Adaptive filter for eliminating baseline wander of pulse wave signals

Fedotov, A. A.; Akulova, A. S.

doi:10.1007/978-3-319-19387-8_248

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Baseline wander is produced by different sources of low-frequency noise ( 104 ), among them a deficient contact of the electrodes to the skin and some effects of respiration as changes in impedance ( 105 ). Other techniques for detrending biosignals include cascaded moving average filter ( 105 ), quadratic variation reduction ( 104 ), wavelet transform ( 106 ), and adaptive filtering ( 107 ).…”

Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable elements of sound and music on the human body. Variations in blood pressure, levels of stress or anxiety, subjective sensations and even changes in emotions constitute multiple aspects that may well-react or respond to musical stimuli. Although both ECG and HRV continue to feature extensively in research in health and perception, methodologies vary substantially. This makes it difficult to compare studies, with researchers making recommendations to improve experiment planning and the analysis and reporting of data. The present work provides a methodological framework to examine the effect of sound on ECG and HRV with the aim of associating musical structures and noise to the signals by means of artificial intelligence (AI); it first presents a way to select experimental study subjects in light of the research aims and then offers possibilities for selecting and producing suitable sound stimuli; once sounds have been selected, a guide is proposed for optimal experimental design. Finally, a framework is introduced for analysis of data and signals, based on both conventional as well as data-driven AI tools. AI is able to study big data at a single stroke, can be applied to different types of data, and is capable of generalisation and so is considered the main tool in the analysis.

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Section: Discussionmentioning

confidence: 99%

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Front. Cardiovasc. Med.

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“…Both can lead to under or over estimates of mean arterial, systolic, diastolic or pulse pressures (Esper and Pinsky 2014). To address this issue, baseline wander is sometimes removed, especially on ECG signals (Fedotov and Akulova 2015, Li et al 2017, 2018). However, editing the original signal in this way may exclude important information from the data.…”

Section: Current Arterial Pulse Waveform Analysismentioning

confidence: 99%

A novel method to quantify arterial pulse waveform morphology: attractor reconstruction for physiologists and clinicians

2018

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Current arterial pulse monitoring systems capture data at high frequencies (100–1000 Hz). However, they typically report averaged or low frequency summary data such as heart rate and systolic, mean and diastolic blood pressure. In doing so, a potential wealth of information contained in the high-fidelity waveform data is discarded, data which has long been known to contain useful information on cardiovascular performance.Here we summarise a new mathematical method, attractor reconstruction, which enables the quantification of arterial waveform shape and variability in real-time. The method can handle long streams of non-stationary data and does not require preprocessing of the raw physiological data by the end user. Whilst the detailed mathematical proofs have been described elsewhere (Aston et al 2008 Physiol. Meas. 39), the authors were motivated to write a summary of the method and its potential utility for biomedical researchers, physiologists and clinician readers.Here we illustrate how this new method may supplement and potentially enhance the sensitivity of detecting cardiovascular disturbances, to aid with biomedical research and clinical decision making.

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Adaptive filter for eliminating baseline wander of pulse wave signals

Cited by 2 publications

References 5 publications

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics

A novel method to quantify arterial pulse waveform morphology: attractor reconstruction for physiologists and clinicians

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