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2.
J Cardiothorac Surg ; 15(1): 296, 2020 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-33008451

RESUMO

BACKGROUND: The Surgical Pleth Index (SPI) is a monitoring method that reflects painful stimuli during general anesthesia, and dexmedetomidine is an analgesic adjuvant with an opioid-sparing effect. But up to now, it is still unclear whether dexmedetomidine has any influence on SPI. To investigate whether dexmedetomidine has an effect on SPI during video-assisted thoracoscopic surgery. METHODS: We enrolled 94 patients who underwent video-assisted thoracoscopic lung lobectomy. Patients were randomly assigned to a dexmedetomidine group (dexmedetomidine: 0.8 µg/kg administered for 10 min before anesthesia) or normal saline group (equal volume of normal saline). SPI and vital signs were recorded. The number rating scale (NRS) pain score was also evaluated. RESULTS: SPI values were significantly lower in the dexmedetomidine group than in the normal saline group at intubation and at discharge from the postanesthesia care unit. Compared with the normal saline group, mean arterial pressure and heart rate were both significantly lower in the dexmedetomidine group at intubation. Heart rate was lower at skin incision in the dexmedetomidine group. The NRS score in the normal saline group was noticeably higher vs. the dexmedetomidine group at discharge from the postanesthesia care unit. CONCLUSIONS: Dexmedetomidine decreased intraoperative SPI and NRS scores. Our results showed that dexmedetomidine attenuated noxious stimuli. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR): ChiCTR-OOC-16009450 , Registered 16 October, 2016.


Assuntos
Analgésicos não Entorpecentes/uso terapêutico , Dexmedetomidina/uso terapêutico , Pneumopatias/cirurgia , Adolescente , Adulto , Idoso , Anestesia Geral , Feminino , Frequência Cardíaca/efeitos dos fármacos , Humanos , Cuidados Intraoperatórios , Masculino , Pessoa de Meia-Idade , Dor Pós-Operatória , Pneumonectomia , Estudos Prospectivos , Cirurgia Torácica Vídeoassistida , Adulto Jovem
3.
Artigo em Inglês | MEDLINE | ID: mdl-33017916

RESUMO

A sufficient oxygen supply of the fetus is necessary for a proper development of the organs. Transabdominal fetal pulse oximetry is a method that allows to measure the oxygenation of the fetal blood non-invasively by placing the light sources and photodetectors on the belly of the pregnant woman. The shape of the measured fetal pulse wave is needed to extract parameters for the estimation of the oxygen saturation. This work presents an extension of our previously presented signal processing strategy that allows to extract an average shape of the fetal pulse wave from noisy mixed photoplethysmograms (PPG) with dominating maternal and very weak fetal signal components. An adaptive noise canceller and a comb filter are used to suppress the maternal component. The quality of the resulting fetal signal is sufficient to identify single pulse waves in time domain. Further processing demonstrates the extraction of the mean shape of a single fetal pulse wave by synchronous averaging of several detected pulses. The method is evaluated with different datasets of several simulated and synthetic signals measured with a tissue mimicking phantom. The feasibility of the approach is demonstrated by preparing the mixed PPGs to perform fetal pulse oximetry in future studies. However, clinical measurements are needed to finally evaluate the proposed system beyond synthetic datasets.


Assuntos
Monitorização Fetal , Oxigênio , Feminino , Frequência Cardíaca , Humanos , Oximetria , Gravidez , Cuidado Pré-Natal
4.
Artigo em Inglês | MEDLINE | ID: mdl-33017920

RESUMO

Cardiography enables diagnostic and preventive care in hospitals and outpatient scenarios. However, most heart monitors do not distinguish the phases of the cardiac cycle. The transition between phases is indicated by the primary heart sounds. OBJECTIVE: Automatically identify the vibrations corresponding to both heart sounds. METHODS: Cardiac activity was monitored for 15 subjects while at rest, during exertion, and while performing static breath holds. The subjects consisted of 6 males and 9 females between the ages of 18-39 years with no known cardiorespiratory ailments. Motion corresponding to the heart sounds was identified using vibrational cardiography (VCG). The waveforms were processed to obtain quantities associated with their linear jerk and rotational kinetic energy. RESULTS: The ability to identity the first vibration was evaluated using the heart rate as a figure of merit. Its correlation with electrocardiography (ECG) measurements produced a r2 coefficient of 0.9887. The second vibration was compared with impedance cardiography (ICG) based on its delay from the ECG R-peak, and the fraction of the beat duration occupied by left ventricular ejection time. The comparisons produced r2 values of 0.251 and 0.2797, respectively. CONCLUSION: The vibrations corresponding to both primary heart sounds have the potential to be analyzed using VCG. SIGNIFICANCE: This study provides evidence of the feasibility of using VCG in identifying mechanical cardiovascular function. It facilitates non-invasive cardiac health monitoring in daily life.


Assuntos
Ruídos Cardíacos , Adolescente , Adulto , Cardiografia de Impedância , Eletrocardiografia , Feminino , Frequência Cardíaca , Humanos , Masculino , Vibração , Adulto Jovem
5.
Artigo em Inglês | MEDLINE | ID: mdl-33017927

RESUMO

Vagal Nerve Stimulation (VNS) is an option in the treatment of drug-resistant epilepsy. However, approximately a quarter of VNS subjects does not respond to the therapy. In this retrospective study, we introduce heart-rate features to distinguish VNS responders and non-responders. Standard pre-implantation measurements of 66 patients were segmented in relation to specific stimuli (open/close eyes, photic stimulation, hyperventilation, and rests between). Median interbeat intervals were found for each segment and normalized (NMRR). Five NMRRs were significant; the strongest feature achieved significance with p=0.013 and AUC=0.66. Low mutual correlation and independence on EEG signals mean that presented features could be considered as an addition for models predicting VNS response using EEG.


Assuntos
Epilepsia , Estimulação do Nervo Vago , Eletroencefalografia , Epilepsia/terapia , Frequência Cardíaca , Humanos , Estudos Retrospectivos
6.
Artigo em Inglês | MEDLINE | ID: mdl-33017938

RESUMO

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASympn) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HFn, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsympn displayed a reciprocal trend with respect to HFn: an initial increase (decrease for HFn) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity.


Assuntos
Resposta Galvânica da Pele , Jogo de Azar , Algoritmos , Nível de Alerta , Frequência Cardíaca , Humanos
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 232-235, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017971

RESUMO

A 24GHz Doppler radar system for accurate contactless monitoring of heart and respiratory rates is demonstrated here. High accuracy predictions are achieved by employing a CNN+LSTM neural network architecture for regression analysis. Detection accuracies of 99% and 98% have been attained for heart rate and respiration rate, respectively.Clinical Relevance- This work establishes a non-contact radar system with 99% detection accuracy for a heart rate variability warning system. This system can enable convenient and fast monitoring for daily care at home.


Assuntos
Algoritmos , Redes Neurais de Computação , Frequência Cardíaca , Respiração , Taxa Respiratória
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 304-307, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017989

RESUMO

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia diagnosis in clinical practice. In recent years, various algorithms based on deep learning have been proposed to solve the heartbeat classification problem and achieved saturated accuracy in intrapatient paradigm, but encountered performance degradation in inter-patient paradigm due to the drastic variation of ECG signals among different individuals. In this paper, we propose a novel unsupervised domain adaptation scheme to address this problem. Specifically, we first propose a robust baseline model called Multi-path Atrous Convolutional Network (MACN) to tackle ECG heartbeat classification. Further, we introduce Cluster-aligning loss and Cluster-separating loss to align the distributions of training and test data and increase the discriminability, respectively. The proposed method requires no expert annotations but a short period of unlabelled data in new records. Experimental results on the MIT-BIH database demonstrate that our scheme effectively intensifies the baseline model and achieves competitive performance with other state-of-the-arts.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Algoritmos , Arritmias Cardíacas/diagnóstico , Frequência Cardíaca , Humanos
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 316-319, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017992

RESUMO

Atrial fibrillation (AF) is a common heart rhythm which occurs when the upper chambers of the heart beat irregularly. With the rapid development of the deep learning algorithm, the Convolutional Neural Networks (CNN) is widely investigated for the ECG classification task. However, for AF detection, the performance of CNN is greatly limited due to the lack of consideration for temporal characteristic of the ECG signal. In order to improve the discriminative ability of CNN, we introduce the attention mechanism to help the network concentrate on the informative parts and obtain the temporal features of the signals. Inspired by this idea, we propose a temporal attention block (TA-block) and a temporal attention convolutional neural network (TACNN) for the AF detection tasks. The TA-block can adaptively learn the temporal features of the signal and generate the attention weights to enhance informative features. With a stack architecture of TA-blocks, the TA-CNN obtains better performance as a result of paying more attention to the informative parts of the signal. We validate our approach on the single lead ECG classification dataset of The PhysioNet Computing in Cardiology Challenge 2017. The experimental results indicate that the proposed framework outperform state-of-the-arts classification networks.Clinical Relevance-The proposed algorithm can be potentially applied to the portable cardiovascular monitoring devices reducing the danger of AF.


Assuntos
Fibrilação Atrial , Algoritmos , Fibrilação Atrial/diagnóstico , Eletrocardiografia , Frequência Cardíaca , Humanos , Redes Neurais de Computação
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 320-323, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017993

RESUMO

This paper presents a simple yet novel method to estimate the heart frequency (HF) of neonates directly from the ECG signal, instead of using the RR-interval signals as generally done in clinical practices. From this, the heart rate (HR) can be derived. Thus, we avoid the use of peak detectors and the inherent errors that come with them.Our method leverages the highest Power Spectral Densities (PSD) of the ECG, for the bins around the frequencies related to heart rates for neonates, as they change in time (spectrograms).We tested our approach with the monitoring data of 6 days for 52 patients in a Neonate Intensive Care Unit (NICU) and compared against the HR from a commercial monitor, which produced a sample every second. The comparison showed that 92.4% of the samples have a difference lower than 5bpm. Moreover, we obtained a median MAE (Mean Absolute Error) between subjects equal to 2.28 bpm and a median RMSE (Root Mean Square Error) equal to 5.82 bpm. Although tested for neonates, we hypothesize that this method can also be customized for other populations.Finally, we analyze the failure cases of our method and found a direct co-allocation of errors due to moments with higher PSD in the lower frequencies with the presence of critical alarms related to other physiological systems (e.g. desaturation).


Assuntos
Eletrocardiografia , Unidades de Terapia Intensiva Neonatal , Algoritmos , Frequência Cardíaca , Humanos , Recém-Nascido , Processamento de Sinais Assistido por Computador
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 324-327, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017994

RESUMO

In this paper, a new simple index has been introduced for the assessment of electrocardiography (ECG) signal quality. In the proposed method, first, the initial spectrum of the ECG is derived by applying synchrosqueezed wavelet transform (SSWT). Then, the main frequency rhythm of heart rate with maximum-energy embedded in the spectrum of the ECG signal is reconstructed using time-frequency ridge estimation algorithm. The ridge is subjected to the inverse SSW and SSW subsequently to reconstruct a clear spectrum corresponding to the main heart rhythm. Subtracting it from the initial spectrum, the resulting differential spectrum is converted to a single time-series by simply summing all the energy levels at each time-point. It has been shown that the derived time-series is proportional to the quality of ECG signal in terms of preserving its physiological features. The results of this research provide a profound basis for signal quality assessment of both ECG and photoplethysmography (PPG) signals under various noisy conditions and abnormal heart rate.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Frequência Cardíaca , Fotopletismografia , Análise de Ondaletas
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 341-344, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017998

RESUMO

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients. In this paper, we propose an adversarial multitask learning method to improve the generalization of heartbeat arrythmia classification. We built an end-to-end deep neural network (DNN) system consisting of three sub-networks; a generator, a heartbeat-type discriminator, and a subject (or patient) discriminator. Each of these two discriminators had its own loss function to control its impact. The generator was "friendly" to the heartbeat-type discrimination task by minimizing its loss function and "hostile" to the subject discrimination task by maximizing its loss function. The network was trained using raw ECG signals to discriminate between five types of heartbeats - normal heartbeats, right bundle branch blocks (RBBB), premature ventricular contractions (PVC), paced beats (PB) and fusion of ventricular and normal beats (FVN). The method was tested with the MIT-BIH arrhythmia dataset and achieved a 17% reduction in classification error compared to a baseline using a fully-connected DNN classifier.Clinical Relevance-This work validates that it is possible to develop a subject-independent automated heart arrhythmia detection system to assist clinicians in the diagnosis process.


Assuntos
Eletrocardiografia , Complexos Ventriculares Prematuros , Frequência Cardíaca , Humanos , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 345-348, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017999

RESUMO

Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(CVD) diagnosis. Although there have been numerous approaches that have successfully addressed the problem, there has been a notable dip in the performance of these existing detectors on ECG episodes that contain noise and HRV Irregulates. On the other hand, Deep Learning(DL) based methods have shown to be adept at modelling data that contain noise. In image to image translation, Unet is the fundamental block in many of the networks. In this work, a novel application of the Unet combined with Inception and Residual blocks is proposed to perform the extraction of R-peaks from an ECG. Furthermore, the problem formulation also robustly deals with issues of variability and sparsity of ECG R-peaks. The proposed network was trained on a database containing ECG episodes that have CVD and was tested against three traditional ECG detectors on a validation set. The model achieved an F1 score of 0.9837, which is a substantial improvement over the other beat detectors. Furthermore, the model was also evaluated on three other databases. The proposed network achieved high F1 scores across all datasets which established its generalizing capacity. Additionally, a thorough analysis of the model's performance in the presence of different levels of noise was carried out.


Assuntos
Aprendizado Profundo , Processamento de Sinais Assistido por Computador , Algoritmos , Eletrocardiografia , Frequência Cardíaca
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 353-356, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018001

RESUMO

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.


Assuntos
Bloqueio de Ramo , Eletrocardiografia , Arritmias Cardíacas/diagnóstico , Bloqueio de Ramo/diagnóstico , China , Frequência Cardíaca , Humanos
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 426-429, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018019

RESUMO

Catheter ablation is a common treatment of atrial fibrillation (AF), but its success rate is around 60%. It is believed that the success rate can be improved if the procedure were to be guided by the specific AF triggers found in the "Flashback", i.e. the trend of around 500 ventricular beats preceding the AF onset stored in an implantable cardiac monitor (ICM). The need to automatically classify these different triggers: atrial tachycardia (AT), atrial flutter, premature atrial contractions (PAC) or Spontaneous AF has motivated the design in this paper of an unsupervised classification method evaluating statistical and geometrical Heart Rate Variability (HRV) features extracted from the Flashback. From a cohort of 132 patients (57± 12 years, male 67%), 528 Flashbacks were extracted and classified into 5 different clusters after the Principal Component Analysis (PCA) was computed on the HRV features. 2 principal components explained more than 95% of the variance and were a combination of the mean R-R interval, Square root of the mean squared differences of successive R-R intervals (RMSSD), Standard deviation of the R-R intervals (SDNN) and Poincare descriptors, SD1 and SD2. RMSSD and SD1 were significantly different among all clusters (p-value < 0.05, with Holm's correction) showing that distinct patterns can be found using this method.Clinical Relevance-Preliminary step towards ablation strategy guidance using the AF trigger patterns to improve catheter ablation success rates.


Assuntos
Fibrilação Atrial , Flutter Atrial , Ablação por Cateter , Fibrilação Atrial/cirurgia , Eletrocardiografia , Frequência Cardíaca , Humanos , Masculino
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 451-454, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018025

RESUMO

Inspired by the application of recurrent neural networks (RNNs) to image recognition, in this paper, we propose a heartbeat detection framework based on the Gated Recurrent Unit (GRU) network. In this contribution, the heartbeat detection task from ballistocardiogram (BCG) signals was modeled as a classification problem where the segments of BCG signals were formulated as images fed into the GRU network for feature extraction. The proposed framework has advantages in fusion of multi-channel BCG signals and effective extraction of the temporal and waveform characteristics of the heartbeat signal, thereby enhancing heart rate estimation accuracy. In laboratory collected BCG data, the proposed method achieved the best heart rate estimation results compared to previous algorithms.


Assuntos
Balistocardiografia , Algoritmos , Coleta de Dados , Frequência Cardíaca , Redes Neurais de Computação
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 455-460, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018026

RESUMO

Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.


Assuntos
Balistocardiografia , Algoritmos , Coração , Frequência Cardíaca , Modelos Lineares
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 461-464, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018027

RESUMO

During running, interactions were considered between three physiological oscillators - the heart, breaths, and steps. During intense exercise, the oscillations of all three systems are close to regular, producing good conditions to observe and characterise synchronization. The origin, as well as any physiological significance, of synchronization between these systems during running is not fully accepted or understood. Furthermore, the impact on synchronization of controlling both breathing and step rate has not been previously reported in detail. This study aims to measure cardiolocomotor, cardiorespiratory and respiratory- locomotor synchronization during different running protocols. Breathing was controlled by taking a fixed number of steps per breath (ratios of 5:1 and 3:1). Step rate was then guided at rates close to active heart rate, to instigate 1:1 phase-locking. Instantaneous phase difference quantified synchronization episodes. We have successfully observed all three forms of synchronization during all running protocols. Furthermore, coupling between heartbeats and steps was more pronounced when step rate was guided, and both cardiorespiratory and respiratory-locomotor coupling were extended when breathing rate was fixed to steps. These are exciting initial results from a novel experimental design, highlighting the complex interconnection that exists between these three systems during running, and the conditions to best observe the phenomena.


Assuntos
Corrida , Coração , Frequência Cardíaca , Respiração , Taxa Respiratória
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 465-468, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018028

RESUMO

Monitoring vital signs of neonates can be harmful and lead to developmental troubles. Ballistocardiography, a contactless heart rate monitoring method, has the potential to reduce this monitoring pain. However, signal processing is uneasy due to noise, inherent physiological variability and artifacts (e.g. respiratory amplitude modulation and body position shifts). We propose a new heartbeat detection method using neural networks to learn this variability. A U-Net model takes thirty-second-long records as inputs and acts like a nonlinear filter. For each record, it outputs the samples probabilities of belonging to IJK segments. A heartbeat detection algorithm finally detects heartbeats from those segments, based on a distance criterion. The U-Net has been trained on 30 healthy subjects and tested on 10 healthy subjects, from 8 to 74 years old. Heartbeats have been detected with 92% precision and 80% recall, with possible optimization in the future to achieve better performance.


Assuntos
Balistocardiografia , Adolescente , Adulto , Idoso , Algoritmos , Criança , Frequência Cardíaca , Humanos , Recém-Nascido , Pessoa de Meia-Idade , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador , Adulto Jovem
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 477-480, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018031

RESUMO

The continuous-wave Doppler radar measures the movement of a chest surface including of cardiac and breathing signals and the body movement. The challenges associated with extracting cardiac information in the presence of respiration and body movement have not been addressed thus far. This paper presents a novel method based on the windowed-singular spectrum analysis (WSSA) for solving this issue. The algorithm consists of two processes: signal decomposition via WSSA followed by the reconstruction of decomposed heartbeat signals through convolution. An experiment was conducted to collect chest signals in 212 people by Doppler radar. In order to confirm the effect of reducing the large noise by the proposed method, we evaluated 136 signals that were considered to contain respiration body movements from the collected signals. When comparing to the performance of a band-pass filter, the proposed analysis achieves improved beat count accuracy. The results indicate its applicability to contactless heartbeat estimation under involving respiration and body movements.


Assuntos
Radar , Processamento de Sinais Assistido por Computador , Frequência Cardíaca , Respiração , Análise Espectral
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