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Non-invasive method for blood glucose monitoring using ECG signal. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2023. [DOI: 10.2478/pjmpe-2023-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Abstract
Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters.
Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal.
Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper.
Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.
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Martinek R, Ladrova M, Sidikova M, Jaros R, Behbehani K, Kahankova R, Kawala-Sterniuk A. Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals. SENSORS (BASEL, SWITZERLAND) 2021; 21:5186. [PMID: 34372424 PMCID: PMC8346990 DOI: 10.3390/s21155186] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 07/23/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022]
Abstract
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
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Affiliation(s)
- Radek Martinek
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Martina Ladrova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Michaela Sidikova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Rene Jaros
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Khosrow Behbehani
- College of Engineering, The University of Texas in Arlington, Arlington, TX 76019, USA;
| | - Radana Kahankova
- FEECS, Department of Cybernetics and Biomedical Engineering, VSB-Technical University Ostrava, 708 00 Ostrava, Czech Republic; (M.L.); (M.S.); (R.J.); (R.K.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland
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Wu S, Hannig J, Lee TCM. Uncertainty quantification for principal component regression. Electron J Stat 2021. [DOI: 10.1214/21-ejs1837] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Suofei Wu
- Department of Statistics, University of California, Davis, One Shields Avenue, Davis, CA 95616, U.S.A
| | - Jan Hannig
- Department of Statistics & Operations Research, 318 Hanes Hall, University of North Carolina at Chapel Hill, NC 27599, U.S.A
| | - Thomas C. M. Lee
- Department of Statistics, University of California, Davis, One Shields Avenue, Davis, CA 95616, U.S.A
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Porumb M, Stranges S, Pescapè A, Pecchia L. Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG. Sci Rep 2020; 10:170. [PMID: 31932608 PMCID: PMC6957484 DOI: 10.1038/s41598-019-56927-5] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 12/18/2019] [Indexed: 01/21/2023] Open
Abstract
Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.
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Affiliation(s)
- Mihaela Porumb
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK
| | - Saverio Stranges
- Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Family Medicine, Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
- Department of Population Health, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Antonio Pescapè
- Department of Electrical Engineering, University of Napoli "Federico II", Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, CV4 7AL, UK.
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Lipponen JA, Tarvainen MP. Principal component model for maternal ECG extraction in fetal QRS detection. Physiol Meas 2014; 35:1637-48. [PMID: 25069651 DOI: 10.1088/0967-3334/35/8/1637] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Fetal cardiac monitoring using noninvasive abdominal leads can provide important information on fetal well-being. However, extraction of fetal electrocardiogram (fECG) from abdominal signals is often problematic because of the higher amplitude maternal ECG (mECG). The aim of this study was to introduce a principal component model for removing the maternal ECG from abdominal signals. The proposed method removes mECG waveforms with high accuracy even though abdominal movements cause morphological deviation to mECG complexes. The method can be used for single or multi-lead measurements. The proposed method was tested using 175 1 min long abdominal measurements with fetal QRS annotation markers acquired from a fetal scalp electrode. Using the presented mECG removing algorithm and matched filtering based fQRS detector, 95% sensitivity for fQRS detection and 4.84 ms RMS error for fetal RR-interval estimation were acquired.
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Affiliation(s)
- Jukka A Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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The correlation of vectorcardiographic changes to blood lactate concentration during an exercise test. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.05.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Du X, Rao N, Ou F, Xu G, Yin L, Wang G. f-Wave Suppression Method for Improvement of Locating T-Wave Ends in Electrocardiograms during Atrial Fibrillation. Ann Noninvasive Electrocardiol 2013; 18:262-70. [DOI: 10.1111/anec.12034] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Xiaochuan Du
- School of Life Science and Technology; University of Electronic Science and Technology of China; Chengdu; China
| | - Nini Rao
- School of Life Science and Technology; University of Electronic Science and Technology of China; Chengdu; China
| | - Feng Ou
- School of Life Science and Technology; University of Electronic Science and Technology of China; Chengdu; China
| | - Guogong Xu
- School of Life Science and Technology; University of Electronic Science and Technology of China; Chengdu; China
| | - Lixue Yin
- Cardiovascular department, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu; China
| | - Gang Wang
- School of Electronic Engineering; University of Electronic Science and Technology of China; Chengdu; China
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Lipponen JA, Kemppainen J, Karjalainen PA, Laitinen T, Mikola H, Kärki T, Tarvainen MP. Hypoglycemia detection based on cardiac repolarization features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:4697-700. [PMID: 22255386 DOI: 10.1109/iembs.2011.6091163] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Hypoglycemia is known to affect repolarization characteristics of the heart. These changes are shown from ECG by prolonged QT-time and T-wave flattening. In this study we constructed a classifier based on these ECG parameters. By using the classifier we tried to detect hypoglycemic events from measurements of 22 test subjects. Hypoglycemic state was achieved using glucose clamp technique. Used test protocol consisted of three stages: normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period. Subjects were divided into three groups: 9 healthy controls (Healthy), 6 otherwise healthy type 1 diabetics (T1DM) and 7 type 1 diabetics with disease complications (T1DMc). Detection of hypoglycemic event could be made passably from 15/22 measurements. In addition, we found that detection process is easier for healthy and T1DM groups than T1DMc group diabetics because in T1DMc group subjects' have lower autonomic response to hypoglycemic events. Also we noticed that changes in ECG occurs few minutes after blood glucose is decreased below 3.5 mmol/1.
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Affiliation(s)
- J A Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
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Lipponen JA, Kemppainen J, Karjalainen PA, Laitinen T, Mikola H, Kärki T, Tarvainen MP. Dynamic estimation of cardiac repolarization characteristics during hypoglycemia in healthy and diabetic subjects. Physiol Meas 2011; 32:649-60. [PMID: 21508439 DOI: 10.1088/0967-3334/32/6/003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Hypoglycemia is known to affect the repolarization characteristics of the heart, but the mechanisms behind these changes are not completely understood. We analyzed repolarization characteristics continuously from 22 subjects during normoglycemic period, transition period (blood glucose concentration decreasing) and hypoglycemic period from nine healthy controls (Healthy), six otherwise healthy type 1 diabetics (T1DM) and seven type 1 diabetics with disease complications (T1DMc). An advanced principal component regression (PCR)-based method was used for estimating ECG parameters beat-by-beat, and thus, continuous comparison between the repolarization characteristics and blood glucose values was made. We observed that hypoglycemia related ECG changes in the T1DMc group were smaller than changes in the Healthy and T1DM groups. We also noticed that when glucose concentration remained at a low level, the heart rate corrected QT interval prolonged progressively. Finally, a few minutes time lag was observed between the start of hypoglycemia and cardiac repolarization changes. One explanation for these observations could be that hypoglycemia related hormonal changes have a significant role behind the repolarization changes. This could explain at least the observed time lag (hormonal changes are slow) and the lower repolarization changes in the T1DMc group (hormonal secretion lowered in long duration diabetics).
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Affiliation(s)
- J A Lipponen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
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