A knowledge-based system for qualitative ECG simulation and ECG analysis

Wang, J.T.; Sehmi, A.S.; Jones, N.B.; Bono, D.P. de

doi:10.1109/cic.1991.169016

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…Classification is one of the final stages in analysing ECG signals. Most research develops systems for several tasks, such as disease classification ( 166 ), patient classification ( 167 ), ECG simulation ( 168 ), and emotion recognition ( 169 ). With this aim, supervised methods such as naive Bayes ( 170 ), random forest ( 171 ), genetic algorithms ( 128 ), linear and quadratic discriminants ( 172 ), SVM ( 173 , 174 ), decision trees ( 175 ), discriminant analysis ( 138 ), and ANN ( 173 , 174 ) have been used.…”

Section: Discussionmentioning

confidence: 99%

“…Classification is one of the final stages in analysing ECG signals. Most research develops systems for several tasks, such as disease classification (166), patient classification (167), ECG simulation (168), and emotion recognition (169). With this aim, supervised methods such as naive Bayes ( 170 Machine learning has contributed to various elements such as detection or classification of heartbeats (185), arrhythmias (129,186), and unexpected changes in heart morphology (187,188).…”

Section: Ecg/hrv Modellin-classification -Machine Learningmentioning

confidence: 99%

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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%

“…Classification is one of the final stages in analysing ECG signals. Most research develops systems for several tasks, such as disease classification (166), patient classification (167), ECG simulation (168), and emotion recognition (169). With this aim, supervised methods such as naive Bayes ( 170 Machine learning has contributed to various elements such as detection or classification of heartbeats (185), arrhythmias (129,186), and unexpected changes in heart morphology (187,188).…”

Section: Ecg/hrv Modellin-classification -Machine Learningmentioning

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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An Introduction to ECG Signal Processing and Analysis

Gacek

2011

ECG Signal Processing, Classification and Interpretation

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The Programmable ECG Simulator

Caner

Engin

2008

J Med Syst

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This paper reports the design and development of Digital Signal Controller (DSPIC)-based ECG simulator intended to use in testing, calibration and maintenance of electrocardiographic equipment, and to support biomedical engineering students' education. It generates all 12 healthy ECG derivation signals having a profile that varies with heart rate, amplitude, and different noise contamination in a manner which reflects true in vivo conditions. The heart rate can be set at the range of 30 to 120 beats/minute in four steps. The noise and power line interference effects can be set at the range of 0 to 20 dB in three steps. Since standard commercially available electronic components were used to construct the prototype simulator, the proposed design was also relatively inexpensive to produce.

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A knowledge-based system for qualitative ECG simulation and ECG analysis

Cited by 6 publications

References 6 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

An Introduction to ECG Signal Processing and Analysis

The Programmable ECG Simulator

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