RESUMEN
The development of information and communication technologies (ICT) changed many aspects of our lives, including cardiovascular research. This area of research is affected by the availability of open databases that can help conduct basic and applied research. In this study, we summarize the current state of knowledge in publicly available signal databases with seismocardiographic (SCG) signals in January 2023. Based on Google search results for the expression "seismocardiography dataset", we have found and described five databases with seismocardiograms, including three databases that contain SCG signals from healthy subjects, one database with data from porcine subjects, and one signal database with data obtained from human patients with valvular heart disease (VHD). All contain additional signals for reference points in the cardiac cycle. The most significant limitations of the described data sets are gender bias toward male subjects, the imbalance between healthy subjects, and subjects with two cardiovascular diseases (VHD and hemorrhage).
Asunto(s)
Enfermedades Cardiovasculares , Procesamiento de Señales Asistido por Computador , Humanos , Masculino , Femenino , Animales , Porcinos , Sexismo , Corazón , Bases de Datos FactualesRESUMEN
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. In this study, we present an application of SPAR to evaluate the quality of seismocardiograms (SCG signals) from the CEBS database, a publicly available seismocardiogram signal database. Visual inspection of symmetric projection attractors suggests that high-quality (clean) seismocardiogram projections resemble six-pointed asterisks (*), and any deviation from this shape suggests the influence of noise and artifacts.Clinical relevance- SPAR analysis enables quick identification of noise and artifacts that can affect the reliability of the diagnosis of cardiovascular diseases based on SCG signals.
Asunto(s)
Enfermedades Cardiovasculares , Procesamiento de Señales Asistido por Computador , Humanos , Reproducibilidad de los ResultadosRESUMEN
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. In our study, we sought to evaluate the influence of valvular heart disease on the correlations between HRV indices obtained from electrocardiograms, seismocardiograms, and gyrocardiograms and to compare the HRV indices obtained from the three aforementioned cardiac signals. The results of HRV analysis in the time domain and frequency domain of the ECG, SCG, and GCG signals are within the standard deviation and have a strong linear correlation. This means that despite the influence of VHDs on the SCG and GCG waveforms, the HRV indices are valid. Clinical Relevance-Cardiac mechanical signals (seismocar-diograms and gyrocardiograms) can be applied to evaluate heart rate variability despite the influence of valvular diseases on the morphology of cardiac mechanical signals.
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Cardiopatías , Enfermedades de las Válvulas Cardíacas , Sistema Nervioso Autónomo , Electrocardiografía , Frecuencia Cardíaca/fisiología , Enfermedades de las Válvulas Cardíacas/diagnóstico , HumanosRESUMEN
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. In this work, we propose a system for detecting the heartbeat based on seismocardiograms using deep learning methods. The study was carried out with a publicly available data set (CEBS) that contains simultaneous measurements of ECG, breathing signal, and seismocardiograms. Our approach to heartbeat detection in seismocardiograms uses a model based on a ResNet-based convolutional neural network and contains a squeeze and excitation unit. Our model scored state-of-the-art results (Jaccard and F1 score above 97%) on the test dataset, demonstrating its high reliability.
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Electrocardiografía , Semántica , Electrocardiografía/métodos , Frecuencia Cardíaca , Redes Neurales de la Computación , Reproducibilidad de los ResultadosRESUMEN
The knee joint, being the largest joint in the human body, is responsible for a great percentage of leg movements. The diagnosis of the state of knee joints is usually based on X-ray scan, ultrasound imaging, computerized tomography (CT), magnetic resonance imaging (MRI), or arthroscopy. In this study, we aimed to create an inexpensive, portable device for recording the sound produced by the knee joint, and a dedicated application for its analysis. During the study, we examined fourteen volunteers of different ages, including those who had a knee injury. The device effectively enables the recording of the sounds produced by the knee joint, and the spectral analysis used in the application proved its reliability in evaluating the knee joint condition.
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Articulación de la Rodilla , Imagen por Resonancia Magnética , Acústica , Humanos , Articulación de la Rodilla/diagnóstico por imagen , Reproducibilidad de los Resultados , UltrasonografíaRESUMEN
BACKGROUND: Dental schools are considered to be a very stressful environment; the stress levels of dental students are higher than those of the general population. The aim of this study was to assess the level of stress among dental students while performing specific dental procedures. METHODS: A survey was conducted among 257 participants. We used an original questionnaire, which consisted of 14 questions assigned to three categories: I-Diagnosis, II-Caries Treatment, and III-Endodontic Treatment. Each participant marked their perceived level of stress during the performed dental treatment procedures. The scale included values of 0-6, where 0 indicates no stress, while 6 indicates high stress. RESULTS: Third- (p=0.006) and fourth-year (p=0.009) women were characterized by a higher level of perceived stress during dental procedures related to caries treatment. Caries treatment procedures were the most stressful for 18.3% of third-year students, 4.3% of fourth-year students, and 3.2% of fifth-year students. Furthermore, 63.4% of third-year students, 47.3% of fourth-year students, and 17.2% of fifth-year students indicated that they felt a high level of stress when performing endodontic procedures. CONCLUSION: Third- and fourth-year female students are characterized by a higher level of stress during caries and endodontic treatment procedures. The most stressful treatments for participants were endodontic treatment procedures.
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Atención Odontológica , Estudiantes de Odontología , Femenino , Humanos , Polonia , Encuestas y CuestionariosRESUMEN
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.
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Electrocardiografía , Corazón , Frecuencia Cardíaca , Hemodinámica , VibraciónRESUMEN
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. The study on time domain and and frequency domain heart rate variability analysis was conducted on electrocardiograms and gyrocardiograms registered on 29 healthy male volunteers. ECG signals were used as a reference and the HRV analysis was performed using PhysioNet Cardiovascular Signal Toolbox. The results of HRV analysis show great similarity and strong linear correlation of HRV indices calculated from ECG and GCG indicate the feasibility and reliability of HRV analysis based on gyrocardiograms.
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Sistema Nervioso Autónomo , Electrocardiografía , Frecuencia Cardíaca , Humanos , Masculino , Reproducibilidad de los Resultados , Estudios de Tiempo y MovimientoRESUMEN
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. In our study we performed HRV analysis on ECG, seismocardiograms (SCG signals) and gyrocardiograms (GCG signals) using the PhysioNet Cardiovascular Toolbox. The heartbeats in ECG were detected using the Pan-Tompkins algorithm and the heartbeats in SCG and GCG signals were detected as peaks within 100 ms from the occurrence of the ECG R waves. The results of time domain, frequency domain and nonlinear HRV analysis on ECG, SCG and GCG signals are similar and this phenomenon is confirmed by very strong linear correlation of HRV indices. The differences between HRV indices obtained on ECG and SCG and on ECG and GCG were statistically insignificant and encourage using SCG or GCG for HRV estimation. Our results of HRV analysis confirm stronger correlation of HRV indices computed on ECG and GCG signals than on ECG and SCG signals because of greater tolerance to inter-subject variability and disturbances.
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Electrocardiografía , Frecuencia Cardíaca , Algoritmos , Voluntarios Sanos , HumanosRESUMEN
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. In this study, we compare HRV indices obtained from SCG and ECG on signals from combined measurement of ECG, breathing and seismocardiogram (CEBS) database and determine the influence of heart beat detector on SCG signals. METHODS: We considered two heart beat detectors on SCG signals: reference detector using R waves from ECG signal to detect heart beats in SCG and a heart beat detector using only SCG signal. We performed HRV analysis and calculated time and frequency features. RESULTS: Beat detection performance of tested algorithm on all SCG signals is quite good on 85,954 beats ([Formula: see text], [Formula: see text]) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ([Formula: see text]) to determine goodness of fit to linear model. The highest [Formula: see text] values were obtained for mean interbeat interval ([Formula: see text] for reference algorithm, [Formula: see text] in the worst case), [Formula: see text] and [Formula: see text] ([Formula: see text] for the best case, [Formula: see text] for the worst case) and the lowest were obtained for [Formula: see text] ([Formula: see text] in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the [Formula: see text] values of pNN50 values in signals p001-p020 and for all analyzed signals. CONCLUSIONS: Calculated HRV indices derived from ECG and SCG are similar using two analyzed beat detectors, except SDNN, RMSSD, NN50, pNN50, and [Formula: see text]. Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.
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Bases de Datos Factuales , Electrocardiografía , Frecuencia Cardíaca , Respiración , Procesamiento de Señales Asistido por Computador , Voluntarios Sanos , HumanosRESUMEN
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. Morphological variations between subjects complicate developing annotation algorithms. To overcome this obstacle we propose the empirical mode decomposition (EMD) to improve the signal quality. We used two algorithms to determine the influence of EMD on HRV indices: the first algorithm uses a band-pass filter and the second algorithm uses EMD as the first step. Higher beat detection performance was achieved for algorithm with EMD (Se=0.926, PPV=0.926 for all analyzed beats) than the algorithm with a band-pass filter (Se=0.859, PPV=0.855). The influence of analyzed algorithms on HRV indices is low despite the differences of heart beat detection performance between analyzed algorithms.
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Electrocardiografía , Frecuencia Cardíaca , Teléfono Inteligente , Algoritmos , Sistema Nervioso Autónomo , Humanos , Procesamiento de Señales Asistido por Computador , VibraciónRESUMEN
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. The purpose of this study is to compare HRV indices calculated on SCG and ECG signals from Combined measurement of ECG, breathing and seismocardiogram (CEBS) database. The authors use 20 signals lasting 200 s acquired from patients in supine position and compare heart rate variability parameters from the seismocardiogram and ECG reference signal. They assessed the performance of heart beat detector on SCG channel. The results of modified version of SCG heart beat detection prove its good performance on signals with higher sampling frequency. Strong linear correlation of HRV indices calculated from ECG and SCG prove the reliability of SCG in HRV analysis performed on signals from CEBS Database.
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Electrocardiografía , Frecuencia Cardíaca , Procesamiento de Señales Asistido por Computador , Humanos , Monitoreo Fisiológico , Reproducibilidad de los ResultadosRESUMEN
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. But a most promising observation, which can be useful for earlier prediction of upcoming bruxism episode seems to be a monotonic increase of LF/HF ratio in HRV power spectrum components, expressing sympatho-vagal balance of autonomous nervous system, which according to our assumptions take basic low level role in bruxism phenomena trigger and control.
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Algoritmos , Técnicas de Diagnóstico Neurológico , Bruxismo del Sueño/fisiopatología , Sistema Nervioso Simpático/fisiopatología , Nervio Vago/fisiopatología , Frecuencia Cardíaca/fisiología , Humanos , Proyectos Piloto , Bruxismo del Sueño/diagnósticoRESUMEN
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. A library of clinical expert evaluated typical atrial fibrillation evolutions was created as a database for optimal matched wavelet construction. Whole data set consisting of 40 cases with long term ECG recording s were divided into learning and verifying set for the multilayer perceptron neural network used as a classifier structure. Compared with other wavelet filters, the matched wavelet was able to improve classifier performance for a given ECG signals in terms of the Sensitivity and Specificity measures.
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Algoritmos , Fibrilación Atrial/diagnóstico , Diagnóstico por Computador/métodos , Electrocardiografía/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Análisis de Ondículas , Humanos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
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. Unfortunately the registration procedure does not deliver a pure signal as EGG is usually associated with some interferences caused by the other organs localized near stomach. On the other hand however there are no databases available, which could allow both comparison and proper interpretation. One of the parameter, among others, which is analyzed owing to proper registration is so called normogastric rhythm, which should cover around 70% of rhythmic behavior of the signal. Proper extraction of the normogastric rhythm is a subject of this paper. Special signal preprocessing steps should be applied before the main tool i.e. Independent Component Analysis (ICA) is applied for normogastric rhythm extraction. Also, to make this analysis possible a special registration procedure has been applied concerning two phases of registration - one with feeding and the other one without with 5 minutes brake between them.
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Algoritmos , Relojes Biológicos/fisiología , Diagnóstico por Computador/métodos , Electromiografía/métodos , Motilidad Gastrointestinal/fisiología , Reconocimiento de Normas Patrones Automatizadas/métodos , Humanos , Análisis de Componente Principal , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
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. Proposed system was tested on the set of ECG signals of 20 atrial fibrillation (AF) and 20 control group (CG) patients, divided into learning and verifying subsets, taken from MIT-BiH database. Obtained results showed, that the ability of generalization of created system, expressed as a measure of sensitivity and specificity increased, due to extracting and selectively choosing only the most representative features for analyzed AF detection problem. Classification results achieved by means of constructed matched wavelet, created for given AF detection features were better than indicators obtained for standard wavelet basic functions used in ECG time-frequency decomposition.
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Algoritmos , Fibrilación Atrial/diagnóstico , Electrocardiografía/métodos , Redes Neurales de la Computación , Procesamiento de Señales Asistido por Computador , Humanos , Proyectos Piloto , Sensibilidad y EspecificidadRESUMEN
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. System was positively verified on the set of clinically classified ECG signals for control and atrial fibrillation (AF) disease patients taken from MITBIH data base. The measures of specificity and sensitivity computed for the set of 20 AF and 20 patients from control group divided into learning and verifying subsets were used to evaluate presented pattern recognition structure. Different types of wavelet basic functions for feature extraction stage and kernels for SVM classifier structure calculation were tested to find the best system architecture. Obtained results showed, that the ability of generalization and separation for enriched feature extraction based system increased, due to selectively choosing only the most representative features for analyzed AF detection problem.