Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Pinheiro, Eduardo; Postolache, Octavian; Girão, Pedro Silva

doi:10.1016/j.measurement.2011.03.022

Cited by 44 publications

(37 citation statements)

References 16 publications

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“…Extracting useful information from large amounts of oscillatory data is important for a considerate number of real world applications such as medical electrocardiography (ECG) reading [1,2,3], atomic crystal images in physics [4,5], mechanical engineering [6,7], art investigation [8,9], geology [10,11,12], imaging [13], etc. In order to extract certain features and analyze adaptive components of oscillatory data, it is typical to assume that the signal f (t) consists of several oscillatory modes like…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

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This paper proposes the multiresolution mode decomposition (MMD) as a novel model for adaptive time series analysis. The main conceptual innovation is the introduction of the multiresolution intrinsic mode function (MIMF) of the formto model nonlinear and non-stationary data with time-dependent amplitudes, frequencies, and waveforms. The multiresolution expansion coefficients {a n }, {b n }, and the shape function series {s cn (t)} and {s sn (t)} provide innovative features for adaptive time series analysis. For complex signals that are a superposition of several MIMFs with well-differentiated phase functions φ(t), a new recursive scheme based on Gauss-Seidel iteration and diffeomorphisms is proposed to identify these MIMFs, their multiresolution expansion coefficients, and shape function series. Numerical examples from synthetic data and natural phenomena are given to demonstrate the power of this new method.

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Section: Introductionmentioning

confidence: 99%

“…In spite of considerable successes of analyzing oscillatory time series in the form of mode decomposition in (1) or GMD in (2), these models conflict with the physical intuition that the oscillation pattern of the time series changes in time. For example, the cardiac and respiratory patterns in Figure 1 vary in time.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution mode decomposition for adaptive time series analysis

Yang

2021

Applied and Computational Harmonic Analysis

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“…In addition, although linear techniques are being increasingly less used in the last years, PCA may be used together with some other nonlinear technique to improve the performance of this latter. An interesting example may be found in [4], where PCA was applied to Empirical Mode Decomposition to isolate the cardiac information in the processing of cardiovascular signals. Therefore, the proposal of new techniques and algorithms based on PCA for dealing with source separation is justifiable, particularly in applications where both speed and memory usage are crucial, e.g.…”

Section: Introductionmentioning

confidence: 99%

A principal component-based algorithm for denoising in single channel data (PCA for denoising in single channel data)

et al. 2015

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“…However, generally only one physiological signal is monitored, often supplemented by accelerometry, and few results are provided regarding the patient's state [7][8][9][10][11][12][13][14][15]. Specifically in chairs or wheelchairs, where physical space is available, and embedding several sensors is possible [16], [17]. Improvements on the state of the art are attainable, as the known implementations on wheelchairs monitor only one cardiac signal, and do not provide a stand alone monitoring solution, or only with limited signal processing data storage and communication capabilities [14,15,26].…”

Section: Introductionmentioning

confidence: 99%

Calibration and validation of homeostasis parameters estimates produced by a DSP embedded in a wheelchair

Pinheiro

Postolache

Girão

2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Ballistocardiogram, capacitive electrocardiogram, and photoplethysmogram (PPG) sensors were embedded in a wheelchair together with a digital signal processor (DSP) with Wi-Fi capabilities. This setup allows monitoring of some homeostasis parameters of the wheelchair user, namely heart rate (HR), pulse arrival and transit times, and oxygen saturation (SpO 2 ). Ten subjects tested the wheelchair capabilities. SpO 2 and HR obtained from the PPG of reference equipment were also recorded for comparative analysis. This paper reports the results of the algorithms developed for HR estimation, for all the signals, and for SpO 2 from the PPG. From the data of the ten subjects the algorithms had their parameters calibrated, and presented an average RMS error of 1.903% for SpO 2 , with the best value being 0.024%. The most unstable signal is the BCG from where the global calibration provides an HR estimate with an average 4.73 bpm error. The algorithms created were confirmed valid, and the signals from the hardware setup can be used to provide estimates with significant accuracy in heart rate estimation, but needing personalized calibration in the SpO 2 case.

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Empirical Mode Decomposition and Principal Component Analysis implementation in processing non-invasive cardiovascular signals

Cited by 44 publications

References 16 publications

Multiresolution mode decomposition for adaptive time series analysis

Multiresolution mode decomposition for adaptive time series analysis

A principal component-based algorithm for denoising in single channel data (PCA for denoising in single channel data)

Calibration and validation of homeostasis parameters estimates produced by a DSP embedded in a wheelchair

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