An innovative hybrid approach for detection of pacemaker pulses at low sampling frequency

Nallathambi, Gabriel; Selvaraj, Nandakumar; Rajbhandary, Paurakh L.

doi:10.1109/embc44109.2020.9176390

Cited by 3 publications

(6 citation statements)

References 6 publications

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“…Accuracy of the designed threshold-based algorithm for pace pulses detection over the test dataset (390 clean ECGs, 312 ECG+EMG) for K = 10. Se and PPV achieved in our previous study over the same datasets[22], as well as Se and PPV reported by Nallathambi et al[18].…”

supporting

confidence: 88%

“…The results reported by Nallathambi et al [18] are comparable to those achieved in this study, although the methodology is quite different. In [18] the authors applied the final detection procedure over an ECG sampled at a relatively low sampling frequency (250 Hz). This is done at the cost of specific hardware preprocessing that suppresses everything except the pace pulses in the analogue ECG.…”

supporting

confidence: 87%

“…The database involves a total of 1404 recordings with annotated positions of the pace pulses, and it has been previously applied for training and test purposes in [18,22]. The total number of clean ECGs with pace pulses is 780, while the paced ECGs that contain EMG noise are 624.…”

Section: Ecg Databasementioning

confidence: 99%

“…Recently, a hybrid approach for pace pulses detection operating at low sampling frequency has been proposed [18]. The method utilizes simple analog processing to extract the pace pulses energy via filters tuned to capture spectral contents outside ECG frequencies of interest, followed by straightforward digital pace pulse detection at a low sampling rate.…”

mentioning

confidence: 99%

“…Comparisons of the performance of the method presented in this study to the accuracies of methods for pace pulses detection reported in the literature are presented in Table 3. It includes an algorithm previously designed by us [22], which was tested over the ECG signals from the test dataset described in section 'ECG database', as well as to a recently reported algorithm [18], which was trained and tested also on 'ArtPacedECGdb' database. In a previous work [22] we have shown that the EMG artifacts significantly reduce the PPV of an algorithm designed to detect pace pulses in the time domain (see Table 3).…”

mentioning

confidence: 99%

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Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

2020

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Electrocardiogram (ECG) analysis is important for the detection of pace pulse artifacts, since their existence indicates the presence of a pacemaker. ECG gives information on the proper functionality of the device and could help to evaluate the reaction of the heart. Beyond the challenges related to the diversity of ECG arrhythmias and pace pulses, the existence of electromyogram (EMG) noise could cause serious problems for the correct detection of pace pulses. This study reveals the potential of a methodology based on Stockwell transformation (S-transform), subsequent Shannon energy calculation and a threshold-based rule for pace artifact detection in a single-lead ECG corrupted with EMG noise. The design, validation and test are performed on a large, publicly available artificial database acquired with high amplitude and time resolution. It includes various combinations of ECG arrhythmias and pace pulses with different amplitudes, rising edges and total pulse durations, as well as timing that corresponds to different pacemaker modes. The training was done over 312 (ECG + EMG) signals. The method was validated on 390 clean ECGs and independently tested on 312 (ECG + EMG) and 390 clean ECGs. The achieved accuracy over the test dataset was Se = 100%, PPV = 98.0% for ECG corrupted by EMG artifacts and Se = 99.9%, PPV = 98.3% for clean ECG signals. This shows that, despite EMG artifacts, the S-transform could distinctly localize the pace pulse positions and, together with the applied ShE, could provide precise pace pulses detection in the time domain.

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supporting

confidence: 88%

supporting

confidence: 87%

Section: Ecg Databasementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

2020

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ECG Signal Quality Assessments of a Small Bipolar Single-Lead Wearable Patch Sensor

Rajbhandary

Nallathambi

Selvaraj

et al. 2022

Cardiovasc Eng Tech

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Purpose There is an increasing clinical interest in the adoption of small single-lead wearable ECG sensors for continuous cardiac monitoring. The purpose of this work is to assess ECG signal quality of such devices compared to gold standard 12-lead ECG. Methods The ECG signal from a 1-lead patch was systematically compared to the 12-lead ECG device in thirty subjects to establish its diagnostic accuracy in terms of clinically relevant signal morphology, wave representation, fiducial markers and interval and wave duration. One minute ECG segments with good signal quality was selected for analysis and the features of ECG were manually annotated for comparative assessment. Results The patch showed closest similarity based on correlation and normalized root-mean-square error to the standard ECG leads I, II, and . P-wave and QRS complexes in the patch showed sensitivity (Se) and positive predictive value (PPV) of at least 99.8% compared to lead II. T-wave representation showed Se and PPV of at least 99.9% compared to lead and . Mean errors for onset and offset of the ECG waves, wave durations, and ECG intervals were within 2 samples based on 125Hz patch ECG sampling frequency. Conclusion This study demonstrates the diagnostic capability with similar morphological representation and reasonable timing accuracy of ECG signal from a patch sensor compared to 12-lead ECG. The advantages and limitations of small bipolar single-lead wearable patch sensor compared to 12-lead ECG are discussed in the context of relevant differences in ECG signal for clinical applications.

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Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

Antikainen

Njoum²,

Kudelka³

et al. 2022

Front. Physiol.

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Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.

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An innovative hybrid approach for detection of pacemaker pulses at low sampling frequency

Cited by 3 publications

References 6 publications

Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

Application of Stockwell Transform and Shannon Energy for Pace Pulses Detection in a Single-Lead ECG Corrupted by EMG Artifacts

ECG Signal Quality Assessments of a Small Bipolar Single-Lead Wearable Patch Sensor

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study

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