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Rahman S, Pal S, Yearwood J, Karmakar C. Robustness of Deep Learning models in electrocardiogram noise detection and classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 253:108249. [PMID: 38815528 DOI: 10.1016/j.cmpb.2024.108249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
BACKGROUND AND OBJECTIVE Automatic electrocardiogram (ECG) signal analysis for heart disease detection has gained significant attention due to busy lifestyles. However, ECG signals are susceptible to noise, which adversely affects the performance of ECG signal analysers. Traditional blind filtering methods use predefined noise frequency and filter order, but they alter ECG biomarkers. Several Deep Learning-based ECG noise detection and classification methods exist, but no study compares recurrent neural network (RNN) and convolutional neural network (CNN) architectures and their complexity. METHODS This paper introduces a knowledge-based ECG filtering system using Deep Learning to classify ECG noise types and compare popular computer vision model architectures in a practical Internet of Medical Things (IoMT) framework. Experimental results demonstrate that the CNN-based ECG noise classifier outperforms the RNN-based model in terms of performance and training time. RESULTS The study shows that AlexNet, visual geometry group (VGG), and residual network (ResNet) achieved over 70% accuracy, specificity, sensitivity, and F1 score across six datasets. VGG and ResNet performances were comparable, but VGG was more complex than ResNet, with only a 4.57% less F1 score. CONCLUSIONS This paper introduces a Deep Learning (DL) based ECG noise classifier for a knowledge-driven ECG filtering system, offering selective filtering to reduce signal distortion. Evaluation of various CNN and RNN-based models reveals VGG and Resnet outperform. Further, the VGG model is superior in terms of performance. But Resnet performs comparably to VGG with less model complexity.
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Affiliation(s)
- Saifur Rahman
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Shantanu Pal
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - John Yearwood
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
| | - Chandan Karmakar
- School of Information Technology, Deakin University, Melbourne, Victoria, Australia.
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2
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Matthews J, Soltis I, Villegas‐Downs M, Peters TA, Fink AM, Kim J, Zhou L, Romero L, McFarlin BL, Yeo W. Cloud-Integrated Smart Nanomembrane Wearables for Remote Wireless Continuous Health Monitoring of Postpartum Women. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307609. [PMID: 38279514 PMCID: PMC10987106 DOI: 10.1002/advs.202307609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/15/2023] [Indexed: 01/28/2024]
Abstract
Noncommunicable diseases (NCD), such as obesity, diabetes, and cardiovascular disease, are defining healthcare challenges of the 21st century. Medical infrastructure, which for decades sought to reduce the incidence and severity of communicable diseases, has proven insufficient in meeting the intensive, long-term monitoring needs of many NCD disease patient groups. In addition, existing portable devices with rigid electronics are still limited in clinical use due to unreliable data, limited functionality, and lack of continuous measurement ability. Here, a wearable system for at-home cardiovascular monitoring of postpartum women-a group with urgently unmet NCD needs in the United States-using a cloud-integrated soft sternal device with conformal nanomembrane sensors is introduced. A supporting mobile application provides device data to a custom cloud architecture for real-time waveform analytics, including medical device-grade blood pressure prediction via deep learning, and shares the results with both patient and clinician to complete a robust and highly scalable remote monitoring ecosystem. Validated in a month-long clinical study with 20 postpartum Black women, the system demonstrates its ability to remotely monitor existing disease progression, stratify patient risk, and augment clinical decision-making by informing interventions for groups whose healthcare needs otherwise remain unmet in standard clinical practice.
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Affiliation(s)
- Jared Matthews
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
| | - Ira Soltis
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
| | - Michelle Villegas‐Downs
- Department of Human Development Nursing ScienceCollege of NursingUniversity of Illinois Chicago845 S. Damen Ave., MC 802ChicagoIL60612USA
| | - Tara A. Peters
- Department of Human Development Nursing ScienceCollege of NursingUniversity of Illinois Chicago845 S. Damen Ave., MC 802ChicagoIL60612USA
| | - Anne M. Fink
- Department of Biobehavioral Nursing ScienceCollege of NursingUniversity of Illinois Chicago845 S. Damen Ave., MC 802ChicagoIL60612USA
| | - Jihoon Kim
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
| | - Lauren Zhou
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
| | - Lissette Romero
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
| | - Barbara L. McFarlin
- Department of Human Development Nursing ScienceCollege of NursingUniversity of Illinois Chicago845 S. Damen Ave., MC 802ChicagoIL60612USA
| | - Woon‐Hong Yeo
- IEN Center for Wearable Intelligent Systems and Healthcare at the Institute for Electronics and NanotechnologyGeorgia Institute of TechnologyAtlantaGA30332USA
- George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGA30332USA
- Wallace H. Coulter Department of Biomedical EngineeringGeorgia Tech and Emory University School of MedicineAtlantaGA30332USA
- Parker H. Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsInstitute for Robotics and Intelligent MachinesGeorgia Institute of TechnologyAtlantaGA30332USA
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3
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Idrobo-Ávila E, Bognár G, Krefting D, Penzel T, Kovács P, Spicher N. Quantifying the Suitability of Biosignals Acquired During Surgery for Multimodal Analysis. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024; 5:250-260. [PMID: 38766543 PMCID: PMC11100950 DOI: 10.1109/ojemb.2024.3379733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 01/22/2024] [Accepted: 03/12/2024] [Indexed: 05/22/2024] Open
Abstract
Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis - which involves their joint analysis - can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled "signal quality indicators" to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.
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Affiliation(s)
- Ennio Idrobo-Ávila
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Gergő Bognár
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Dagmar Krefting
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
| | - Thomas Penzel
- Interdisciplinary Center of Sleep MedicineCharité - Universitätsmedizin Berlin10117BerlinGermany
| | - Péter Kovács
- Department of Numerical Analysis, Faculty of InformaticsEötvös Loránd University1117BudapestHungary
| | - Nicolai Spicher
- Department of Medical InformaticsUniversity Medical Center Göttingen, Georg-August-Universität37075GöttingenGermany
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Huang X, Zhang F, Fan H, Chang H, Zhou B, Li Z. Pseudo anomalies enhanced deep support vector data description for electrocardiogram quality assessment. Comput Biol Med 2024; 170:107928. [PMID: 38228029 DOI: 10.1016/j.compbiomed.2024.107928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 12/08/2023] [Accepted: 01/01/2024] [Indexed: 01/18/2024]
Abstract
Electrocardiogram (ECG) recordings obtained from wearable devices are susceptible to noise interference that degrades the signal quality. Traditional methods for assessing the quality of electrocardiogram signals (SQA) are mostly supervised and typically rely on limited types of noise in the training data, which imposes limitations in detecting unknown anomalies. The high variability of both ECG signals and noise presents a greater challenge to the generalization of traditional methods. In this paper, we propose a simple and effective unsupervised SQA method by modeling the SQA of ECG as a problem of anomaly detection, in which, a model of pseudo anomalies enhanced deep support vector data description is introduced to learn a more discriminative and generalized hypersphere of the high-quality ECG in a self-supervised manner. Specifically, we propose a series of ECG noise-generation methods to simulate the noise of real scenarios and use the generated noise samples as the pseudo anomalies to correct the hypersphere learned solely by the high-quality ECG samples. Finally, the quality of ECG can be measured based on the distance to the center of the hypersphere. Extensive experimental results on multiple public datasets and our constructed real-world 12-lead dataset demonstrate the effectiveness of the proposed method.
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Affiliation(s)
- Xunhua Huang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Fengbin Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Haoyi Fan
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China.
| | - Huihui Chang
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China.
| | - Bing Zhou
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, 450001, China.
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China.
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5
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Ben-David K, Wittels HL, Wishon MJ, Lee SJ, McDonald SM, Howard Wittels S. Tracking Cancer: Exploring Heart Rate Variability Patterns by Cancer Location and Progression. Cancers (Basel) 2024; 16:962. [PMID: 38473322 DOI: 10.3390/cancers16050962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 02/18/2024] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
Reduced heart rate variability (HRV) is an autonomic nervous system (ANS) response that may indicate dysfunction in the human body. Consistent evidence shows cancer patients elicit lower HRV; however, only select cancer locations were previously evaluated. Thus, the aim of the current study was to explore HRV patterns in patients diagnosed with and in varying stages of the most prevalent cancers. At a single tertiary academic medical center, 798 patients were recruited. HRV was measured via an armband monitor (Warfighter MonitorTM, Tiger Tech Solutions, Inc., Miami, FL, USA) equipped with electrocardiographic capabilities and was recorded for 5 to 7 min with patients seated in an upright position. Three time-domain metrics were calculated: SDNN (standard deviation of the NN interval), rMSSD (the root mean square of successive differences of NN intervals), and the percentage of time in which the change in successive NN intervals exceeds 50ms within a measurement (pNN50). Of the 798 patients, 399 were diagnosed with cancer. Cancer diagnoses were obtained via medical records one week following the measurement. Analysis of variance models were performed comparing the HRV patterns between different cancers, cancer stages (I-IV), and demographic strata. A total of 85% of the cancer patients had breast, gastrointestinal, genitourinary, or respiratory cancer. The cancer patients were compared to a control non-cancer patient population with similar patient size and distributions for sex, age, body mass index, and co-morbidities. For all HRV metrics, non-cancer patients exhibited significantly higher rMSSDs (11.1 to 13.9 ms, p < 0.0001), SDNNs (22.8 to 27.7 ms, p < 0.0001), and pNN50s (6.2 to 8.1%, p < 0.0001) compared to stage I or II cancer patients. This significant trend was consistently observed across each cancer location. Similarly, compared to patients with stage III or IV cancer, non-cancer patients possessed lower HRs (-11.8 to -14.0 bpm, p < 0.0001) and higher rMSSDs (+31.7 to +32.8 ms, p < 0.0001), SDNNs (+45.2 to +45.8 ms), p < 0.0001, and pNN50s (19.2 to 21.6%, p < 0.0001). The HR and HRV patterns observed did not significantly differ between cancer locations (p = 0.96 to 1.00). The depressed HRVs observed uniformly across the most prevalent cancer locations and stages appeared to occur independent of patients' co-morbidities. This finding highlights the potentially effective use of HRV as a non-invasive tool for determining common cancer locations and their respective stages. More studies are needed to delineate the HRV patterns across different ages, between sexes and race/ethnic groups.
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Affiliation(s)
- Kfir Ben-David
- Department of Surgery, Division of Oncology, Mount Sinai Medical Center, Miami Beach, FL 33140, USA
- Department of Surgery, Wertheim School of Medicine, Florida International University, Miami, FL 33199, USA
| | - Harrison L Wittels
- Tiger Tech Solutions, Inc., Miami, FL 33156, USA
- Science, Technology and Research, Inc., Miami, FL 33156, USA
| | | | - Stephen J Lee
- United States Army Research Laboratory, United States Army Combat Capabilities Development Command, Adelphi, MD 20783, USA
| | - Samantha M McDonald
- Tiger Tech Solutions, Inc., Miami, FL 33156, USA
- School of Kinesiology and Recreation, Illinois State University, Normal, IL 61761, USA
| | - S Howard Wittels
- Tiger Tech Solutions, Inc., Miami, FL 33156, USA
- Science, Technology and Research, Inc., Miami, FL 33156, USA
- Department of Anesthesiology, Mount Sinai Medical Center, Miami, FL 33140, USA
- Department of Anesthesiology, Wertheim School of Medicine, Florida International University, Miami, FL 33199, USA
- Miami Beach Anesthesiology Associates, Miami, FL 33140, USA
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6
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Zhong W, Luo J, Du W. Deep learning with fetal ECG recognition. Physiol Meas 2023; 44:115006. [PMID: 37939396 DOI: 10.1088/1361-6579/ad0ab7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/07/2023] [Indexed: 11/10/2023]
Abstract
Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.
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Affiliation(s)
- Wei Zhong
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Jiahui Luo
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
| | - Wei Du
- Guangdong Police College, Guangzhou, 510000, People's Republic of China
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7
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Shin S, Choi S, Kim C, Mousavi AS, Hahn JO, Jeong S, Jeong H. BCG Signal Quality Assessment Based on Time-Series Imaging Methods. SENSORS (BASEL, SWITZERLAND) 2023; 23:9382. [PMID: 38067755 PMCID: PMC10708708 DOI: 10.3390/s23239382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/01/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023]
Abstract
This paper describes a signal quality classification method for arm ballistocardiogram (BCG), which has the potential for non-invasive and continuous blood pressure measurement. An advantage of the BCG signal for wearable devices is that it can easily be measured using accelerometers. However, the BCG signal is also susceptible to noise caused by motion artifacts. This distortion leads to errors in blood pressure estimation, thereby lowering the performance of blood pressure measurement based on BCG. In this study, to prevent such performance degradation, a binary classification model was created to distinguish between high-quality versus low-quality BCG signals. To estimate the most accurate model, four time-series imaging methods (recurrence plot, the Gramain angular summation field, the Gramain angular difference field, and the Markov transition field) were studied to convert the temporal BCG signal associated with each heartbeat into a 448 × 448 pixel image, and the image was classified using CNN models such as ResNet, SqueezeNet, DenseNet, and LeNet. A total of 9626 BCG beats were used for training, validation, and testing. The experimental results showed that the ResNet and SqueezeNet models with the Gramain angular difference field method achieved a binary classification accuracy of up to 87.5%.
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Affiliation(s)
- Sungtae Shin
- Department of Mechanical Engineering, Dong-A University, Busan 49315, Republic of Korea; (S.S.); (S.C.)
| | - Soonyoung Choi
- Department of Mechanical Engineering, Dong-A University, Busan 49315, Republic of Korea; (S.S.); (S.C.)
| | - Chaeyoung Kim
- Institute for Digital Antiaging and Healthcare, Inje University, Gimhae 50834, Republic of Korea;
| | - Azin Sadat Mousavi
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.S.M.); (J.-O.H.)
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.S.M.); (J.-O.H.)
| | - Sehoon Jeong
- Institute for Digital Antiaging and Healthcare, Inje University, Gimhae 50834, Republic of Korea;
- Department of Healthcare Information Technology, Inje University, Gimhae 50834, Republic of Korea
- Paik Institute for Clinical Research, Inje University, Busan 50834, Republic of Korea
| | - Hyundoo Jeong
- Department of Mechatronics Engineering, Incheon National University, Incheon 22012, Republic of Korea
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Rahman S, Habib A, Karmakar C, Yearwood J. Impact of synthetic noise signature and physiologic ECG signal on designing ML-based ECG noise detection framework. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083183 DOI: 10.1109/embc40787.2023.10341145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.
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Dang TH, Jang GY, Lee K, Oh TI. Motion Artifacts Reduction for Noninvasive Hemodynamic Monitoring of Conscious Patients Using Electrical Impedance Tomography: A Preliminary Study. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115308. [PMID: 37300035 DOI: 10.3390/s23115308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/19/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs occurred. A total of 36 measurements with 113 one-hour sub-datasets were collected from 14 patients. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 L per minute (LPM), respectively, compared to 4.05 and 3.82 LPM for the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability by at least two times, particularly in high-motion environments.
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Affiliation(s)
- Thi Hang Dang
- Department of Medical Engineering, Graduate School, Kyung Hee University, Seoul 02453, Republic of Korea
| | - Geuk Young Jang
- Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam-si 13620, Republic of Korea
| | - Kyounghun Lee
- Medical Science Research Institute, Kyung Hee University Medical Center, Seoul 02447, Republic of Korea
| | - Tong In Oh
- Department of Medical Engineering, Graduate School, Kyung Hee University, Seoul 02453, Republic of Korea
- Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Seoul 02453, Republic of Korea
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10
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Chehbani A, Sahuguede S, Julien-Vergonjanne A, Bernard O. Quality Indexes of the ECG Signal Transmitted Using Optical Wireless Link. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094522. [PMID: 37177726 PMCID: PMC10181618 DOI: 10.3390/s23094522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 05/01/2023] [Accepted: 05/03/2023] [Indexed: 05/15/2023]
Abstract
This work relates to the quality of the electrocardiogram (ECG) signal of an elderly person, transmitted using optical wireless links. The studied system uses infrared signals between an optical transmitter located on the person's wrist and optical receivers placed on the ceiling. As the elderly person moves inside a room, the optical channel is time-varying, affecting the received ECG signal. To assess the ECG quality, we use specific signal quality indexes (SQIs), allowing the evaluation of the spectral and statistical characteristics of the signal. Our main contribution is studying how the SQIs behave according to the optical transmission performance and the studied context in order to determine the conditions required to obtain excellent quality indexes. The approach is based on the simulation of the whole chain, from the raw ECG to the extraction process after transmission until the evaluation of SQIs. This technique was developed considering optical channel modeling, including the mobility of the elderly. The obtained results show the potential of optical wireless communication technologies for reliable ECG monitoring in such a context. It has been observed that excellent ECG quality can be obtained with a minimum SNR of 11 dB for on-off keying modulation.
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Affiliation(s)
- Amel Chehbani
- XLIM Laboratory, UMR CNRS 7252, University of Limoges, 87000 Limoges, France
| | - Stephanie Sahuguede
- XLIM Laboratory, UMR CNRS 7252, University of Limoges, 87000 Limoges, France
| | | | - Olivier Bernard
- MOVE Laboratory, University of Poitiers, 86000 Poitiers, France
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11
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Capisizu AS, Cuzino D, Stanciu SM. A Pilot Study on the Role of Computed Tomography in the Management of Patients with Coronary Artery Anomalies in Romania. J Cardiovasc Dev Dis 2023; 10:jcdd10040170. [PMID: 37103049 PMCID: PMC10142656 DOI: 10.3390/jcdd10040170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/09/2023] [Accepted: 04/13/2023] [Indexed: 04/28/2023] Open
Abstract
Coronary artery anomalies may occur during embryogenesis and can lead to changes in the vascularization of the heart, possible ischemia, and an increased risk of sudden death. A retrospective study was conducted with the aim of assessing the prevalence of coronary anomalies in a Romanian sample of patients, investigated with computed tomography angiography for coronary artery disease. The objectives of the study were to identify the anomalies of the coronary arteries and to conduct an anatomical classification according to Angelini. The study also consisted of evaluations regarding coronary artery calcification in the sample of patients by the Agatston calcium score and assessments regarding the presence of cardiac symptoms and their association with coronary abnormalities. The results showed a prevalence of coronary anomalies of 8.7%, of which 3.8% were origin and course anomalies and 4.9% were coronary anomalies with intramuscular bridging of the left anterior descending artery. Recommendations for practice include the widespread use of coronary computed tomography angiography for the diagnosis of coronary artery anomalies and coronary artery disease in larger patient groups and encouraging this investigation across the country.
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Affiliation(s)
- Adriana Sorina Capisizu
- Faculty of General Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Bvd, 050474 Bucharest, Romania
| | - Dragos Cuzino
- Faculty of General Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Bvd, 050474 Bucharest, Romania
- Clinical Radiology-Medical Imaging Center, Dr. Carol Davila Central Military Emergency University Hospital, 134 Calea Plevnei Str., 010825 Bucharest, Romania
| | - Silviu Marcel Stanciu
- Faculty of General Medicine, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Bvd, 050474 Bucharest, Romania
- Center for Cardiovascular Diseases, Laboratory of Noninvasive Cardiovascular Functional Explorations, Dr. Carol Davila Central Military Emergency University Hospital, 134 Calea Plevnei Str., 010825 Bucharest, Romania
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12
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Zhong W, Mao L, Du W. A signal quality assessment method for fetal QRS complexes detection. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7943-7956. [PMID: 37161180 DOI: 10.3934/mbe.2023344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Non-invasive fetal ECG (NI-FECG) provides a non-invasive method to monitor the health of the fetus. However, the NI-FECG is easily interfered by noise, which makes the signal quality decline, leading to the fetal heart rate (FHR) monitoring becoming a challenging task. METHODS In this work, an algorithm for dynamic evaluation of signal quality is proposed to improve the multi-channel FHR monitoring. The innovation of the method is to assess the signal quality in the process of multi-channel fetal QRS (FQRS) complexes detection. Specifically, the detected FQRS is used as quality unit. Each quality unit can be a true R peak (TR) or a false R peak (FR). It is the basic quality information in this work. The signal quality of each channel is estimated by estimating the correctness of the detection results. Further, the TRs of all channels can be fused to obtain more reliable fetal heart rate monitoring. MAIN RESULTS Analysis results demonstrate that the proposed algorithm is capable of selecting the good quality signal for FQRS detection achieving 97.40% PPV, 98.33% SE and 97.86% F1. SIGNIFICANCE This work sheds light on the quality assessment of fetal monitoring signal.
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Affiliation(s)
- Wei Zhong
- Guangdong Police College, Guangzhou 510000, China
| | - Li Mao
- Guangdong Police College, Guangzhou 510000, China
| | - Wei Du
- Guangdong Police College, Guangzhou 510000, China
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Campero Jurado I, Lorato I, Morales J, Fruytier L, Stuart S, Panditha P, Janssen DM, Rossetti N, Uzunbajakava N, Serban IB, Rikken L, de Kok M, Vanschoren J, Brombacher A. Signal Quality Analysis for Long-Term ECG Monitoring Using a Health Patch in Cardiac Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:2130. [PMID: 36850728 PMCID: PMC9965306 DOI: 10.3390/s23042130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 02/07/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios.
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Affiliation(s)
- Israel Campero Jurado
- Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Ilde Lorato
- Stichting IMEC Nederland, 5656 AE Eindhoven, The Netherlands
| | - John Morales
- Stichting IMEC Nederland, 5656 AE Eindhoven, The Netherlands
| | - Lonneke Fruytier
- Department of Cardiology, Máxima Medical Center, De Run 4600, 5504 DB Veldhoven, The Netherlands
| | - Shavini Stuart
- Holst Centre, TNO, Biomedical R&D, 5656 AE Eindhoven, The Netherlands
| | - Pradeep Panditha
- Holst Centre, TNO, Biomedical R&D, 5656 AE Eindhoven, The Netherlands
| | - Daan M. Janssen
- Department of Cardiology, Máxima Medical Center, De Run 4600, 5504 DB Veldhoven, The Netherlands
| | - Nicolò Rossetti
- Stichting IMEC Nederland, 5656 AE Eindhoven, The Netherlands
| | | | - Irina Bianca Serban
- Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Lars Rikken
- Holst Centre, TNO, Biomedical R&D, 5656 AE Eindhoven, The Netherlands
| | - Margreet de Kok
- Holst Centre, TNO, Biomedical R&D, 5656 AE Eindhoven, The Netherlands
| | - Joaquin Vanschoren
- Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Aarnout Brombacher
- Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
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Castiglioni P, Parati G, Faini A. Cepstral Analysis for Scoring the Quality of Electrocardiograms for Heart Rate Variability. Front Physiol 2022; 13:921210. [PMID: 35784895 PMCID: PMC9247307 DOI: 10.3389/fphys.2022.921210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Mobile-health solutions based on heart rate variability often require electrocardiogram (ECG) recordings by inexperienced operators or real-time automatic analyses of long-term recordings by wearable devices in free-moving individuals. In this context, it is useful to associate a quality index with the ECG, scoring the adequacy of the recording for heart rate variability to identify noise or arrhythmias. Therefore, this work aims to propose and validate a computational method for assessing the adequacy of single-lead ECGs for heart rate variability analysis that may run in real time on wearable systems with low computational power. The method quantifies the ECG pseudo-periodic structure employing cepstral analysis. The cepstrum (spectrum of log-spectrum) is estimated on a running ECG window of 10 s before and after “liftering” (filtering in the cepstral domain) to remove slower noise components. The ECG periodicity generates a dominant peak in the liftered cepstrum at the “quefrency” of the mean cardiac interval. The Cepstral Quality Index (CQI) is the ratio between the cepstral-peak power and the total power of the unliftered cepstrum. Noises and arrhythmias reduce the relative power of the cepstral peak decreasing CQI. We analyzed a public dataset of 6072 single-lead ECGs manually classified in normal rhythm or inadequate for heart rate variability analysis because of noise or atrial fibrillation, and the CQI = 47% cut-off identified the inadequate recordings with 79% sensitivity and 85% specificity. We showed that the performance is independent of the lead considering a public dataset of 1,000 12-lead recordings with quality classified as “acceptable” or “unacceptable” by visual inspection. Thus, the cepstrum describes the ECG periodic structure effectively and concisely and CQI appears to be a robust score of the adequacy of ECG recording for heart rate variability analysis, evaluable in real-time on wearable devices.
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Affiliation(s)
- Paolo Castiglioni
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
- *Correspondence: Paolo Castiglioni,
| | - Gianfranco Parati
- IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy
- Department of Medicine and Surgery, University of Milano-Bicocca, Milan, Ital
| | - Andrea Faini
- IRCCS Istituto Auxologico Italiano, San Luca Hospital, Milan, Italy
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