Study for Integration of Multi Modal Biometric Personal Identification Using Heart Rate Variability (HRV) Parameter

Budiman, Priatna Ahmad; Tresnawati, Teni; Alamsyah, Ahmad Tossin; Riandini,

doi:10.1088/1742-6596/1364/1/012074

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The AV conduction system is proximally composed of bundle of His, distally divided into two branches spreading along both sides of the interventricular septum as the left and right bundle branches. The left bundle branch further divides into the left anterior and the left posterior fascicles, further divided into Purkinje fibers, diverging into the inner layers of the ventricular muscle [7,8].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Augustýnek¹,

Kubicek²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

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The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary. This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker's functionality. In this work, we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment. The electrical model of the heart allows signals generation (right atrium, right ventricle) and the monitoring of the stimulation pulses. The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time. The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses. The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode. In addition, the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

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Section: Introductionmentioning

confidence: 99%

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Augustýnek¹,

Kubicek²,

Thomas³

et al. 2022

Computers, Materials &Amp; Continua

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show abstract

“…It is often used as an indicator of the physiological condition and as a diagnostic tool of the cardiovascular system, in particular of the heart health [ 3 ]. Among other models of ECG analysis, heart rate variability (HRV), derived from the R-R interval peaks of ECG, emerges to reveal relationships between autonomous neuron system and physiological, physical or psychological variables [ 4 ]. HRV exposes the interplay between the sympathetic and parasympathetic nervous system [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Idrobo-Ávila

Correa

Muñoz-Bolaños

et al. 2021

Heliyon

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The intention of the experiment is to investigate whether different sounds have influence on heart signal features in the situation the observer is judging the different sounds as positive or negative. As the heart is under (para)sympathetic control of the nervous system this experiment could give information about the processing of sound stimuli beyond the conscious processing of the subject. As the nature of the influence on the heart signal is not known these signals are to be analysed with AI/machine learning techniques. Heart rate variability (HRV) is a variable derived from the R-R interval peaks of electrocardiogram which exposes the interplay between the sympathetic and parasympathetic nervous system. In addition to its uses as a diagnostic tool and an active part in the clinic and research domain, the HRV has been used to study the effects of sound and music on the heart response; among others, it was observed that heart rate is higher in response to exciting music compared with tranquilizing music while heart rate variability and its low-frequency and high-frequency power are reduced. Nevertheless, it is still unclear which musical element is related to the observed changes. Thus, this study assesses the effects of harmonic intervals and noise stimuli on the heart response by using machine learning. The results show that noises and harmonic intervals change heart activity in a distinct way; e.g., the ratio between the axis of the ellipse fitted in the Poincaré plot increased between harmonic intervals and noise exposition. Moreover, the frequency content of the stimuli produces different heart responses, both with noise and harmonic intervals. In the case of harmonic intervals, it is also interesting to note how the effect of consonance quality could be found in the heart response.

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Face Image Feature Extraction based on Deep Learning Algorithm

Kuang

2021

J. Phys.: Conf. Ser.

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In recent years, due to the rapid development of computer technology, artificial intelligence technology in the computer field has begun to integrate into people’s life, and facial recognition, as a unique biometric recognition method, is the core of artificial intelligence technology. Based on this, this paper discusses the local feature extraction and global feature extraction based on the deep learning algorithm, and proposes a training classification method based on the deep learning model combined with local pattern and GLQP representation feature extraction algorithm. In this paper, the local quantization method is used to input the data set preprocessed by the filter into the network. The depth of CNN network is selected as 4 layers, and the network is trained to produce high-resolution features. Experiments show that the accuracy of the trained deep network model is 92.2% in the test set. Therefore, compared with the traditional methods, deep learning has the advantages of powerful visualization and automatic face feature extraction, overcomes the shortcomings of deep learning model in the process of shallow feature learning, and shows higher recognition efficiency and generalization.

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Study for Integration of Multi Modal Biometric Personal Identification Using Heart Rate Variability (HRV) Parameter

Cited by 3 publications

References 16 publications

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

Judgement of valence of musical sounds by hand and by heart, a machine learning paradigm for reading the heart

Face Image Feature Extraction based on Deep Learning Algorithm

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