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Article ; Online: Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements.

Farago, Emma / Chan, Adrian D C

2023 Volume 23, Issue 10

Abstract: High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and ... ...

Abstract	High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG.
MeSH term(s)	Artifacts ; Electricity ; Electromyography ; Muscle Contraction ; Signal-To-Noise Ratio
Language	English
Publishing date	2023-05-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23104759
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Article: Multiresolution semantic segmentation of biological structures in digital histopathology.

Salsabili, Sina / Chan, Adrian D C / Ukwatta, Eranga

Journal of medical imaging (Bellingham, Wash.)

2024 Volume 11, Issue 3, Page(s) 37501

Abstract: Purpose: Semantic segmentation in high-resolution, histopathology whole slide images (WSIs) is an important fundamental task in various pathology applications. Convolutional neural networks (CNN) are the state-of-the-art approach for image segmentation. ...

Abstract	Purpose: Semantic segmentation in high-resolution, histopathology whole slide images (WSIs) is an important fundamental task in various pathology applications. Convolutional neural networks (CNN) are the state-of-the-art approach for image segmentation. A patch-based CNN approach is often employed because of the large size of WSIs; however, segmentation performance is sensitive to the field-of-view and resolution of the input patches, and balancing the trade-offs is challenging when there are drastic size variations in the segmented structures. We propose a multiresolution semantic segmentation approach, which is capable of addressing the threefold trade-off between field-of-view, computational efficiency, and spatial resolution in histopathology WSIs. Approach: We propose a two-stage multiresolution approach for semantic segmentation of histopathology WSIs of mouse lung tissue and human placenta. In the first stage, we use four different CNNs to extract the contextual information from input patches at four different resolutions. In the second stage, we use another CNN to aggregate the extracted information in the first stage and generate the final segmentation masks. Results: The proposed method reported 95.6%, 92.5%, and 97.1% in our single-class placenta dataset and 97.1%, 87.3%, and 83.3% in our multiclass lung dataset for pixel-wise accuracy, mean Dice similarity coefficient, and mean positive predictive value, respectively. Conclusions: The proposed multiresolution approach demonstrated high accuracy and consistency in the semantic segmentation of biological structures of different sizes in our single-class placenta and multiclass lung histopathology WSI datasets. Our study can potentially be used in automated analysis of biological structures, facilitating the clinical research in histopathology applications.
Language	English
Publishing date	2024-05-09
Publishing country	United States
Document type	Journal Article
ISSN	2329-4302
ISSN	2329-4302
DOI	10.1117/1.JMI.11.3.037501
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Article ; Online: Investigating concurrent validity of inertial sensors to evaluate multiplanar spine movement.

Beange, Kristen H E / Chan, Adrian D C / Graham, Ryan B

Journal of biomechanics

2024 Volume 164, Page(s) 111939

Abstract: Inertial measurement units (IMUs) offer a portable and inexpensive alternative to traditional optical motion capture systems, and have potential to support clinical diagnosis and treatment of low back pain; however, due to a lack of confidence regarding ... ...

Abstract	Inertial measurement units (IMUs) offer a portable and inexpensive alternative to traditional optical motion capture systems, and have potential to support clinical diagnosis and treatment of low back pain; however, due to a lack of confidence regarding the validity of IMU-derived metrics, their uptake and acceptance remain a challenge. The objective of this work was to assess the concurrent validity of the Xsens DOT IMUs for tracking multiplanar spine movement, and to evaluate concurrent validity and reliability for estimating clinically relevant metrics relative to gold-standard optical motion capture equipment. Ten healthy controls performed spine range of motion (ROM) tasks, while data were simultaneously tracked from IMUs and optical marker clusters placed over the C7, T12, and S1 vertebrae. Root mean square error (RMSE), mean absolute error (MAE), and intraclass correlation coefficients (ICC
MeSH term(s)	Humans ; Reproducibility of Results ; Movement ; Sacrum ; Low Back Pain ; Rotation ; Range of Motion, Articular ; Biomechanical Phenomena
Language	English
Publishing date	2024-01-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	218076-5
ISSN	1873-2380 ; 0021-9290
ISSN (online)	1873-2380
ISSN	0021-9290
DOI	10.1016/j.jbiomech.2024.111939
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Article ; Online: A Review of Techniques for Surface Electromyography Signal Quality Analysis.

Farago, Emma / MacIsaac, Dawn / Suk, Michelle / Chan, Adrian D C

IEEE reviews in biomedical engineering

2023 Volume 16, Page(s) 472–486

Abstract: Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade ... ...

Abstract	Electromyography (EMG) signals are instrumental in a variety of applications including prosthetic control, muscle health assessment, rehabilitation, and workplace monitoring. Signal contaminants including noise, interference, and artifacts can degrade the quality of the EMG signal, leading to misinterpretation; therefore it is important to ensure that collected EMG signals are of sufficient quality prior to further analysis. A literature search was conducted to identify current approaches for detecting, identifying, and quantifying contaminants within surface EMG signals. We identified two main strategies: 1) bottom-up approaches for identifying specific and well-characterized contaminants and 2) top-down approaches for detecting anomalous EMG signals or outlier channels in high-density EMG arrays. The best type(s) of approach are dependent on the circumstances of data collection including the environment, the susceptibility of the application to contaminants, and the resilience of the application to contaminants. Further research is needed for assessing EMG with multiple simultaneous contaminants, identifying ground-truths for clean EMG data, and developing user-friendly and autonomous methods for EMG signal quality analysis.
MeSH term(s)	Humans ; Electromyography/methods ; Algorithms ; Muscle Contraction/physiology ; Signal Processing, Computer-Assisted ; Artifacts ; Muscle, Skeletal
Language	English
Publishing date	2023-01-05
Publishing country	United States
Document type	Review ; Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1941-1189
ISSN (online)	1941-1189
DOI	10.1109/RBME.2022.3164797
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Article ; Online: Signal Quality Assessment of Compressively Sensed Electrocardiogram.

Abdelazez, Mohamed / Rajan, Sreeraman / Chan, Adrian D C

IEEE transactions on bio-medical engineering

2022 Volume 69, Issue 11, Page(s) 3397–3406

Abstract: Objective: Develop a signal quality index (SQI) to determine the quality of compressively sensed electrocardiogram (ECG) by estimating the signal-to-noise ratio (SNR).: Methods: The SQI used random forests, with the ratio of the standard deviations ... ...

Abstract	Objective: Develop a signal quality index (SQI) to determine the quality of compressively sensed electrocardiogram (ECG) by estimating the signal-to-noise ratio (SNR). Methods: The SQI used random forests, with the ratio of the standard deviations of an ECG segment and a clean ECG and the Wasserstein metric between the amplitude distributions of an ECG segment and a clean ECG, as features. The SQI was tested using the Long-Term Atrial Fibrillation Database (LTAFDB) and the PhysioNet/CinC Challenge 2011 Database Set A (CinCDB). Clean ECG segments from the LTAFDB were corrupted using simulated motion artifact, with preset SNR between -12 dB and 12 dB. The CinCDB was used as-it-is. The databases were compressively sensed using three types of sensing matrices at three compression ratios (50%, 75%, and 95%). For LTAFDB, the RMSE and Spearman correlation between the SQI and the preset SNR were used for evaluation, while for CinCDB, accuracy and F1 score were used. Results: The average RMSE was 3.18 dB and 3.47 dB in normal and abnormal ECG. The average Spearman correlation was 0.94 and 0.93 in normal and abnormal ECG, respectively. The average accuracy and F1 score were 0.90 and 0.88, respectively. Conclusion: The SQI determined the quality of compressively sensed ECG and generalized across different databases. There was no consequential effect on the SQI due to abnormal ECG or compression using different sensing matrices and compression ratios. Significance: Without reconstruction, the SQI can inform which ECG should be analyzed to reduce false alarms due to contamination.
MeSH term(s)	Humans ; Signal Processing, Computer-Assisted ; Algorithms ; Electrocardiography ; Data Compression ; Signal-To-Noise Ratio ; Atrial Fibrillation/diagnosis
Language	English
Publishing date	2022-10-19
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2022.3170047
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Article ; Online: Simulating Motion Artifact Using an Autoregressive Model for Research in Biomedical Signal Quality Analysis.

Farago, Emma / Chan, Adrian D C

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 940–943

Abstract: Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with ... ...

Abstract	Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.
MeSH term(s)	Algorithms ; Artifacts ; Motion ; Signal Processing, Computer-Assisted ; Walking
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175965
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Article ; Online: A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus.

Chao-Chia Lu, David / Boulay, Chadwick / Chan, Adrian D C / Sachs, Adam J

Neuromodulation : journal of the International Neuromodulation Society

2023 Volume 27, Issue 3, Page(s) 409–421

Abstract: Objective: This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization.!## ...

Abstract	Objective: This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization. Materials and methods: The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions. Results: The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed. Conclusion: This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.
MeSH term(s)	Humans ; Subthalamic Nucleus/surgery ; Deep Brain Stimulation/methods ; Neurophysiology ; Parkinson Disease/surgery
Language	English
Publishing date	2023-07-18
Publishing country	United States
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	1500372-3
ISSN	1525-1403 ; 1094-7159
ISSN (online)	1525-1403
ISSN	1094-7159
DOI	10.1016/j.neurom.2023.02.081
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Article ; Online: Transfer Learning for Detection of Atrial Fibrillation in Deterministic Compressive Sensed ECG.

Abdelazez, Mohamed / Rajan, Sreeraman / Chan, Adrian D C

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 5398–5401

Abstract: Atrial Fibrillation (AF) is a cardiac condition resulting from uncoordinated contraction of the atria which may lead to an increase in the risk of heart attacks, strokes, and death. AF symptoms may go undetected and may require longterm monitoring of ... ...

Abstract	Atrial Fibrillation (AF) is a cardiac condition resulting from uncoordinated contraction of the atria which may lead to an increase in the risk of heart attacks, strokes, and death. AF symptoms may go undetected and may require longterm monitoring of electrocardiogram (ECG) to be detected. Long-term ECG monitoring can generate a large amount of data which can increase power, storage, and the wireless transmission bandwidth of monitoring devices. Compressive Sensing (CS) is compression technique at the sampling stage which may save power, storage, and wireless bandwidth of monitoring devices. The reconstruction of compressive sensed ECG is a computationally expensive operation; therefore, detection of AF in compressive sensed ECG is warranted. This paper presents preliminary results of using deep learning to detect AF in deterministic compressive sensed ECG. MobileNetV2 convolutional neural network (CNN) was used in this paper. Transfer learning was utilized to leverage a pre-trained CNN with the final two layers retrained using 24 records from the Long-Term Atrial Fibrillation Database. The Short-Term Fourier Transform was used to generate spectrograms that were fed to the CNN. The CNN was tested on the MIT-BIH Atrial Fibrillation Database at the uncompressed, 50%, 75%, and 95% compressed ECG. The performance of the CNN was evaluated using weighted average precision (AP) and area under the curve (AUC) of the receiver operator curve (ROC). The CNN had AP of 0.80, 0.70, 0.70, and 0.57 at uncompressed, 50%, 75%, and 95% compression levels. The AUC was 0.87, 0.78, 0.79, and 0.75 at each compression level. The preliminary results show promise for using deep learning to detect AF in compressive sensed ECG.Clinical Relevance-This paper confirms that AF can be detected in compressive sensed ECG using deep learning, This will facilitate long-term ECG monitoring using wearable devices and will reduce adverse complications resulting from undiagnosed AF.
MeSH term(s)	Atrial Fibrillation/diagnosis ; Data Compression ; Electrocardiography ; Humans ; Machine Learning ; Neural Networks, Computer
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175813
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Article ; Online: Automated detection of microscopic placental features indicative of maternal vascular malperfusion using machine learning.

Patnaik, Purvasha / Khodaee, Afsoon / Vasam, Goutham / Mukherjee, Anika / Salsabili, Sina / Ukwatta, Eranga / Grynspan, David / Chan, Adrian D C / Bainbridge, Shannon

Placenta

2023 Volume 145, Page(s) 19–26

Abstract: Introduction: Hypertensive disorders of pregnancy (HDP) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Currently, clinical placental ... ...

Abstract	Introduction: Hypertensive disorders of pregnancy (HDP) and fetal growth restriction (FGR) are common obstetrical complications, often with pathological features of maternal vascular malperfusion (MVM) in the placenta. Currently, clinical placental pathology methods involve a manual visual examination of histology sections, a practice that can be resource-intensive and demonstrates moderate-to-poor inter-pathologist agreement on diagnostic outcomes, dependant on the degree of pathologist sub-specialty training. Methods: This study aims to apply machine learning (ML) feature extraction methods to classify digital images of placental histopathology specimens, collected from cases of HDP [pregnancy induced hypertension (PIH), preeclampsia (PE), PE + FGR], normotensive FGR, and healthy pregnancies, according to the presence or absence of MVM lesions. 159 digital images were captured from histological placental specimens, manually scored for MVM lesions (MVM- or MVM+) and used to develop a support vector machine (SVM) classifier model, using features extracted from pre-trained ResNet18. The model was trained with data augmentation and shuffling, with the performance assessed for patch-level and image-level classification through measurements of accuracy, precision, and recall using confusion matrices. Results: The SVM model demonstrated accuracies of 70 % and 79 % for patch-level and image-level MVM classification, respectively, with poorest performance observed on images with borderline MVM presence, as determined through post hoc observation. Discussion: The results are promising for the integration of ML methods into the placental histopathological examination process. Using this study as a proof-of-concept will lead our group and others to carry ML models further in placental histopathology.
MeSH term(s)	Pregnancy ; Female ; Humans ; Placenta/pathology ; Pregnancy Outcome ; Retrospective Studies ; Pre-Eclampsia/pathology ; Hypertension, Pregnancy-Induced/pathology ; Fetal Growth Retardation/diagnostic imaging ; Fetal Growth Retardation/pathology
Language	English
Publishing date	2023-11-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	603951-0
ISSN	1532-3102 ; 0143-4004
ISSN (online)	1532-3102
ISSN	0143-4004
DOI	10.1016/j.placenta.2023.11.005
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Article ; Online: Compression of surface myoelectric signals using MP3 encoding.

Chan, Adrian D C

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2011 Volume 2011, Page(s) 5012–5015

Abstract: The potential of MP3 compression of surface myoelectric signals is explored in this paper. MP3 compression is a perceptual-based encoder scheme, used traditionally to compress audio signals. The ubiquity of MP3 compression (e.g., portable consumer ... ...

Abstract	The potential of MP3 compression of surface myoelectric signals is explored in this paper. MP3 compression is a perceptual-based encoder scheme, used traditionally to compress audio signals. The ubiquity of MP3 compression (e.g., portable consumer electronics and internet applications) makes it an attractive option for remote monitoring and telemedicine applications. The effects of muscle site and contraction type are examined at different MP3 encoding bitrates. Results demonstrate that MP3 compression is sensitive to the myoelectric signal bandwidth, with larger signal distortion associated with myoelectric signals that have higher bandwidths. Compared to other myoelectric signal compression techniques reported previously (embedded zero-tree wavelet compression and adaptive differential pulse code modulation), MP3 compression demonstrates superior performance (i.e., lower percent residual differences for the same compression ratios).
MeSH term(s)	Algorithms ; Data Compression/methods ; Electromyography/methods ; Humans ; Muscle Contraction/physiology ; Muscle, Skeletal/physiology ; Reproducibility of Results ; Sensitivity and Specificity
Language	English
Publishing date	2011-12-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/IEMBS.2011.6091242
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