], that is, by leveraging the reliability of R-peak detection to inform mechanical aortic opening (AO) detection.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

D’Mello

Skoric

et al. 2019

Sensors

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Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

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“…However, a significant difficulty with CRA measurements is inter-subject variations, which necessitates robust and adaptable digital signal processing (DSP) algorithms [27]. The current state of the art regarding accuracy in SCG-DSP includes algorithms that either perform an analysis of the signal offline due to high computational requirements [28]- [30], that require synchronized measurements with ECG [21], [31], or that implement pre-training for calibration to the user [32], [33]. Caveats such as these preclude the implementation of SCG in real-time, portable cardiac analysis.…”

mentioning

confidence: 99%

Autocorrelated Differential Algorithm for Real-Time Seismocardiography Analysis

D’Mello

Skoric

et al. 2019

IEEE Sensors J.

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We present a novel seismocardiography (SCG)based approach for real-time cardio-respiratory activity measurement called the Autocorrelated Differential Algorithm (ADA). Measurements were performed on ten male subjects in the supine position for three 7-minute-long sets each, corresponding to 14,619 heartbeats. The ADA utilized temporal variations, windowing, and autocorrelation to produce physiological measurements corresponding to heart rate (HR), and left ventricular ejection time, and estimations of respiration rate, volume, and phase. The versatility of the ADA was investigated in two contexts: physical exertion and heart rate variability. The accuracy of HR measurements at a sampling frequency of 200 Hz resulted in a correlation coefficient (r 2) of 0.9808 when compared with a manual annotation of all datasets. Its reproducibility was tested on externally obtained SCG and electrocardiography datasets, which produced an r 2 of 0.8224. The accuracy and computational time were also characterized by different sampling frequencies to quantify performance. The recommended sampling frequency is 200 Hz corresponding to a computation time of 0.05 s per instantaneous measurement using a standard desktop computer. The ADA delivered real-time SCG measurements with a refresh rate that was dependent on the computational time per measurement, which could be decreased by lowering the sampling frequency. The presented algorithm offers a novel tool toward real-time physiological monitoring in clinical and everyday scenarios.

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Applying the EFuNN Evolving Paradigm to the Recognition of Artefactual Beats in Continuous Seismocardiogram Recordings

Cited by 4 publications

References 8 publications

Recent Advances in Seismocardiography

Recent Advances in Seismocardiography

Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

Autocorrelated Differential Algorithm for Real-Time Seismocardiography Analysis

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