Accelerometer-Based Method for Extracting Respiratory and Cardiac Gating Information for Dual Gating during Nuclear Medicine Imaging

Tadi, Mojtaba Jafari; Koivisto, Tero; Pänkäälä, Mikko; Paasio, A.

doi:10.1155/2014/690124

Cited by 57 publications

(30 citation statements)

References 18 publications

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“…Algorithm presented by Tadi et al in 2015 [25] uses R waves as reference points and is based on the windowing method proposed in papers [36, 37]. The first step of the algorithm is applying a band-pass filter with cut-off frequencies of 4 Hz and 50 Hz.…”

Section: Methodsmentioning

confidence: 99%

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

2019

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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 ( , ) despite lower performance on noisy signals. Correlation between HRV indices was calculated as coefficient of determination ( ) to determine goodness of fit to linear model. The highest values were obtained for mean interbeat interval ( for reference algorithm, in the worst case), and ( for the best case, for the worst case) and the lowest were obtained for ( in the worst case). Using robust model improved achieved correlation between HRV indices obtained from ECG and SCG signals except the 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 . Relationship of HRV indices derived from ECG and SCG was influenced by used beat detection method on SCG signal.

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Section: Methodsmentioning

confidence: 99%

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

2019

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“…Band-stop and band-pass filters can address resolving power line noise [56]. More generally different types of band-pass filtering are repeatedly incorporated to limit any out-of-band noise when the frequency band of interest is known [26,37,41,57].…”

Section: Robustness and Artifact Cancellationmentioning

confidence: 99%

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Rahmani

Berkvens

Weyn

2021

Sensors

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Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.

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“…Beside respiration (< 1 Hz), low frequency (0.6 to 5 Hz) chest wall motions related with heart muscle contraction and high frequency (> 5 Hz) chest wall vibrations related with acoustic waves of the valve closing are measured [25, 28]. Both signals can be used to detect respiration and heartbeats [29].…”

Section: Alternative Approaches For Assessing Heart Activitymentioning

confidence: 99%

Detection and analysis of pulse waves during sleep via wrist-worn actigraphy

et al. 2019

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The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

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Accelerometer-Based Method for Extracting Respiratory and Cardiac Gating Information for Dual Gating during Nuclear Medicine Imaging

Cited by 57 publications

References 18 publications

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

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

Chest-Worn Inertial Sensors: A Survey of Applications and Methods

Detection and analysis of pulse waves during sleep via wrist-worn actigraphy

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