1/f Fluctuation of Heartbeat Period
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Abstract:
It is found that a heartbeat period fluctuation usually has a power spectral density which is inversely proportional to frequency, to which a spike is added at a breathing frequency.Keywords:
Heart beat
A non-contact heartbeat monitoring sensor using stepped-FM ultra-wideband (UWB) scheme is suggested which is robust to body movement. The biological signal should be obtained from periodic chest movement consisting of the heartbeat and respiration. However, the heartbeat sensing suffers from body movement because of its smaller displacement relative to breathing motion. Therefore, a stationary subject is assumed for conventional heartbeat monitoring schemes. However, some displacement of a body should be presented during the measurement. This paper suggests a heartbeat estimation scheme with high accuracy for some body movement. The estimation performance has been experimentally evaluated for four subjects sitting on the stool using our fabricated sensor. It is found that our proposed system can be achieved with the estimation error less than 2%.
Heart beat
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Conventional cardiac CINE imaging acquires segmented data over multiple heartbeats to satisfy sampling requirements. Recently, cardiac CINE reconstructions from a single heartbeat have been achieved using motion corrected reconstructions. This approach allows single heartbeat CINE and therefore can image the unique dynamics that occur in each heartbeat. Experiments in healthy subjects demonstrate that single heartbeat CINE is feasible and can detect heartbeat to heartbeat variation; the dynamics of individual heartbeats differ and cannot be detected with conventional CINE performed over multiple heartbeats. Single heartbeat CINE shows promise to characterize arrhythmias and other heart conditions where heartrate variability occurs.
Heart beat
Cardiac Imaging
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인터넷의 사용자가 급증하여 고가용성과 확장성을 지닌 고성능의 인터넷 서버들이 요구된다. 클러스터 시스템은 이러한 요구사항을 만족시킬 수 있는 서버이다. 본 연구에서는 N-node heartbeat 을 구현하였고, 이것을 기반으로 하는 고가용 부하분산 클러스터, PersistentCluster를 구현하였다. PersistentCluster는 로드벨런서가 사용자의 요구를 서버들에게 분산시켜주는 LVS 시스템에서 로드벨런서가 고장나면 나머지 서버중에 하나가 그 역할을 인계 받아 계속 수행하는 고가용성 클러스터링 솔루션이다. PersistentCluster는 로드벨런서만 heartbeat 메시지를 전송하는 비대칭 heartbeat을 채택하여 시스템의 메시지 전송 및 처리 오버헤드를 감소시켰다. 비대칭 heartbeat을 적용할 경우에 나타나는 각 노드의 부하 감소량을 실측하여 비대칭 heartbeat 의 성능을 평가하였다.
Heart beat
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Objective To separate the heartbeat signal from the mixsignal of the respiration and heartbeat,which was measured via contact-free biological radar.Methods A heartbeat signal separate method was proposed by using recursive-least square(RLS) algorithm based adaptive noise canceler.Results RLS algorithm based adaptive noise canceler could isolate the heartbeat signal,and there was a strong correlation between the heart rate and heart rate obtained from the ECG signal(γ2 = 0.95,P 0.000 1).Conclusion The heartbeat signal separation method present possesses good practicality,which is expected to achieve the real-time separation of respiration and heartbeat signal.
Heart beat
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Heart beat
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ABSTRACT The present study investigated the effect of a self‐attention enhancing manipulation on heartbeat perception in 22 females using a within‐subject design. Self‐attention was enhanced by placing a mirror in front of the subjects. Heartbeat perception was assessed using both heartbeat discrimination and heartbeat tracking procedures. It was found that subjects performed heartbeat discrimination more accurately when facing a mirror but heartbeat tracking performance was not affected by the same manipulation. The possible reasons for these conflicting results are discussed.
Heart beat
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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The nationally-recognized Susquehanna
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Heartbeat detection is one of key techniques to monitor our health condition in daily life, and demands for this technique have increased year and year. Thanks to the non-contact and non-invasive features, various Doppler sensor-based detection methods have been investigated so far. However, the heartbeat detection accuracy of the conventional methods could get degraded due to the low SNR (Signal-to-Noise Ratio) of heartbeat components. Thus, even after some signal processing, non-heartbeat components still remain over such processed signal, which could degrade the heartbeat detection accuracy. In particular for the subjects with low HR (Heart Rate), the estimated HR tends to be higher than the ground truth HR due to such non-heartbeat components, though the conventional methods have mainly focused on the heartbeat detection against the subjects with the normal HR higher than 50 bpm (Beats Per Minute). In this paper, to accurately detect heartbeat even with low HR via a Doppler sensor, we propose a heartbeat detection method based on heartbeat signal reconstruction with convolutional LSTM (Bidirectional-Long Short-Term Memory). In the proposed method, to reconstruct a heartbeat signal based on the periodicity of heartbeat and the spectrum distribution peculiar to heartbeat, successive spectrograms that might be due to heartbeat is used as an input to convolutional LSTM. In addition, for better reconstruction of a heartbeat signal, the previously estimated RRI (R-R Interval) is also used as a feature in the proposed deep learning model with convolutional LSTM. Through the experiments, we confirmed that our proposed method accurately detected heartbeat against 17 subjects including the ones with the HR lower than 50 bpm.
Heart beat
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