Neural Network-based Classification of Static Lung Volume States using Vibrational Cardiography

Clairmonte, Nathan; Skoric, James; D’Mello, Yannick; Hakim, Siddiqui; Aboulezz, Ezz; Lortie, Michel; Plant, David V.

doi:10.1109/embc44109.2020.9176119

Cited by 4 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this year Clairmonte et al in [ 79 ] confirmed the feasibility of classification of two lung volume states (high and low volume state) on 50 participants. D’Mello et al in [ 80 ] identified the heart sounds based on seismocardiography and gyrocardiography with a high correlation coefficients of 0.9887 for HR measured with concurrent ECG measurement.…”

Section: Resultsmentioning

confidence: 87%

“…Yang et al [ 78 ] proposed a machine learning-based method for classification of aortic stenosis. Another studies describe the estimation of static lung volume states [ 79 ]. Based on the findings of the study of Yang et al [ 78 ], the patients after TAVR are not recognized as healthy people because artificial heart valves produce different vibrations than natural valves.…”

Section: Resultsmentioning

confidence: 99%

“…The GCG along with the SCG and BCG constitute the mechanocardiography (MCG) or vibrational cardiography (VCG). The first term was proposed by [ 24 , 49 , 55 , 58 , 60 , 64 , 65 , 71 , 73 , 77 , 88 ] and the second term was proposed by [ 9 , 73 , 79 , 80 ].…”

Section: Resultsmentioning

confidence: 99%

“…Clairmonte et al in [ 79 ] designed the classifier of two lung volume states (high and low volume state) on 50 participants based on 1D convolutional neural network (CNN). High volume state was defined when the subject had fully inhaled the air whereas the low volume state was when the subject had fully exhaled the air.…”

Section: Resultsmentioning

confidence: 99%

“…Because the ECG is considered the gold standard in analyzing cardiac activity [ 46 ], the advantages of gyrocardiography are underlined below as follows: small in size, inexpensive and accurate sensors [ 32 ] which can be embedded in smart clothes [ 122 ] and smartphones [ 64 , 71 , 123 ], no need for applying multiple electrodes which may irritate the patient’s skin [ 9 , 120 ], providing the information on the mechanical aspects of the cardiac cycle [ 8 , 11 ] and consequently the ability to distinguish the systolic and diastolic phases for analyses of left ventricular performance [ 80 , 121 ], providing the opportunity for registering respiratory movements [ 79 ], and the feasibility of self-application of the GCG using a smartphone [ 64 , 71 ].…”

Section: Discussionmentioning

confidence: 99%

See 4 more Smart Citations

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

View full text Add to dashboard Cite

Gyrocardiography (GCG) is a non-invasive technique of analyzing cardiac vibrations by a MEMS (microelectromechanical system) gyroscope placed on a chest wall. Although its history is short in comparison with seismocardiography (SCG) and electrocardiography (ECG), GCG becomes a technique which may provide additional insight into the mechanical aspects of the cardiac cycle. In this review, we describe the summary of the history, definition, measurements, waveform description and applications of gyrocardiography. The review was conducted on about 55 works analyzed between November 2016 and September 2020. The aim of this literature review was to summarize the current state of knowledge in gyrocardiography, especially the definition, waveform description, the physiological and physical sources of the signal and its applications. Based on the analyzed works, we present the definition of GCG as a technique for registration and analysis of rotational component of local cardiac vibrations, waveform annotation, several applications of the gyrocardiography, including, heart rate estimation, heart rate variability analysis, hemodynamics analysis, and classification of various cardiac diseases.

show abstract

Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Sieciński

Kostka

Tkacz

2020

Sensors

View full text Add to dashboard Cite

show abstract

Respiratory Modulation of Sternal Motion in the Context of Seismocardiography

et al. 2022

Self Cite

View full text Add to dashboard Cite

Wearable sensing has enabled physiological monitoring in everyday life. Clinically, cardiovascular and respiratory monitoring commonly requires separate devices. However, for feasibility in wearable systems, they are integrated into a single device. Since seismocardiography (SCG) records the motion of the chest wall, it has the potential to monitor both vital signs. The primary application of SCG is cardiovascular monitoring. As a result, its ability to detect respiration has been less defined. This study characterizes how SCG is affected by respiration and consequently, how respiratory metrics can be derived from these effects. The sternal motion of 40 subjects was recorded using an inertial measurement unit with a spirometer for reference. Three scenarios were examined with the subject at rest, holding their breath at peak inhalation, and holding their breath when exhaled. Three main aspects of respiratory modulation were explored: amplitude modulation, frequency modulation, and baseline wandering. The SCG amplitude was observed to be dependent on respiratory volume whereas baseline and frequency modulation were dependent on inhalation phase. All three effects were employed separately in the calculation of respiratory rate. Estimations from baseline wandering, amplitude modulation, and heart rate modulation produced r-squared values of 0.71, 0.44, and 0.66, respectively. The accuracy of tidal volume measurement was limited by a high inter-subject variability. Estimation of the respiratory waveform produced an average r-squared of 0.76 using multivariate linear regression. This demonstrates the potential of SCG as a tool for respiratory monitoring within an integrated, noninvasive cardiorespiratory monitoring system from a single point of contact.

show abstract

Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control

Skoric,

D'Mello,

Plant

2025

IEEE Open J. Eng. Med. Biol.

View full text Add to dashboard Cite

Neural Network-based Classification of Static Lung Volume States using Vibrational Cardiography

Cited by 4 publications

References 17 publications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Gyrocardiography: A Review of the Definition, History, Waveform Description, and Applications

Respiratory Modulation of Sternal Motion in the Context of Seismocardiography

Generation of Seismocardiography Heartbeats Using a Wasserstein Generative Adversarial Network With Feature Control

Contact Info

Product

Resources

About