Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line

Escalona, OJ; Mukhtar, Sephorah; McEneaney, David; Finlay, Dewar

doi:10.3390/s22197240

Cited by 3 publications

(7 citation statements)

References 29 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Beat-by-beat (BbyB) segmentation of each subject case for the simultaneous ICG and ECG recordings (for both arm and thorax ICG modes) was based on the single fiducial point (SFP) technique previously reported [7,23,24], which was applied to the ECG signal recorded in a particular ICG mode and subject case as the reference signal for extracting the accurate time position of valid ventricular depolarisation events of QRS complexes along the whole ECG recording. Therefore, the output from the SFP process is a vector of sample numbers (n) of valid heartbeat QRS complexes at the SFP position along the 480 s simultaneous ICG and ECG recording length.…”

Section: Beat-by-beat Recursive Ensemble Averaging Denoising Process ...mentioning

confidence: 99%

“…Therefore, the output from the SFP process is a vector of sample numbers (n) of valid heartbeat QRS complexes at the SFP position along the 480 s simultaneous ICG and ECG recording length. Thus, the heartbeat sample positions SFP vector was used for the accurate segmentation of both the simultaneous ICG and the ECG signals by defining 1400 samples (700 ms) centred around each sample point (element) in the SFP vector [23].…”

Section: Beat-by-beat Recursive Ensemble Averaging Denoising Process ...mentioning

confidence: 99%

“…Following the ICG basic features determination and analysis within a particular ICG recording mode (arm or thorax), the final stage of the study investigated the prospects and extent of considering the Arm-ICG method as a scaled surrogate for the conventional Thorax-ICG, and hence, the potential intrinsic advantages and limitations offered by Arm-ICG methods in ambulatory, long-term cardiac contractility or SV monitoring. Therefore, besides using scatter plots, Pearson correlation (p) and linear trend modelling with the associated coefficient of determination (R 2 ) [22,23], five functional relationship metrics (D, E, F, G and H) of Arm-versus Thorax-ICGs, as ratio or difference values, were evaluated and analysed for all subject cases in this study (N = 15) using descriptive statistics. These Arm-Thorax relational metrics were defined as follows:…”

Section: Functional Relationship Between Arm-icg and Thorax-icg Featu...mentioning

confidence: 99%

“…Statistically, outliers are the data points that differ significantly from other observations, which may cause distortion and skewed data, leading to inaccurate analysis of the results. For the exclusion of such values, output data vectors were further processed in Matlab using the rmoutliers command [22,23,40].…”

Section: Removing Outliersmentioning

confidence: 99%

“…Further robust denoising methods are required for addressing Arm-ICG signal quality enhancement issues in such a way that makes the signal quality clinically acceptable and attractive for long-term ambulatory cardiac contractility (SV) monitoring for the benefit of cardiac patients. Based on previous knowledge of effective Arm-ICG and Arm-ECG denoising [7,[22][23][24][25][26][27][28][29] methods, a satisfactory and robust solution was investigated by means of advanced processes, such as discrete wavelet transform (DWT)-based filtering techniques, supported by ensemble averaging algorithms for cardiac deterministic ICG events [30], both in the Arm-ICG and Thorax-ICG on a beat-by-beat (BbyB) basis. The widely recommended mother wavelet configuration used in DWT-based denoising approaches for ICG signals is the Daubechies wavelet, and more specifically, the fourth-order (Db4) and the eighth-order (Db8) Dabechies wavelets were reported to be suitable for the ICG signal [14,17,22,28,29].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Escalona,

Cullen,

Weli

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Impedance cardiography (ICG) is a low-cost, non-invasive technique that enables the clinical assessment of haemodynamic parameters, such as cardiac output and stroke volume (SV). Conventional ICG recordings are taken from the patient’s thorax. However, access to ICG vital signs from the upper-arm brachial artery (as an associated surrogate) can enable user-convenient wearable armband sensor devices to provide an attractive option for gathering ICG trend-based indicators of general health, which offers particular advantages in ambulatory long-term monitoring settings. This study considered the upper arm ICG and control Thorax-ICG recordings data from 15 healthy subject cases. A prefiltering stage included a third-order Savitzky–Golay finite impulse response (FIR) filter, which was applied to the raw ICG signals. Then, a multi-stage wavelet-based denoising strategy on a beat-by-beat (BbyB) basis, which was supported by a recursive signal-averaging optimal thresholding adaptation algorithm for Arm-ICG signals, was investigated for robust signal quality enhancement. The performance of the BbyB ICG denoising was evaluated for each case using a 700 ms frame centred on the heartbeat ICG pulse. This frame was extracted from a 600-beat ensemble signal-averaged ICG and was used as the noiseless signal reference vector (gold standard frame). Furthermore, in each subject case, enhanced Arm-ICG and Thorax-ICG above a threshold of correlation of 0.95 with the noiseless vector enabled the analysis of beat inclusion rate (BIR%), yielding an average of 80.9% for Arm-ICG and 100% for Thorax-ICG, and BbyB values of the ICG waveform feature metrics A, B, C and VET accuracy and precision, yielding respective error rates (ER%) of 0.83%, 11.1%, 3.99% and 5.2% for Arm-IG, and 0.41%, 3.82%, 1.66% and 1.25% for Thorax-ICG, respectively. Hence, the functional relationship between ICG metrics within and between the arm and thorax recording modes could be characterised and the linear regression (Arm-ICG vs. Thorax-ICG) trends could be analysed. Overall, it was found in this study that recursive averaging, set with a 36 ICG beats buffer size, was the best Arm-ICG BbyB denoising process, with an average of less than 3.3% in the Arm-ICG time metrics error rate. It was also found that the arm SV versus thorax SV had a linear regression coefficient of determination (R2) of 0.84.

show abstract

Section: Beat-by-beat Recursive Ensemble Averaging Denoising Process ...mentioning

confidence: 99%

Section: Beat-by-beat Recursive Ensemble Averaging Denoising Process ...mentioning

confidence: 99%

Section: Functional Relationship Between Arm-icg and Thorax-icg Featu...mentioning

confidence: 99%

Section: Removing Outliersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Escalona,

Cullen,

Weli

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

Heartbeat detection from high-density EMG electrodes on the upper arm at different EMG intensity levels using Zephlet

Beni,

Jiang

2023

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Person identification with arrhythmic ECG signals using deep convolution neural network

Al-Jibreen,

Al-Ahmadi,

Islam

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person’s beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

show abstract

Armband Sensors Location Assessment for Left Arm-ECG Bipolar Leads Waveform Components Discovery Tendencies around the MUAC Line

Cited by 3 publications

References 29 publications

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Robust Arm Impedocardiography Signal Quality Enhancement Using Recursive Signal Averaging and Multi-Stage Wavelet Denoising Methods for Long-Term Cardiac Contractility Monitoring Armbands

Heartbeat detection from high-density EMG electrodes on the upper arm at different EMG intensity levels using Zephlet

Person identification with arrhythmic ECG signals using deep convolution neural network

Contact Info

Product

Resources

About