Wearable armband device for daily life electrocardiogram monitoring

A wearable armband electrocardiogram (ECG) monitor has been used for daily life monitoring. The armband records three ECG channels, one electromyogram (EMG) channel, and tri-axial accelerometer signals. Contrary to conventional Holter monitors, the armband-based ECG device is convenient for long-term daily life monitoring because it uses no obstructive leads and has dry electrodes (no hydrogels), which do not cause skin irritation even after a few days. Principal component analysis (PCA) and normalized least mean squares (NLMS) adaptive filtering were used to reduce the EMG noise from the ECG channels. An artifact detector and an optimal channel selector were developed based on a support vector machine (SVM) classifier with a radial basis function (RBF) kernel using features that are related to the ECG signal quality. Mean HR was estimated from the 24-hour armband recordings from 16 volunteers in segments of 10 seconds each. In addition, four classical HR variability (HRV) parameters (SDNN, RMSSD, and powers at low and high frequency bands) were computed. For comparison purposes, the same parameters were estimated also for data from a commercial Holter monitor. The armband provided usable data (difference less than 10% from Holter-estimated mean HR) during 75.25%/11.02% (inter-subject median/interquartile range) of segments when the user was not in bed, and during 98.49%/0.79% of the bed segments. The automatic artifact detector found 53.85%/17.09% of the data to be usable during the non-bed time, and 95.00%/2.35% to be usable during the time in bed. The HRV analysis obtained a relative error with respect to the Holter data not higher than 1.37% (inter-subject median/interquartile range). Although further studies have to be conducted for specific applications, results suggest that the armband device has a good potential for daily life HR monitoring, especially for applications such as arrhythmia or seizure detection, stress assessment, or sleep studies

Quality Assessment of Ambulatory Electrocardiogram Signals by Noise Detection using Optimal Binary Classification

In order to improve the diagnostic capability in Ambulatory Electrocardiogram signal and to reduce the noise signal impacts, there is a need for more robust models in place. In terms of improvising to the existing solutions, this article explores a novel binary classifier that learns from the features optimized by fusion of diversity assessment measures, which performs Quality Assessment of Ambulatory Electrocardiogram Signals (QAAES) by Noise Detection. The performance of the proposed model QAAES has been scaled by comparing it with contemporary models. Concerning performance analysis, the 10-fold cross-validation has been carried on a benchmark dataset. The results obtained from experiments carried on proposed and other contemporary models for cross-validation metrics have been compared to signify the sensitivity, specificity, and noise detection accuracy

Sistema para aquisição e tratamento do sinal de ECG fetal

TCC (graduação) - Universidade Federal de Santa Catarina, Centro Tecnológico, Engenharia Eletrônica.No ano de 1902 foi inventado por Willem Einthoven o eletrocardiógrafo, ou eletrocardiograma (ECG), que representa a atividade elétrica cardíaca percebida de forma não invasiva pela superfície do corpo. Este pode ser registrado em papel milimetrado ou em formato digital. O ECG é muito utilizado na prática clínica para avaliação inicial de doenças cardíacas, podendo ser útil na avaliação tanto em adultos como em crianças, neonatos e até mesmo em fetos. O presente trabalho aborda o desenvolvimento de um sistema para a aquisição e processamento de um sinal de eletrocardiograma fetal utilizando um circuito contendo amplificadores e circuitos condicionadores além de um Raspberry Pi 3 para interface gráfica com o usuário. Antes da montagem do circuito em placa, foram realizadas simulações do circuito no software Tina-TI para validação dos circuitos. Como o Raspberry Pi 3 só possui entradas digitais foi implementada uma etapa de conversão AD antes de o Raspberry receber o sinal de ECG para processamento. Foi utilizada uma bateria externa (i.e., power bank) para alimentar o circuito de ECG e o Raspberry. Além dos componentes de hardware citados, foi utilizada uma ferramenta chamada Node-RED para conectar o Raspberry Pi à internet e fazer os gráficos de ECG. O Node-RED é utilizado para conectar dispositivos de hardware e adquirir dados, facilitando a visualização desses dados na internet em outros dispositivos. Os resultados obtidos com este trabalho foram satisfatórios, visto que o hardware desenvolvido permite fazer o registro e aquisição de dois canais de ECG simultâneos, necessários para aplicação em eletrocardiografia fetal.In 1902 Willem Einthoven invented the electrocardiograph, or electrocardiogram (ECG), which represents the cardiac electrical activity perceived non-invasively by the surface of the body. This can be recorded on graph paper or in digital format. The ECG is widely used in clinical practice for the initial assessment of cardiac diseases, and can be useful in the assessment of both adults and children, neonates and even fetuses. The present work deals with the development of a system for the acquisition and processing of a fetal electrocardiogram signal using a circuit containing amplifiers and conditioning circuits in addition to a Raspberry Pi 3 for graphical user interface. Before assembling the circuit on a board, circuit simulations were performed in the Tina-TI software to validate the circuits. As the Raspberry Pi 3 only has digital inputs, an AD conversion step was implemented before the Raspberry receives the ECG signal for processing. An external battery (i.e., power bank) was used to power the ECG circuit and the Raspberry. In addition to the hardware components mentioned, a tool called Node-RED was used to connect the Raspberry Pi to the internet and make the ECG graphs. Node-RED is used to connect hardware devices and acquire data, facilitating the visualization of this data on the internet on other devices. The results obtained with this work were satisfactory, since the developed hardware allows the recording and acquisition of two simultaneous ECG channels, necessary for application in fetal electrocardiography

Automatic motion and noise artifact detection in Holter ECG data using empirical mode decomposition and statistical approaches

We present a real-time method for the detection of motion and noise (MN) artifacts, which frequently interferes with accurate rhythm assessment when ECG signals are collected from Holter monitors. Our MN artifact detection approach involves two stages. The first stage involves the use of the first-order intrinsic mode function (F-IMF) from the empirical mode decomposition to isolate the artifacts\u27 dynamics as they are largely concentrated in the higher frequencies. The second stage of our approach uses three statistical measures on the F-IMF time series to look for characteristics of randomness and variability, which are hallmark signatures of MN artifacts: the Shannon entropy, mean, and variance. We then use the receiver-operator characteristics curve on Holter data from 15 healthy subjects to derive threshold values associated with these statistical measures to separate between the clean and MN artifacts\u27 data segments. With threshold values derived from 15 training data sets, we tested our algorithms on 30 additional healthy subjects. Our results show that our algorithms are able to detect the presence of MN artifacts with sensitivity and specificity of 96.63% and 94.73%, respectively. In addition, when we applied our previously developed algorithm for atrial fibrillation (AF) detection on those segments that have been labeled to be free from MN artifacts, the specificity increased from 73.66% to 85.04% without loss of sensitivity (74.48%-74.62%) on six subjects diagnosed with AF. Finally, the computation time was less than 0.2 s using a MATLAB code, indicating that real-time application of the algorithms is possible for Holter monitoring

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Data-driven methods for analyzing ballistocardiograms in longitudinal cardiovascular monitoring

Cardiovascular disease (CVD) is the leading cause of death in the US; about 48% of American adults have one or more types of CVD. The importance of continuous monitoring of the older population, for early detection of changes in health conditions, has been shown in the literature, as the key to a successful clinical intervention. We have been investigating environmentally-embedded in-home networks of non-invasive sensing modalities. This dissertation concentrates on the signal processing techniques required for the robust extraction of morphological features from the ballistocardiographs (BCG), and machine learning approaches to utilize these features in non-invasive monitoring of cardiovascular conditions. At first, enhancements in the time domain detection of the cardiac cycle are addressed due to its importance in the estimation of heart rate variability (HRV) and sleep stages. The proposed enhancements in the energy-based algorithm for BCG beat detection have shown at least 50% improvement in the root mean square error (RMSE) of the beat to beat heart rate estimations compared to the reference estimations from the electrocardiogram (ECG) R to R intervals. These results are still subject to some errors, primarily due to the contamination of noise and motion artifacts caused by floor vibration, unconstrained subject movements, or even the respiratory activities. Aging, diseases, breathing, and sleep disorders can also affect the quality of estimation as they slightly modify the morphology of the BCG waveform.Includes bibliographical reference