Symmetric Projection Attractor Reconstruction Analysis as a Method to Assess Seismocardiogram Quality in a Healthy Population.

Signal quality significantly affects the processing, analysis, and interpretation of biomedical signals. There are many procedures for assessing signal quality that use averaged numerical values, thresholding, analysis in the time or frequency domain, or nonlinear approaches. An interesting approach to the assessment of signal quality is using symmetric projection attractor reconstruction (SPAR) analysis, which transforms an entire signal into a two-dimensional plot that reflects the waveform morphology.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms of Healthy Volunteers and Patients with Valvular Heart Diseases.

Heart rate variability (HRV) is the physiological variation in the intervals between consecutive heartbeats that reflects the activity of the autonomic nervous system. This parameter is traditionally evaluated based on electrocardiograms (ECG signals). Seismocardiography (SCG) and/or gyrocardiography (GCG) are used to monitor cardiac mechanical activity; therefore, they may be used in HRV analysis and the evaluation of valvular heart diseases (VHDs) simultaneously.

Heartbeat Detection in Seismocardiograms with Semantic Segmentation.

Heartbeat detection is an essential part of cardiac signal analysis because it is recognized as a representative measure of cardiac function. The gold standard for heartbeat detection is to locate QRS complexes in electrocardiograms. Due to the development of sensors and information and communication technologies (ICT), seismocardiography (SCG) is becoming a viable alternative to electrocardiography to monitor heart rate.

Time Domain and Frequency Domain Heart Rate Variability Analysis on Electrocardiograms and Mechanocardiograms from Patients with Valvular Diseases.

Heart rate variability (HRV) is a physiological phenomenon of the variation of a cardiac interval (interbeat) over time that reflects the activity of the autonomic nervous system. HRV analysis is usually based on electrocardiograms (ECG signals) and has found many applications in the diagnosis of cardiac diseases, including valvular diseases. This analysis could also be performed on seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) that provide information on cardiac cycles and the state of heart valves.

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

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.

Time Domain And Frequency Domain Heart Rate Variability Analysis on Gyrocardiograms.

Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. The interest in heart rate monitoring without electrodes led to the rise of alternative heart beat monitoring methods, such as gyrocardiography (GCG). The purpose of this study was to compare HRV indices calculated on GCG and ECG signals.

Heart Rate Variability Analysis on Electrocardiograms, Seismocardiograms and Gyrocardiograms on Healthy Volunteers.

Physiological variation of the interval between consecutive heartbeats is known as the heart rate variability (HRV). HRV analysis is traditionally performed on electrocardiograms (ECG signals) and has become a useful tool in the diagnosis of different clinical and functional conditions. The progress in the sensor technique encouraged the development of alternative methods of analyzing cardiac activity: Seismocardiography and gyrocardiography.

Influence of Empirical Mode Decomposition on Heart Rate Variability Indices Obtained from Smartphone Seismocardiograms.

Heart rate variability (HRV) is a physiological variation of time interval between consecutive heart beats caused by the activity of autonomic nervous system. Seismocardiography (SCG) is a non-invasive method of analyzing cardiac vibrations and can be used to obtain inter-beat intervals required to perform HRV analysis. Heart beats on SCG signals are detected as the occurrences of aortic valve opening (AO) waves.

Comparison of HRV indices obtained from ECG and SCG signals from CEBS database.

Background: Heart rate variability (HRV) has become a useful tool of assessing the function of the heart and of the autonomic nervous system. Over the recent years, there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing vibrations generated by the heart using an accelerometer.

Heart Rate Variability Analysis on CEBS Database Signals.

Heart rate variability (HRV) is a valuable noninvasive tool of assessing the state of cardiovascular autonomic function. Over the recent years there has been interest in heart rate monitoring without electrodes. Seismocardiography (SCG) is a non-invasive technique of recording and analyzing cardiovascular vibrations.

Multi-sources data analysis with sympatho-vagal balance estimation toward early bruxism episodes detection.

Sleep bruxism events detection system is presented, based on integrated, synchronized on-line analysis of EMG signal, heart rave variability (HRV) obtained from ECG recordings as well as sympatho-vagal balance estimated in real time as an possible early indicator of upcoming bruxism episodes. As an relative reliable alternative for very complex systems, only for clinical environment usage with audio and video recordings a pilot study toward elaboration of compact, comfortable for home usage device with early bruxism detection algorithms was carried out, preliminary tested on 10h sleeping registrations from group of 12 patients, clinically characterized by experts as Bruxers. As a result a set of decision rules regarding simultaneous monotonic increase of heart rate with significant increase of EMG signal amplitude during bruxism episode was elaborated.

Feature extraction in time-frequency signal analysis by means of matched wavelets as a feature generator.

The goal of presented work was to compare the usage of standard basic wave let function like e.g. bio-orthogonal or dbn with the optimized wavelet created to the best match analyzing ECG signals in the context of P-wave and atrial fibrillation detection.

Rules extraction in SVM and neural network classifiers of atrial fibrillation patients with matched wavelets as a feature generator.

Presented paper describes a system of biomedical signal classifiers with preliminary feature extraction stage based on matched wavelets analysis, where two structures of classifier using Neural Networks (NN) and Support Vector Machine (SVM) are applied. As a pilot study the rules extraction algorithm applied for two of mentioned machine learning approaches (NN & SVM) was used. This was made to extract and transform the representation of knowledge gathered in Black Box parameters during classifier learning phase to be better and natural understandable for human user/expert.

An improvement of normogastric rhythm extraction from electrogastrographic (EGG) signal using independent component analysis.

Electrogastrographic Signal (EGG) is considered to be one of the less interesting from both registration and interpretation point of view. There are several reasons of that two facts. EGG presents gastric myoelectrical activity measured by several electrodes attached on the abdomen.

Feature extraction based on time-frequency and Independent Component Analysis for improvement of separation ability in Atrial Fibrillation detector.

Due to redundancy of over-dimensioned information, observed often in originally recorded biomedical signals, feature extraction and selection has become focus of much researches connected with biomedical signal processing and classification. Mixed new feature vector combined from time-frequency signal representation (obtained after wavelet transform) and Independent Component Analysis (ICA) applied for non-stationary signals is proposed as a preliminary stage in ECG waveform classification for patients with Atrial Fibrillation (AF). Discrete fast wavelet transform coefficients parameters including energy and entropy measures and components extracted as a result of FastICA algorithm implementation after optimization gave the best classifier performance of whole AF ECG classifier system.