Simulating Motion Artifact Using an Autoregressive Model for Research in Biomedical Signal Quality Analysis

Farago, Emma; Chan, Adrian D. C.

doi:10.1109/embc44109.2020.9175965

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…ECG segments were artificially contaminated using simulated motion artifacts generated using an autoregressive model (Farago and Chan, 2020). Unfortunately, motion artifact overlaps with the ECG in both time and frequency domain, making it challenging to remove.…”

Section: Contaminating Electrocardiogrammentioning

confidence: 99%

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

2022

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The objective of this paper is to develop an optimized system to detect Atrial Fibrillation (AF) in compressively sensed electrocardiogram (ECG) for long-term remote patient monitoring. A three-stage system was developed to 1) reject ECG of poor signal quality, 2) detect AF in compressively sensed ECG, and 3) detect AF in selectively reconstructed ECG. The Long-Term AF Database (LTAFDB), sampled at 128 Hz using a 12-bit ADC with a range of 20 mV, was used to validate the system. The LTAFDB had 83,315 normal and 82,435 AF rhythm 30 s ECG segments. Clean ECG from the LTAFDB was artificially contaminated with motion artifact to achieve −12 to 12 dB Signal-to-Noise Ratio (SNR) in steps of 3 dB. The contaminated ECG was compressively sensed at 50% and 75% compression ratio (CR). The system was evaluated using average precision (AP), the area under the curve (AUC) of the receiver operator characteristic curve, and the F1 score. The system was optimized to maximize the AP and minimize ECG rejection and reconstruction ratios. The optimized system for 50% CR had 0.72 AP, 0.63 AUC, and 0.58 F1 score, 0.38 rejection ratio, and 0.38 reconstruction ratio. The optimized system for 75% CR had 0.72 AP, 0.63 AUC, and 0.59 F1 score, 0.40 rejection ratio, and 0.35 reconstruction ratio. Challenges for long-term AF monitoring are the short battery life of monitors and the high false alarm rate due to artifacts. The proposed system improves the short battery life through compressive sensing while reducing false alarms (high AP) and ECG reconstruction (low reconstruction ratio).

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Section: Contaminating Electrocardiogrammentioning

confidence: 99%

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

2022

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“…Other contaminants, such as baseline wander, power line interference, and high-frequency noise, can be filtered out [29]. Simulated motion artifact generated using an autoregressive model was used [149]. Each ECG segment was contaminated with motion artifact such that the preset SNR varied from -12 dB to 12 dB, in steps of 3 dB.…”

Section: Contaminating Ecg With Motion Artifactmentioning

confidence: 99%

“…The LTAFDB and the preprocessing procedure described in Section 3.1.1 were used in this chapter. ECG segments were artificially contaminated using simulated motion artifact generated using an autoregressive model [149]. Motion artifact overlaps with the ECG in both time and frequency domain, making it challenging to remove.…”

Section: Databasementioning

confidence: 99%

Analysis of Compressively Sensed Electrocardiogram for Detection of Atrial Fibrillation

Abdelazez¹

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Cardiovascular diseases (CVDs) are one of the leading causes of death in the world. Longterm ambulatory electrocardiogram (ECG) monitoring enables timely medical interventions. Effective and efficient ambulatory ECG monitoring can be supported by compressive sensing, which can reduce battery consumption; however, the reconstruction of compressively sensed ECG could be a computationally intensive operation. Therefore, detection of CVDs in compressively sensed ECG is highly desirable. This thesis proposes a three-stage system based on machine learning for detecting atrial fibrillation (AF) in compressively sensed ECG while reducing false alarms (i.e., false positives) due to contamination and reducing reconstruction of the ECG. Stage 1 uses a novel signal quality index (SQI) to represent the quality of the compressively sensed ECG on a continuous scale and reject low-quality ECG. Stage 2 classifies the ECG as either AF or normal and generates an associated confidence score. Stage 3 selectively reconstructs the ECG when the Stage 2 confidence score is unacceptable and classifies the reconstructed ECG as either AF or normal. Clean ECG from Long-Term AF Database was artificially corrupted withsimulated motion artifact to pre-set signal-to-noise ratios. The corrupted ECG was compressively sensed at 50% and 75% compression ratios (CRs). The system was evaluated using average precision (AP), the area under the curve (AUC) of the receiver operator characteristic curve, false-positive rate (FPR), and true positive rate (TPR). The system was optimized to maximize the AP and minimize ECG rejection and reconstruction ratios. The optimized system for 50% CR had 0.72 AP, 0.63 AUC, 0.38 rejection ratio, and 0.38 reconstruction ratio. The optimized system for 75% CR had 0.72 AP, 0.63 AUC, 0.40 rejection ratio, and 0.35 reconstruction ratio. The optimized system reduced FPR by 0.37 iii for both CRs compared to Stage 2 alone. At a fixed FPR of 0.10, the TPR of the proposed system was 0.43 and 0.42 for 50% and 75% CRs, respectively, an improvement of 0.10 and 0.14 over Stage 2. The proposed system reduced the probability of false alarms (FPR) and detected AF in compressively sensed ECG (improved TPR) while being computationally efficient (low reconstruction ratio).

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Automated Biosignal Quality Analysis of Electrocardiograms

Abdelazez¹,

Rajan²,

Chan³

2021

IEEE Instrum. Meas. Mag.

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Simulating Motion Artifact Using an Autoregressive Model for Research in Biomedical Signal Quality Analysis

Cited by 4 publications

References 21 publications

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram for Remote Monitoring

Analysis of Compressively Sensed Electrocardiogram for Detection of Atrial Fibrillation

Automated Biosignal Quality Analysis of Electrocardiograms

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